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Effective Big Data Analytics Use Cases in 20+ Industries

If we have to talk about the modern technologies and industry disruptions that can benefit every industry and every business organization, then Big Data Analytics fits the bill perfectly.

The big data analytics market is slated to hit 103 bn USD by 2023 and 70% of the large enterprise business setups are using big data.

Organizations continue to generate heaps of data every year, and the global amount of data created, stored, and consumed by 2025 is slated to surpass 180 zettabytes.

However, they are unable to put this huge amount of data to the right use because they are clueless about putting their big data to work.

Here, we are discussing the top big data analytics use cases for a wide range of industries. So, take a thorough read and get started with your big data journey.

Let us begin with understanding the term Big Data Analytics.

What is Big Data Analytics?

Big data analytics is the process of using advanced analytical techniques against extremely large and diverse data sets, with huge blocks of unstructured or semi-structured, or structured data. It is a complex process where the data is processed and parsed to discover hidden patterns, market trends, and correlations and draw actionable insights from them.

The following image shows some benefits of big data analytics:

Big data analytics enables business organizations to make sense of the data they are accumulating and leverage the insights drawn from it for various business activities.

The following visual shows some of the direct benefits of using big data analytics:

Before we move on to discuss the use cases of big data analytics, it is important to address one more thing – What makes big data analytics so versatile?

Core Strengths of Big Data Analytics

Big data analytics is a combination of multiple advanced technologies that work together to help business organizations use the best set of technologies to get the best value out of their data.

Some of these technologies are machine learning, data mining, data management, Hadoop, etc.

Below, we discuss the core strengths of big data.

1. Cost Reduction

Big data analytics offers data-driven insights for the business stakeholders and they can take better strategic decisions, streamline and optimize the operational processes and understand their customers better. All this helps in cost-cutting and adds efficiency to the business model.

Big data analytics also streamline the supply chains to reduce time, effort, and resource consumption.

Studies also reveal that big data analytics solutions can help companies reduce the cost of failure by 35% via:

Real-time monitoring
Real-time visualization
In-memory Analytics
Product Monitoring
Effective Fleet Management

2. Reliable and Continuous Data

As big data analytics allows business enterprises to make use of organizational data, they don’t have to rely upon third-party market research or tools for the same. Further, as the organizational data expands continually, having a reliable and robust big data analytics platform ensures reliable and continuous data streams.

3. New Products and Services

Because of the availability of a set of diverse and advanced technologies in the form of big data analytics, you can take better decisions related to developing new products and services.

Also, you always have the best market and customer or end-user insights to steer the development processes in the right direction.

Hence, big data analytics also facilitates faster decision-making stemming from data-driven actionable insights.

4. Improved Efficiency

Big data analytics improves accuracy, efficiency, and overall decision-making in business organizations. You can analyze the customer behavior via the shopping data and leverage the power of predictive analytics to make certain calculations, such as checkout wait times, etc. Stats reveal that 38% of companies use big data for organizational efficiency.

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5. Better Monitoring and Tracking

Big data analytics also empowers organizations with real-time monitoring and tracking functionalities and amplifies the results by suggesting the appropriate actions or strategizing nudges stemming from predictive data analytics.

These tracking and monitoring capabilities are of extreme importance in:

Security posture management
Mitigating cybersecurity attacks and minimizing the damage
Database backup
IT infrastructure management

6. Better Remote Resource Management

Be it hiring or remote team management and monitoring, big data analytics offers a wide range of capabilities to enterprises. Big data analytics can empower business owners with core insights to make better decisions regarding employee tracking, employee hiring, performance management, etc.

This remote resource management capability works well for IT infrastructure management as well.

7. Taking Right Organizational Decisions

Take a look at the following visual that shows how big data analytics can help companies take better and data-driven organizational decisions.

Decision Making Based on Big Data Analytics

Now, we discuss the top big data analytics use cases in various industries.

Big Data Analytics Use Cases in Various Industries

1. banking and finance (fraud detection, risk & insurance, and asset management).

Futuristic banks and financial institutions are capitalizing on big data in various ways, ranging from capturing new markets and market opportunities to fraud reduction and investment risk management. These organizations are able to leverage big data analytics as a powerful solution to gain a competitive advantage as well.

Take a look at the following image that shows various use cases of big data analytics in the finance and banking sector:

Big Data Analytics in Finance and Banking

Recent studies suggest that big data analytics is going to register a CAGR of 22.97% over the period of 2021 to 2026. As the amount of data generated and government regulations increase, they are fueling the demand for big data analytics in the sector.

2. Accounting

Data is Accounting’s heart and using big data analytics in accounting will certainly deliver more value to the accounting businesses. The accounting sector has various activities, such as different types of audits, checking and maintaining ledger, transaction management, taxation, financial planning, etc.

The auditors have to deal with numerous sorts of data that might be structured or unstructured, and big data analytics can help them in:

Outliers identification
Exclude exceptions
Focus on data blocks of greatest risk areas
Visualize data
Connect financial and non-financial data
Compare predicted outcomes for improving forecasting etc

Using big data analytics will also improve regulatory efficiency, and minimize the redundancy in accounting.

3. Aviation

Studies reveal that the aviation analytics market will hit the 3bn USD by 2025 and will register a CAGR of 11.5% over the forecast period.

The major growth drivers of the aviation market are:

Increasing demand for optimized business operations
COVID-19 outbreak affecting the normal aviation operations
Mergers, acquisitions, and joint ventures

Recent trends and changes in the Original Equipment Manufacturer (OEM) and user segment of the aviation industry One of the most bankable big data analytics opportunities in the aviation industry is cloud-based real-time data collection and analytics, which requires diverse data models.

Likewise, big data analytics has a huge potential in the airlines’ industry as well, improving basic operations, such as maintenance, distribution of resources, flight safety, flight services, to business goals, such as loyalty programs and route optimization.

The following image shows the various points of data generation in the aviation industry (flights only), that can be a valid use case for big data analytics:

4. Agriculture

UN estimates reveal that the world population will hit the 9.8 billion mark by 2050 and to fulfill the food demands of such a large population, agriculture needs modification. However, the climate changes have not only rendered the majority of farmlands unfit for farming, but have also impacted the rainfall patterns, and dried a number of water sources.

This means that apart from increasing crop production, farmers have to improve the other farming-related activities.

Big data analytics can help agriculture and agribusiness stakeholders in the following ways:

Precision farming techniques stemming from advanced technologies, such as big data, IoT , analytics, etc.
Offer advance warnings and climate change predictions
Ethical and wise use of pesticides
Farm equipment optimization
Supply chain optimization and streamlining

Some of the ideal case studies in this regard are:

IBM food trust

5. Automotive

Be it research and development, or marketing planning, big data analytics has a huge scope in the automotive industry that is a combination of a number of individual industries. Being a core infrastructure segment empowering a number of crucial public and private ecosystems, the automobile sector generates huge loads of data every single day!

Hence, it is one of the most critical use cases for big data analytics.

Some common applications are:

Improve the design and manufacturing process via a definitive cost analysis of various designs and concepts.
Vehicle use and maintenance constraints
Tracking and monitoring the manufacturing processes to ensure Zero fault in production
Predicting market trends for sales, manufacturing, and technologies used by the automotive companies
Supply chain and logistics analysis
Streamlining the manufacturing to stay ahead of market competition
Excellent quality analytics to create extremely user-friendly and high-performing vehicles

Take a look at the following visual to have an overall idea of the big analytics use cases in the value chain of the automotive industry:

Big Data Use Cases along the Value Chain of Automotive

6. Biomedical Research and Healthcare (Cancer, Genomic medicine, COVID-19 Management)

Recent stats reveal that the big data analytics market in healthcare will be around 67.82 bn USD by 2025. Healthcare is a huge industry generating mountains of data that is extremely crucial for the patients, medical institutions, insurance companies, government, and research as well.

With proper analysis of huge data blocks, big data analytics can not only help medical researchers to devise more targeted and successful treatment plans but also procure medical supplies from all over the world.

Organ donation, betterment of treatment facilities, development of better medicines, and prediction of pandemic or epidemic outbreaks to contain their ferocity – there are multiple ways big data analytics can benefit the healthcare industry.

Take a look at the following image for a better understanding:

Big Data Analytics and Artificial Intelligence in Healthcare

Also, big data analytics is playing a huge role in COVID-19 management by predicting the outbreaks, red zones, and facilitating crucial data for the frontline workers.

Finally, when we talk about Biomedical research, big data analytics emerges as a powerful tool for:

Data sourcing, processing, and reporting
Predicting trends, and offering hidden patterns from historic data blocks
Genome research and individual genetic data processing for personalized medicine development

The biomedical research and healthcare industry is a huge use case for big data analytics and the applications can themselves form a topic of lengthy discussion.

Various applications of big data analytics in biomedical informatics:

Biomedical Informatics and Data Science Process Cycle

7. Business and Management

95% of businesses cite unstructured data management as a major problem and 97.2% of business organizations are investing in AI and big data to streamline operations, implement digitization and introduce automation, among other business objectives.

However, the business organizations suffer from multiple data pain points, such as:

Unstructured data
Fragmented data
Database incompatibility
Unstructured data storage and management
Data loss due to cyber crimes

Big data analytics can thus be a knight in shining armor for business process streamlining and management with its massive capability set.

Business owners can take more targeted, data-driven, and smart decisions based on the data insights provided by big data analytics, and do much more, as ideated in the following visual:

8. Cloud Computing

45% of businesses across the globe are running at least one big data workload on the cloud, and public cloud services will drive 90% of innovation in analytics and data.

Cloud computing has many challenges, and security is one of them. In fact, security is becoming a major concern for business organizations across the world as well. ‘

Also, big data analytics has rigorous network, data, and server requirements that persuade business organizations across the globe to outsource the hassle and operational overloads to third parties. It is spurring a number of new opportunities that support big data analytics and help organizations overcome architectural hurdles.

9. Cybersecurity

In cybersecurity, big data security analytics is an emerging trend and helps business organizations to improve security via:

Identify outliers and anomalies in security data to detect malicious or suspicious activities
Workflow automation for responding to threats, such as disrupting obvious malware attacks

53% of the companies that are already using big data security analytics say that they experienced high benefits from big data analytics.

10. Government and Law Enforcement

Government and public infrastructure produce a large amount of data in various forms, such as body cameras, CCTV footage, satellites, public schemes, registrations, certifications, social media, etc.

Big data analytics can empower the government and public services sector in many ways, some of which are mentioned below:

Open data initiatives to manage, monitor, and track the private company data
Encouraging public participation and transparency in open data initiatives by the government
Predicting consumer frauds, political shifts, and tracking the border security
Defense and consumer protection
Public safety via a rapid and efficient address of public grievances
Transportation and city infrastructure management
Public health management
Efficient and data-driven management of energy, environment, and public utilities

Also, big data analytics are of extreme importance in the law enforcement segment as well. Tracking crimes, real-time and 24X7 policing of sensitive areas, real-time monitoring and tracking of criminals, smugglers, and tracing money launderers – there are various ways big data analytics can help law enforcement stakeholders.

The following visual shows how big data analytics can help the law enforcement and national security sectors:

Big Data Analytics in Law Enforcement and National Security Sectors

11. Oil, Gas & Renewable Energy

From offering new ways to innovate for various sectors to using data sensors for tracking and monitoring new preserves, big data analytics offers many use cases in the energy industry.

Some common application areas include:

Tracking and monitoring of oil well and equipment performance
Monitor well activity
Predictive equipment maintenance in remote and deep-water locations
Oil exploration and optimizing drilling sites
Optimization of oil production via unstructured sensor and historical data

Some other potential areas where data analytics is of extreme importance are the safety of oil sites, supply pipes, and saving time via automation.

Improvement of fuel transportation, supply chain, and logistics are some other areas where big data analytics can be of help.

Further, in the renewable energy sector, the technology can offer actionable insights such as geographical data insights for installing renewable energy plants, deforestation maps, efficiency, and cost-benefit analysis of various methods of energy production, as shown below:

12. Manufacturing & Supply Chain Management

When the world is on the verge of the fourth industrial revolution, the manufacturing sector and supply chains are subject to an intense revolution in many ways. The manufacturers are looking for ways to harness massive data they generate in order to streamline the business processes, dig hidden patterns and market trends from huge data blocks to drive profits, and boost their business equities.

There are three core segments in the manufacturing industry that form crucial application areas of big data analytics:

Predictive Maintenance – Predict equipment failure, discover potential issues in the manufacturing units as well as products, etc.
Operational Efficiency – Analysis and assessment of production processes, proactive customer feedback, future demand forecasts, etc.
Production Optimization – Optimizing the production lines to decrease the costs and increase business revenue, and identify the processes or activities causing delays in production.

Big data analytics can help businesses revolutionize the supply chains in various ways, such as:

How Big Data Analytics is Benefiting Supply Chain Businesses

13. Retail

The modern retail landscape is alight with fierce competition and is becoming increasingly volatile with industry disruptions and the break-neck pace of technological advancements. Businesses are focusing on many granular aspects of customers and business offerings, irrespective of them being product-based vendors or service-based vendors.

Some of the big data analysis use cases in retail are:

Product Development – Predictive business models, market research for developing products that are high in demand, and get deep insights from huge consumer and market data from multiple platforms.
Customer Experience and Service – Providing personalized and hyper-personalized services and customer experiences throughout the customer journeys and addressing crucial events, such as customer complaints, customer churn, etc.

Customer Lifetime Value – Rich actionable insights on customer behavior, purchase patterns , and motivation to offer a highly personalized lifetime plan to all the customers.

14. Stock Market

Another crucial industry that walks in parallel with retail, and drives the economy is the Stock Market. And, big data analytics can be a game-changer here as well.

Experts say that big data analytics has changed finance and stock market trading by:

Offering smart automated investment and trading modules
Smart modules for funds planning and management of stocks based on real-time market insights
Using predictive insights for gaining more by trading well ahead of time
Estimation of outcomes and returns for investments of all sizes and all types.

15. Telecom

The telecom industry is in for a huge wave of digital transformation and revolution by advanced technologies and data analytics. As the number of smartphone users increases and technologies like 5G is all set to penetrate the developing countries as well, big data analytics emerges as a credible tool to tackle multiple issues.

Some applications are shown in the following image:

Some use cases for big data in the telecom industry are:

Optimizing Network Capacity – Analysis of network usage for deciding rerouting bandwidth, managing the network limitation, and decoding infrastructure investments with data-driven insights from multiple areas.
Telecom Customer Churn – With multiple options available in the market, the business operators are always at a risk of losing customers to their competitors.
With insights collected from data about customer satisfaction, market research, and service quality, the brands can address the issue with much clarity.
New Product Offerings – With predictive analytics and thorough market research, the telecom companies can come up with new product offerings that are unique, address the customer pain points, and cater to usability concerns, instead of generic brand offerings.

16. Media and Entertainment

In the Media and Entertainment industry, big data analytics can offer insights about the various content preferences, reception, and cost/subscription ideas to the brands.

Further, analysis of customer behavior and content consumption can be used to offer more personalized content recommendations and get insights for creating new shows. Market potential, market segmentation, and insights about customer sentiments can also help drive core business decisions to increase revenue and decrease the odds of creating flop or lopsided content.

17. Education

Market forecasts suggest that the big data analytics market in education will stand at 57.14 bn USD by 2030. Despite being extremely useful in various segments of the industry, the technology valuation differs greatly from the industries mentioned above.

There are many reasons for the same, such as regional education policies, lack of digitization , and technological advancements in the sector.

Some core areas of application are shown in the following visual:

Core Areas of Big Data Analytics Application in Education Industry

18. Pharmacy

In the Pharmacy sector, big data analytics is of extreme importance in the following areas:

Standardization of images, numerical, data processing methods
Gaining insights from hoards of analytical and medical data that is still siloed in the research files
Clinical monitoring
Personalized drug development and digitized data analysis
Operations management in institutes and manufacturing units
Addressing the failure of traditional data processing methods
Taking model-based decisions

19. Psychology

If you are unable to grasp the relationship between psychology and data analytics, take a look at the graphical relationship diagram below:

Big data analytics has a big role in psychology, such and in its multiple branches, such as organizational psychology to understand employee motivation and satisfaction in a better manner, etc, and safety psychology to make counseling and medical consultation better.

Further, when it comes to therapeutic counseling, big data analytics can help the practitioners by offering behavioral models of a patient and their tendencies, and develop personalized therapy programs or diagnosing severe psychological disorders for criminal cases, etc.

20. Project Management

The global business dissatisfaction with project management techniques is increasing despite innovation in workplace tech. Also, only 78% of the projects meet original goals and only 64% of them are completed on time.

Project management is a huge use case for big data analytics, and some application areas are:

Deriving project feasibility stats from initial work plans and SRS documents
Predicting the success and failure of the development process
Checking the market relevance, budgeting, etc

Some other applications of big data analytics in project management are:

21. Marketing and Sales (Advertising)

Market research is a complex industry with various independent surveys and studies going on simultaneously. Apart from generating a huge amount of data, these studies also generate a huge number of redundancies because of the unstructured nature of data.

Big data analytics can not only make study results better but also help organizations to leverage them better by allowing them to define specific test cases and custom parameters.

Also, when it comes to sales and sales processes, big data analytics is of paramount importance as it surpasses the “ dry ” nature of data.

It can go beyond the statistics to discover the underlying trends, such as behavioral analytics, sentiment analysis, predictive analysis of customer comments in informal or regional language to decode customer satisfaction levels, etc.

The following visual shows how these stats help businesses make important decisions:

Thus, the brands can market more, better, and with proper customer targets in mind.

22. Social Media Management

Another crucial segment of marketing and sales is social media management and monitoring as more and more people are now using social media platforms for shopping, reviewing, and interacting with brands.

However, when it comes to drawing sensible business-relevant insights from the huge amounts of social media data, the majority of brands succumb to feeble data analytics software.

Big data analytics can uncover excellent data insights from the social media channels and platforms to make marketing, customer service, and advertising better and more aligned to business goals.

23. Hospitality, Restaurants, and Tourism

Ranging from an increase in online revenue to a reduction in guest complaints, and increasing customer satisfaction via highly personalized services during the stay – there are multiple use cases for big data analytics in the hospitality and restaurant industries .

Apart from the customer-relevant insights, big data analytics can also offer business insights to the business owners such as:

Location suggestions
Itinerary suggestions
Deals, discounts, and promotional campaigns
Smart advertising
Pricing and family/corporate-specific services
Travelers’ needs

The tourism industry is also an interesting use case, as people are now traveling for many purposes, other than business, leisure, and work, such as medical tourism.

Some of the application areas of big data analytics in the tourism industry are shown in the following visual:

24. Miscellaneous Use Cases

Construction.

Resolving structural issues
Improved collaboration
Reduced construction time, wastage, and carbon emissions
Wearables’ data processing to improve worker safety

Image Processing

Better image data visualization
Satellite image processing
Improved security for confidential images
Interactive digital media
Military imagery protection and image data processing
Image-based modeling and algorithms
Knowledge-based recognition
Virtual and augmented reality
Track maintenance and planning
Service, customer, and travel data
Real-time predictive analysis for minimizing delays owing to weather and sudden incidents
Infrastructure management
Coach maintenance, facility maintenance, and safety of travelers

Big Data Analytics: Laying the Road for Future-Ready Businesses

The future of the business landscape is full of uncertainties and intense competition, and nothing is more reliable and credible than data!

Big data analytics offers powerful data mining, management, and processing capabilities that can help businesses make the most of historical data and continuously generated organizational data.

With abilities to drive business decisions for the present and future, big data analytics is one of the most bankable technologies for businesses of all types and all scales.

While it is easy to say, adopting and implementing big data analytics is a challenging task with serious requirements, in terms of resources and capital. Hence, the best way to take the first step towards embracing the revolution is by opting for reputed big data consulting companies , such as DataToBiz that can help you identify, understand, and cater to your big data analytics needs.

For more information, book an appointment today!

Arya Bharti

Driven by passion and an unrelenting urge to learn, Arya Bharti has a keen interest in evolving and innovative business technology and solutions that empower businesses and people alike. You can connect with Arya on LinkedIn and Facebook.

+91 70099 35623

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Big data case study: How UPS is using analytics to improve performance

A new initiative at UPS will use real-time data, advanced analytics and artificial intelligence to help employees make better decisions.

As chief information and engineering officer for logistics giant UPS, Juan Perez is placing analytics and insight at the heart of business operations.

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"Big data at UPS takes many forms because of all the types of information we collect," he says. "We're excited about the opportunity of using big data to solve practical business problems. We've already had some good experience of using data and analytics and we're very keen to do more."

Perez says UPS is using technology to improve its flexibility, capability, and efficiency, and that the right insight at the right time helps line-of-business managers to improve performance.

The aim for UPS, says Perez, is to use the data it collects to optimise processes, to enable automation and autonomy, and to continue to learn how to improve its global delivery network.

Leading data-fed projects that change the business for the better

Perez says one of his firm's key initiatives, known as Network Planning Tools, will help UPS to optimise its logistics network through the effective use of data. The system will use real-time data, advanced analytics and artificial intelligence to help employees make better decisions. The company expects to begin rolling out the initiative from the first quarter of 2018.

"That will help all our business units to make smart use of our assets and it's just one key project that's being supported in the organisation as part of the smart logistics network," says Perez, who also points to related and continuing developments in Orion (On-road Integrated Optimization and Navigation), which is the firm's fleet management system.

Orion uses telematics and advanced algorithms to create optimal routes for delivery drivers. The IT team is currently working on the third version of the technology, and Perez says this latest update to Orion will provide two key benefits to UPS.

First, the technology will include higher levels of route optimisation which will be sent as navigation advice to delivery drivers. "That will help to boost efficiency," says Perez.

Second, Orion will use big data to optimise delivery routes dynamically.

"Today, Orion creates delivery routes before drivers leave the facility and they stay with that static route throughout the day," he says. "In the future, our system will continually look at the work that's been completed, and that still needs to be completed, and will then dynamically optimise the route as drivers complete their deliveries. That approach will ensure we meet our service commitments and reduce overall delivery miles."

Once Orion is fully operational for more than 55,000 drivers this year, it will lead to a reduction of about 100 million delivery miles -- and 100,000 metric tons of carbon emissions. Perez says these reductions represent a key measure of business efficiency and effectiveness, particularly in terms of sustainability.

Projects such as Orion and Network Planning Tools form part of a collective of initiatives that UPS is using to improve decision making across the package delivery network. The firm, for example, recently launched the third iteration of its chatbot that uses artificial intelligence to help customers find rates and tracking information across a series of platforms, including Facebook and Amazon Echo.

"That project will continue to evolve, as will all our innovations across the smart logistics network," says Perez. "Everything runs well today but we also recognise there are opportunities for continuous improvement."

Overcoming business challenges to make the most of big data

"Big data is all about the business case -- how effective are we as an IT team in defining a good business case, which includes how to improve our service to our customers, what is the return on investment and how will the use of data improve other aspects of the business," says Perez.

These alternative use cases are not always at the forefront of executive thinking. Consultant McKinsey says too many organisations drill down on a single data set in isolation and fail to consider what different data sets mean for other parts of the business.

However, Perez says the re-use of information can have a significant impact at UPS. Perez talks, for example, about using delivery data to help understand what types of distribution solutions work better in different geographical locations.

"Should we have more access points? Should we introduce lockers? Should we allow drivers to release shipments without signatures? Data, technology, and analytics will improve our ability to answer those questions in individual locations -- and those benefits can come from using the information we collect from our customers in a different way," says Perez.

Perez says this fresh, open approach creates new opportunities for other data-savvy CIOs. "The conversation in the past used to be about buying technology, creating a data repository and discovering information," he says. "Now the conversation is changing and it's exciting. Every time we talk about a new project, the start of the conversation includes data."

By way of an example, Perez says senior individuals across the organisation now talk as a matter of course about the potential use of data in their line-of-business and how that application of insight might be related to other models across the organisation.

These senior executive, he says, also ask about the availability of information and whether the existence of data in other parts of the business will allow the firm to avoid a duplication of effort.

"The conversation about data is now much more active," says Perez. "That higher level of collaboration provides benefits for everyone because the awareness across the organisation means we'll have better repositories, less duplication and much more effective data models for new business cases in the future."

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10 Real World Data Science Case Studies Projects with Example

Top 10 Data Science Case Studies Projects with Examples and Solutions in Python to inspire your data science learning in 2023.

BelData science has been a trending buzzword in recent times. With wide applications in various sectors like healthcare , education, retail, transportation, media, and banking -data science applications are at the core of pretty much every industry out there. The possibilities are endless: analysis of frauds in the finance sector or the personalization of recommendations on eCommerce businesses. We have developed ten exciting data science case studies to explain how data science is leveraged across various industries to make smarter decisions and develop innovative personalized products tailored to specific customers.

Walmart Sales Forecasting Data Science Project

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So, without much ado, let's get started with data science business case studies !

With humble beginnings as a simple discount retailer, today, Walmart operates in 10,500 stores and clubs in 24 countries and eCommerce websites, employing around 2.2 million people around the globe. For the fiscal year ended January 31, 2021, Walmart's total revenue was $559 billion showing a growth of $35 billion with the expansion of the eCommerce sector. Walmart is a data-driven company that works on the principle of 'Everyday low cost' for its consumers. To achieve this goal, they heavily depend on the advances of their data science and analytics department for research and development, also known as Walmart Labs. Walmart is home to the world's largest private cloud, which can manage 2.5 petabytes of data every hour! To analyze this humongous amount of data, Walmart has created 'Data Café,' a state-of-the-art analytics hub located within its Bentonville, Arkansas headquarters. The Walmart Labs team heavily invests in building and managing technologies like cloud, data, DevOps , infrastructure, and security.

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Walmart is experiencing massive digital growth as the world's largest retailer . Walmart has been leveraging Big data and advances in data science to build solutions to enhance, optimize and customize the shopping experience and serve their customers in a better way. At Walmart Labs, data scientists are focused on creating data-driven solutions that power the efficiency and effectiveness of complex supply chain management processes. Here are some of the applications of data science at Walmart:

i) Personalized Customer Shopping Experience

Walmart analyses customer preferences and shopping patterns to optimize the stocking and displaying of merchandise in their stores. Analysis of Big data also helps them understand new item sales, make decisions on discontinuing products, and the performance of brands.

ii) Order Sourcing and On-Time Delivery Promise

Millions of customers view items on Walmart.com, and Walmart provides each customer a real-time estimated delivery date for the items purchased. Walmart runs a backend algorithm that estimates this based on the distance between the customer and the fulfillment center, inventory levels, and shipping methods available. The supply chain management system determines the optimum fulfillment center based on distance and inventory levels for every order. It also has to decide on the shipping method to minimize transportation costs while meeting the promised delivery date.

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iii) Packing Optimization

Also known as Box recommendation is a daily occurrence in the shipping of items in retail and eCommerce business. When items of an order or multiple orders for the same customer are ready for packing, Walmart has developed a recommender system that picks the best-sized box which holds all the ordered items with the least in-box space wastage within a fixed amount of time. This Bin Packing problem is a classic NP-Hard problem familiar to data scientists .

Whenever items of an order or multiple orders placed by the same customer are picked from the shelf and are ready for packing, the box recommendation system determines the best-sized box to hold all the ordered items with a minimum of in-box space wasted. This problem is known as the Bin Packing Problem, another classic NP-Hard problem familiar to data scientists.

Here is a link to a sales prediction data science case study to help you understand the applications of Data Science in the real world. Walmart Sales Forecasting Project uses historical sales data for 45 Walmart stores located in different regions. Each store contains many departments, and you must build a model to project the sales for each department in each store. This data science case study aims to create a predictive model to predict the sales of each product. You can also try your hands-on Inventory Demand Forecasting Data Science Project to develop a machine learning model to forecast inventory demand accurately based on historical sales data.

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Amazon is an American multinational technology-based company based in Seattle, USA. It started as an online bookseller, but today it focuses on eCommerce, cloud computing , digital streaming, and artificial intelligence . It hosts an estimate of 1,000,000,000 gigabytes of data across more than 1,400,000 servers. Through its constant innovation in data science and big data Amazon is always ahead in understanding its customers. Here are a few data analytics case study examples at Amazon:

i) Recommendation Systems

Data science models help amazon understand the customers' needs and recommend them to them before the customer searches for a product; this model uses collaborative filtering. Amazon uses 152 million customer purchases data to help users to decide on products to be purchased. The company generates 35% of its annual sales using the Recommendation based systems (RBS) method.

Here is a Recommender System Project to help you build a recommendation system using collaborative filtering.

ii) Retail Price Optimization

Amazon product prices are optimized based on a predictive model that determines the best price so that the users do not refuse to buy it based on price. The model carefully determines the optimal prices considering the customers' likelihood of purchasing the product and thinks the price will affect the customers' future buying patterns. Price for a product is determined according to your activity on the website, competitors' pricing, product availability, item preferences, order history, expected profit margin, and other factors.

Check Out this Retail Price Optimization Project to build a Dynamic Pricing Model.

iii) Fraud Detection

Being a significant eCommerce business, Amazon remains at high risk of retail fraud. As a preemptive measure, the company collects historical and real-time data for every order. It uses Machine learning algorithms to find transactions with a higher probability of being fraudulent. This proactive measure has helped the company restrict clients with an excessive number of returns of products.

You can look at this Credit Card Fraud Detection Project to implement a fraud detection model to classify fraudulent credit card transactions.

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Let us explore data analytics case study examples in the entertainment indusry.

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Netflix started as a DVD rental service in 1997 and then has expanded into the streaming business. Headquartered in Los Gatos, California, Netflix is the largest content streaming company in the world. Currently, Netflix has over 208 million paid subscribers worldwide, and with thousands of smart devices which are presently streaming supported, Netflix has around 3 billion hours watched every month. The secret to this massive growth and popularity of Netflix is its advanced use of data analytics and recommendation systems to provide personalized and relevant content recommendations to its users. The data is collected over 100 billion events every day. Here are a few examples of data analysis case studies applied at Netflix :

i) Personalized Recommendation System

Netflix uses over 1300 recommendation clusters based on consumer viewing preferences to provide a personalized experience. Some of the data that Netflix collects from its users include Viewing time, platform searches for keywords, Metadata related to content abandonment, such as content pause time, rewind, rewatched. Using this data, Netflix can predict what a viewer is likely to watch and give a personalized watchlist to a user. Some of the algorithms used by the Netflix recommendation system are Personalized video Ranking, Trending now ranker, and the Continue watching now ranker.

ii) Content Development using Data Analytics

Netflix uses data science to analyze the behavior and patterns of its user to recognize themes and categories that the masses prefer to watch. This data is used to produce shows like The umbrella academy, and Orange Is the New Black, and the Queen's Gambit. These shows seem like a huge risk but are significantly based on data analytics using parameters, which assured Netflix that they would succeed with its audience. Data analytics is helping Netflix come up with content that their viewers want to watch even before they know they want to watch it.

iii) Marketing Analytics for Campaigns

Netflix uses data analytics to find the right time to launch shows and ad campaigns to have maximum impact on the target audience. Marketing analytics helps come up with different trailers and thumbnails for other groups of viewers. For example, the House of Cards Season 5 trailer with a giant American flag was launched during the American presidential elections, as it would resonate well with the audience.

Here is a Customer Segmentation Project using association rule mining to understand the primary grouping of customers based on various parameters.

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In a world where Purchasing music is a thing of the past and streaming music is a current trend, Spotify has emerged as one of the most popular streaming platforms. With 320 million monthly users, around 4 billion playlists, and approximately 2 million podcasts, Spotify leads the pack among well-known streaming platforms like Apple Music, Wynk, Songza, amazon music, etc. The success of Spotify has mainly depended on data analytics. By analyzing massive volumes of listener data, Spotify provides real-time and personalized services to its listeners. Most of Spotify's revenue comes from paid premium subscriptions. Here are some of the examples of case study on data analytics used by Spotify to provide enhanced services to its listeners:

i) Personalization of Content using Recommendation Systems

Spotify uses Bart or Bayesian Additive Regression Trees to generate music recommendations to its listeners in real-time. Bart ignores any song a user listens to for less than 30 seconds. The model is retrained every day to provide updated recommendations. A new Patent granted to Spotify for an AI application is used to identify a user's musical tastes based on audio signals, gender, age, accent to make better music recommendations.

Spotify creates daily playlists for its listeners, based on the taste profiles called 'Daily Mixes,' which have songs the user has added to their playlists or created by the artists that the user has included in their playlists. It also includes new artists and songs that the user might be unfamiliar with but might improve the playlist. Similar to it is the weekly 'Release Radar' playlists that have newly released artists' songs that the listener follows or has liked before.

ii) Targetted marketing through Customer Segmentation

With user data for enhancing personalized song recommendations, Spotify uses this massive dataset for targeted ad campaigns and personalized service recommendations for its users. Spotify uses ML models to analyze the listener's behavior and group them based on music preferences, age, gender, ethnicity, etc. These insights help them create ad campaigns for a specific target audience. One of their well-known ad campaigns was the meme-inspired ads for potential target customers, which was a huge success globally.

iii) CNN's for Classification of Songs and Audio Tracks

Spotify builds audio models to evaluate the songs and tracks, which helps develop better playlists and recommendations for its users. These allow Spotify to filter new tracks based on their lyrics and rhythms and recommend them to users like similar tracks ( collaborative filtering). Spotify also uses NLP ( Natural language processing) to scan articles and blogs to analyze the words used to describe songs and artists. These analytical insights can help group and identify similar artists and songs and leverage them to build playlists.

Here is a Music Recommender System Project for you to start learning. We have listed another music recommendations dataset for you to use for your projects: Dataset1 . You can use this dataset of Spotify metadata to classify songs based on artists, mood, liveliness. Plot histograms, heatmaps to get a better understanding of the dataset. Use classification algorithms like logistic regression, SVM, and Principal component analysis to generate valuable insights from the dataset.

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Below you will find case studies for data analytics in the travel and tourism industry.

Airbnb was born in 2007 in San Francisco and has since grown to 4 million Hosts and 5.6 million listings worldwide who have welcomed more than 1 billion guest arrivals in almost every country across the globe. Airbnb is active in every country on the planet except for Iran, Sudan, Syria, and North Korea. That is around 97.95% of the world. Using data as a voice of their customers, Airbnb uses the large volume of customer reviews, host inputs to understand trends across communities, rate user experiences, and uses these analytics to make informed decisions to build a better business model. The data scientists at Airbnb are developing exciting new solutions to boost the business and find the best mapping for its customers and hosts. Airbnb data servers serve approximately 10 million requests a day and process around one million search queries. Data is the voice of customers at AirBnB and offers personalized services by creating a perfect match between the guests and hosts for a supreme customer experience.

i) Recommendation Systems and Search Ranking Algorithms

Airbnb helps people find 'local experiences' in a place with the help of search algorithms that make searches and listings precise. Airbnb uses a 'listing quality score' to find homes based on the proximity to the searched location and uses previous guest reviews. Airbnb uses deep neural networks to build models that take the guest's earlier stays into account and area information to find a perfect match. The search algorithms are optimized based on guest and host preferences, rankings, pricing, and availability to understand users’ needs and provide the best match possible.

ii) Natural Language Processing for Review Analysis

Airbnb characterizes data as the voice of its customers. The customer and host reviews give a direct insight into the experience. The star ratings alone cannot be an excellent way to understand it quantitatively. Hence Airbnb uses natural language processing to understand reviews and the sentiments behind them. The NLP models are developed using Convolutional neural networks .

Practice this Sentiment Analysis Project for analyzing product reviews to understand the basic concepts of natural language processing.

iii) Smart Pricing using Predictive Analytics

The Airbnb hosts community uses the service as a supplementary income. The vacation homes and guest houses rented to customers provide for rising local community earnings as Airbnb guests stay 2.4 times longer and spend approximately 2.3 times the money compared to a hotel guest. The profits are a significant positive impact on the local neighborhood community. Airbnb uses predictive analytics to predict the prices of the listings and help the hosts set a competitive and optimal price. The overall profitability of the Airbnb host depends on factors like the time invested by the host and responsiveness to changing demands for different seasons. The factors that impact the real-time smart pricing are the location of the listing, proximity to transport options, season, and amenities available in the neighborhood of the listing.

Here is a Price Prediction Project to help you understand the concept of predictive analysis which is widely common in case studies for data analytics.

Uber is the biggest global taxi service provider. As of December 2018, Uber has 91 million monthly active consumers and 3.8 million drivers. Uber completes 14 million trips each day. Uber uses data analytics and big data-driven technologies to optimize their business processes and provide enhanced customer service. The Data Science team at uber has been exploring futuristic technologies to provide better service constantly. Machine learning and data analytics help Uber make data-driven decisions that enable benefits like ride-sharing, dynamic price surges, better customer support, and demand forecasting. Here are some of the real world data science projects used by uber:

i) Dynamic Pricing for Price Surges and Demand Forecasting

Uber prices change at peak hours based on demand. Uber uses surge pricing to encourage more cab drivers to sign up with the company, to meet the demand from the passengers. When the prices increase, the driver and the passenger are both informed about the surge in price. Uber uses a predictive model for price surging called the 'Geosurge' ( patented). It is based on the demand for the ride and the location.

ii) One-Click Chat

Uber has developed a Machine learning and natural language processing solution called one-click chat or OCC for coordination between drivers and users. This feature anticipates responses for commonly asked questions, making it easy for the drivers to respond to customer messages. Drivers can reply with the clock of just one button. One-Click chat is developed on Uber's machine learning platform Michelangelo to perform NLP on rider chat messages and generate appropriate responses to them.

iii) Customer Retention

Failure to meet the customer demand for cabs could lead to users opting for other services. Uber uses machine learning models to bridge this demand-supply gap. By using prediction models to predict the demand in any location, uber retains its customers. Uber also uses a tier-based reward system, which segments customers into different levels based on usage. The higher level the user achieves, the better are the perks. Uber also provides personalized destination suggestions based on the history of the user and their frequently traveled destinations.

You can take a look at this Python Chatbot Project and build a simple chatbot application to understand better the techniques used for natural language processing. You can also practice the working of a demand forecasting model with this project using time series analysis. You can look at this project which uses time series forecasting and clustering on a dataset containing geospatial data for forecasting customer demand for ola rides.

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7) LinkedIn

LinkedIn is the largest professional social networking site with nearly 800 million members in more than 200 countries worldwide. Almost 40% of the users access LinkedIn daily, clocking around 1 billion interactions per month. The data science team at LinkedIn works with this massive pool of data to generate insights to build strategies, apply algorithms and statistical inferences to optimize engineering solutions, and help the company achieve its goals. Here are some of the real world data science projects at LinkedIn:

i) LinkedIn Recruiter Implement Search Algorithms and Recommendation Systems

LinkedIn Recruiter helps recruiters build and manage a talent pool to optimize the chances of hiring candidates successfully. This sophisticated product works on search and recommendation engines. The LinkedIn recruiter handles complex queries and filters on a constantly growing large dataset. The results delivered have to be relevant and specific. The initial search model was based on linear regression but was eventually upgraded to Gradient Boosted decision trees to include non-linear correlations in the dataset. In addition to these models, the LinkedIn recruiter also uses the Generalized Linear Mix model to improve the results of prediction problems to give personalized results.

ii) Recommendation Systems Personalized for News Feed

The LinkedIn news feed is the heart and soul of the professional community. A member's newsfeed is a place to discover conversations among connections, career news, posts, suggestions, photos, and videos. Every time a member visits LinkedIn, machine learning algorithms identify the best exchanges to be displayed on the feed by sorting through posts and ranking the most relevant results on top. The algorithms help LinkedIn understand member preferences and help provide personalized news feeds. The algorithms used include logistic regression, gradient boosted decision trees and neural networks for recommendation systems.

iii) CNN's to Detect Inappropriate Content

To provide a professional space where people can trust and express themselves professionally in a safe community has been a critical goal at LinkedIn. LinkedIn has heavily invested in building solutions to detect fake accounts and abusive behavior on their platform. Any form of spam, harassment, inappropriate content is immediately flagged and taken down. These can range from profanity to advertisements for illegal services. LinkedIn uses a Convolutional neural networks based machine learning model. This classifier trains on a training dataset containing accounts labeled as either "inappropriate" or "appropriate." The inappropriate list consists of accounts having content from "blocklisted" phrases or words and a small portion of manually reviewed accounts reported by the user community.

Here is a Text Classification Project to help you understand NLP basics for text classification. You can find a news recommendation system dataset to help you build a personalized news recommender system. You can also use this dataset to build a classifier using logistic regression, Naive Bayes, or Neural networks to classify toxic comments.

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Pfizer is a multinational pharmaceutical company headquartered in New York, USA. One of the largest pharmaceutical companies globally known for developing a wide range of medicines and vaccines in disciplines like immunology, oncology, cardiology, and neurology. Pfizer became a household name in 2010 when it was the first to have a COVID-19 vaccine with FDA. In early November 2021, The CDC has approved the Pfizer vaccine for kids aged 5 to 11. Pfizer has been using machine learning and artificial intelligence to develop drugs and streamline trials, which played a massive role in developing and deploying the COVID-19 vaccine. Here are a few data analytics case studies by Pfizer :

i) Identifying Patients for Clinical Trials

Artificial intelligence and machine learning are used to streamline and optimize clinical trials to increase their efficiency. Natural language processing and exploratory data analysis of patient records can help identify suitable patients for clinical trials. These can help identify patients with distinct symptoms. These can help examine interactions of potential trial members' specific biomarkers, predict drug interactions and side effects which can help avoid complications. Pfizer's AI implementation helped rapidly identify signals within the noise of millions of data points across their 44,000-candidate COVID-19 clinical trial.

ii) Supply Chain and Manufacturing

Data science and machine learning techniques help pharmaceutical companies better forecast demand for vaccines and drugs and distribute them efficiently. Machine learning models can help identify efficient supply systems by automating and optimizing the production steps. These will help supply drugs customized to small pools of patients in specific gene pools. Pfizer uses Machine learning to predict the maintenance cost of equipment used. Predictive maintenance using AI is the next big step for Pharmaceutical companies to reduce costs.

iii) Drug Development

Computer simulations of proteins, and tests of their interactions, and yield analysis help researchers develop and test drugs more efficiently. In 2016 Watson Health and Pfizer announced a collaboration to utilize IBM Watson for Drug Discovery to help accelerate Pfizer's research in immuno-oncology, an approach to cancer treatment that uses the body's immune system to help fight cancer. Deep learning models have been used recently for bioactivity and synthesis prediction for drugs and vaccines in addition to molecular design. Deep learning has been a revolutionary technique for drug discovery as it factors everything from new applications of medications to possible toxic reactions which can save millions in drug trials.

You can create a Machine learning model to predict molecular activity to help design medicine using this dataset . You may build a CNN or a Deep neural network for this data analyst case study project.

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9) Shell Data Analyst Case Study Project

Shell is a global group of energy and petrochemical companies with over 80,000 employees in around 70 countries. Shell uses advanced technologies and innovations to help build a sustainable energy future. Shell is going through a significant transition as the world needs more and cleaner energy solutions to be a clean energy company by 2050. It requires substantial changes in the way in which energy is used. Digital technologies, including AI and Machine Learning, play an essential role in this transformation. These include efficient exploration and energy production, more reliable manufacturing, more nimble trading, and a personalized customer experience. Using AI in various phases of the organization will help achieve this goal and stay competitive in the market. Here are a few data analytics case studies in the petrochemical industry:

i) Precision Drilling

Shell is involved in the processing mining oil and gas supply, ranging from mining hydrocarbons to refining the fuel to retailing them to customers. Recently Shell has included reinforcement learning to control the drilling equipment used in mining. Reinforcement learning works on a reward-based system based on the outcome of the AI model. The algorithm is designed to guide the drills as they move through the surface, based on the historical data from drilling records. It includes information such as the size of drill bits, temperatures, pressures, and knowledge of the seismic activity. This model helps the human operator understand the environment better, leading to better and faster results will minor damage to machinery used.

ii) Efficient Charging Terminals

Due to climate changes, governments have encouraged people to switch to electric vehicles to reduce carbon dioxide emissions. However, the lack of public charging terminals has deterred people from switching to electric cars. Shell uses AI to monitor and predict the demand for terminals to provide efficient supply. Multiple vehicles charging from a single terminal may create a considerable grid load, and predictions on demand can help make this process more efficient.

iii) Monitoring Service and Charging Stations

Another Shell initiative trialed in Thailand and Singapore is the use of computer vision cameras, which can think and understand to watch out for potentially hazardous activities like lighting cigarettes in the vicinity of the pumps while refueling. The model is built to process the content of the captured images and label and classify it. The algorithm can then alert the staff and hence reduce the risk of fires. You can further train the model to detect rash driving or thefts in the future.

Here is a project to help you understand multiclass image classification. You can use the Hourly Energy Consumption Dataset to build an energy consumption prediction model. You can use time series with XGBoost to develop your model.

10) Zomato Case Study on Data Analytics

Zomato was founded in 2010 and is currently one of the most well-known food tech companies. Zomato offers services like restaurant discovery, home delivery, online table reservation, online payments for dining, etc. Zomato partners with restaurants to provide tools to acquire more customers while also providing delivery services and easy procurement of ingredients and kitchen supplies. Currently, Zomato has over 2 lakh restaurant partners and around 1 lakh delivery partners. Zomato has closed over ten crore delivery orders as of date. Zomato uses ML and AI to boost their business growth, with the massive amount of data collected over the years from food orders and user consumption patterns. Here are a few examples of data analyst case study project developed by the data scientists at Zomato:

i) Personalized Recommendation System for Homepage

Zomato uses data analytics to create personalized homepages for its users. Zomato uses data science to provide order personalization, like giving recommendations to the customers for specific cuisines, locations, prices, brands, etc. Restaurant recommendations are made based on a customer's past purchases, browsing history, and what other similar customers in the vicinity are ordering. This personalized recommendation system has led to a 15% improvement in order conversions and click-through rates for Zomato.

You can use the Restaurant Recommendation Dataset to build a restaurant recommendation system to predict what restaurants customers are most likely to order from, given the customer location, restaurant information, and customer order history.

ii) Analyzing Customer Sentiment

Zomato uses Natural language processing and Machine learning to understand customer sentiments using social media posts and customer reviews. These help the company gauge the inclination of its customer base towards the brand. Deep learning models analyze the sentiments of various brand mentions on social networking sites like Twitter, Instagram, Linked In, and Facebook. These analytics give insights to the company, which helps build the brand and understand the target audience.

iii) Predicting Food Preparation Time (FPT)

Food delivery time is an essential variable in the estimated delivery time of the order placed by the customer using Zomato. The food preparation time depends on numerous factors like the number of dishes ordered, time of the day, footfall in the restaurant, day of the week, etc. Accurate prediction of the food preparation time can help make a better prediction of the Estimated delivery time, which will help delivery partners less likely to breach it. Zomato uses a Bidirectional LSTM-based deep learning model that considers all these features and provides food preparation time for each order in real-time.

Data scientists are companies' secret weapons when analyzing customer sentiments and behavior and leveraging it to drive conversion, loyalty, and profits. These 10 data science case studies projects with examples and solutions show you how various organizations use data science technologies to succeed and be at the top of their field! To summarize, Data Science has not only accelerated the performance of companies but has also made it possible to manage & sustain their performance with ease.

FAQs on Data Analysis Case Studies

A case study in data science is an in-depth analysis of a real-world problem using data-driven approaches. It involves collecting, cleaning, and analyzing data to extract insights and solve challenges, offering practical insights into how data science techniques can address complex issues across various industries.

To create a data science case study, identify a relevant problem, define objectives, and gather suitable data. Clean and preprocess data, perform exploratory data analysis, and apply appropriate algorithms for analysis. Summarize findings, visualize results, and provide actionable recommendations, showcasing the problem-solving potential of data science techniques.

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A growing number of enterprises are pooling terabytes and petabytes of data, but many of them are grappling with ways to apply their big data as it grows.

How can companies determine what big data solutions will work best for their industry, business model, and specific data science goals?

Check out these big data enterprise case studies from some of the top big data companies and their clients to learn about the types of solutions that exist for big data management.

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The need to lead in data and analytics

Ash Gupta, chief risk officer, American Express: The first change we had to make was just to make our data of higher quality. We have a lot of data, and sometimes we just weren’t using that data and we weren’t paying as much attention to its quality as we now need to. That was, one, to make sure that the data has the right lineage, that the data has the right permissible purpose to serve the customers. This, in my mind, is a journey. We made good progress and we expect to continue to make this progress across our system.

The second area is working with our people and making certain that we are centralizing some aspects of our business. We are centralizing our capabilities and we are democratizing its use. I think the other aspect is that we recognize as a team and as a company that we ourselves do not have sufficient skills, and we require collaboration across all sorts of entities outside of American Express. This collaboration comes from technology innovators, it comes from data providers, it comes from analytical companies. We need to put a full package together for our business colleagues and partners so that it’s a convincing argument that we are developing things together, that we are colearning, and that we are building on top of each other.

Examples of impact

Victor Nilson, senior vice president, big data, AT&T: We always start with the customer experience. That’s what matters most. In our customer care centers now, we have a large number of very complex products. Even the simple products sometimes have very complex potential problems or solutions, so the workflow is very complex. So how do we simplify the process for both the customer-care agent and the customer at the same time, whenever there’s an interaction?

We’ve used big data techniques to analyze all the different permutations to augment that experience to more quickly resolve or enhance a particular situation. We take the complexity out and turn it into something simple and actionable. Simultaneously, we can then analyze that data and then go back and say, “Are we optimizing the network proactively in this particular case?” So, we take the optimization not only for the customer care but also for the network, and then tie that together as well.

Vince Campisi: I’ll give you one internal perspective and one external perspective. One is we are doing a lot in what we call enabling a digital thread—how you can connect innovation through engineering, manufacturing, and all the way out to servicing a product. [For more on the company’s “digital thread” approach, see “ GE’s Jeff Immelt on digitizing in the industrial space .”] And, within that, we’ve got a focus around brilliant factory. So, take driving supply-chain optimization as an example. We’ve been able to take over 60 different silos of information related to direct-material purchasing, leverage analytics to look at new relationships, and use machine learning to identify tremendous amounts of efficiency in how we procure direct materials that go into our product.

An external example is how we leverage analytics to really make assets perform better. We call it asset performance management. And we’re starting to enable digital industries, like a digital wind farm, where you can leverage analytics to help the machines optimize themselves. So you can help a power-generating provider who uses the same wind that’s come through and, by having the turbines pitch themselves properly and understand how they can optimize that level of wind, we’ve demonstrated the ability to produce up to 10 percent more production of energy off the same amount of wind. It’s an example of using analytics to help a customer generate more yield and more productivity out of their existing capital investment.

Winning the talent war

Ruben Sigala: Competition for analytical talent is extreme. And preserving and maintaining a base of talent within an organization is difficult, particularly if you view this as a core competency. What we’ve focused on mostly is developing a platform that speaks to what we think is a value proposition that is important to the individuals who are looking to begin a career or to sustain a career within this field.

When we talk about the value proposition, we use terms like having an opportunity to truly affect the outcomes of the business, to have a wide range of analytical exercises that you’ll be challenged with on a regular basis. But, by and large, to be part of an organization that views this as a critical part of how it competes in the marketplace—and then to execute against that regularly. In part, and to do that well, you have to have good training programs, you have to have very specific forms of interaction with the senior team. And you also have to be a part of the organization that actually drives the strategy for the company.

Murli Buluswar: I have found that focusing on the fundamentals of why science was created, what our aspirations are, and how being part of this team will shape the professional evolution of the team members has been pretty profound in attracting the caliber of talent that we care about. And then, of course, comes the even harder part of living that promise on a day-in, day-out basis.

Yes, money is important. My philosophy on money is I want to be in the 75th percentile range; I don’t want to be in the 99th percentile. Because no matter where you are, most people—especially people in the data-science function—have the ability to get a 20 to 30 percent increase in their compensation, should they choose to make a move. My intent is not to try and reduce that gap. My intent is to create an environment and a culture where they see that they’re learning; they see that they’re working on problems that have a broader impact on the company, on the industry, and, through that, on society; and they’re part of a vibrant team that is inspired by why it exists and how it defines success. Focusing on that, to me, is an absolutely critical enabler to attracting the caliber of talent that I need and, for that matter, anyone else would need.

Developing the right expertise

Victor Nilson: Talent is everything, right? You have to have the data, and, clearly, AT&T has a rich wealth of data. But without talent, it’s meaningless. Talent is the differentiator. The right talent will go find the right technologies; the right talent will go solve the problems out there.

We’ve helped contribute in part to the development of many of the new technologies that are emerging in the open-source community. We have the legacy advanced techniques from the labs, we have the emerging Silicon Valley. But we also have mainstream talent across the country, where we have very advanced engineers, we have managers of all levels, and we want to develop their talent even further.

So we’ve delivered over 50,000 big data related training courses just this year alone. And we’re continuing to move forward on that. It’s a whole continuum. It might be just a one-week boot camp, or it might be advanced, PhD-level data science. But we want to continue to develop that talent for those who have the aptitude and interest in it. We want to make sure that they can develop their skills and then tie that together with the tools to maximize their productivity.

Zoher Karu: Talent is critical along any data and analytics journey. And analytics talent by itself is no longer sufficient, in my opinion. We cannot have people with singular skills. And the way I build out my organization is I look for people with a major and a minor. You can major in analytics, but you can minor in marketing strategy. Because if you don’t have a minor, how are you going to communicate with other parts of the organization? Otherwise, the pure data scientist will not be able to talk to the database administrator, who will not be able to talk to the market-research person, who which will not be able to talk to the email-channel owner, for example. You need to make sound business decisions, based on analytics, that can scale.

Murli Buluswar is chief science officer at AIG, Vince Campisi is chief information officer at GE Software, Ash Gupta is chief risk officer at American Express, Zoher Karu is vice president of global customer optimization and data at eBay, Victor Nilson is senior vice president of big data at AT&T, and Ruben Sigala is chief analytics officer at Caesars Entertainment.

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Big data: Getting a better read on performance

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Transforming into an analytics-driven insurance carrier

Big data analytics is the use of advanced analytic techniques against very large, diverse big data sets that include structured, semi-structured and unstructured data, from different sources, and in different sizes from terabytes to zettabytes.

What is big data exactly? It can be defined as data sets whose size or type is beyond the ability of traditional relational databases to capture, manage and process the data with low latency. Characteristics of big data include high volume, high velocity and high variety. Sources of data are becoming more complex than those for traditional data because they are being driven by artificial intelligence (AI) , mobile devices, social media and the Internet of Things (IoT). For example, the different types of data originate from sensors, devices, video/audio, networks, log files, transactional applications, web and social media — much of it generated in real time and at a very large scale.

With big data analytics, you can ultimately fuel better and faster decision-making, modelling and predicting of future outcomes and enhanced business intelligence. As you build your big data solution, consider open source software such as Apache Hadoop , Apache Spark and the entire Hadoop ecosystem as cost-effective, flexible data processing and storage tools designed to handle the volume of data being generated today.

Businesses can access a large volume of data and analyze a large variety sources of data to gain new insights and take action. Get started small and scale to handle data from historical records and in real-time.

Flexible data processing and storage tools can help organizations save costs in storing and analyzing large anmounts of data. Discover patterns and insights that help you identify do business more efficiently.

Analyzing data from sensors, devices, video, logs, transactional applications, web and social media empowers an organization to be data-driven. Gauge customer needs and potential risks and create new products and services.

Accelerate analytics on a big data platform that unites Cloudera’s Hadoop distribution with an IBM and Cloudera product ecosystem.

Gain low latency, high performance and a single database connection for disparate sources with a hybrid SQL-on-Hadoop engine for advanced data queries.

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GE’s Big Bet on Data and Analytics

Seeking opportunities in the internet of things, ge expands into industrial analytics., february 18, 2016, by: laura winig, introduction.

If software experts truly knew what Jeff Immelt and GE Digital were doing, there’s no other software company on the planet where they would rather be. –Bill Ruh, CEO of GE Digital and CDO for GE

In September 2015, multinational conglomerate General Electric (GE) launched an ad campaign featuring a recent college graduate, Owen, excitedly breaking the news to his parents and friends that he has just landed a computer programming job — with GE. Owen tries to tell them that he will be writing code to help machines communicate, but they’re puzzled; after all, GE isn’t exactly known for its software. In one ad, his friends feign excitement, while in another, his father implies Owen may not be macho enough to work at the storied industrial manufacturing company.

Owen's Hammer

Ge's ad campaign aimed at millennials emphasizes its new digital direction..

The campaign was designed to recruit Millennials to join GE as Industrial Internet developers and remind them — using GE’s new watchwords, “The digital company. That’s also an industrial company.” — of GE’s massive digital transformation effort. GE has bet big on the Industrial Internet — the convergence of industrial machines, data, and the Internet (also referred to as the Internet of Things) — committing $1 billion to put sensors on gas turbines, jet engines, and other machines; connect them to the cloud; and analyze the resulting flow of data to identify ways to improve machine productivity and reliability. “GE has made significant investment in the Industrial Internet,” says Matthias Heilmann, Chief Digital Officer of GE Oil & Gas Digital Solutions. “It signals this is real, this is our future.”

While many software companies like SAP, Oracle, and Microsoft have traditionally been focused on providing technology for the back office, GE is leading the development of a new breed of operational technology (OT) that literally sits on top of industrial machinery. Long known as the technology that controls and monitors machines, OT now goes beyond these functions by connecting machines via the cloud and using data analytics to help predict breakdowns and assess the machines’ overall health. GE executives say they are redefining industrial automation by extracting lessons from the IT revolution and customizing them for rugged heavy-industrial environments.

One such environment is the oil and gas industry, where GE sees a $1 billion opportunity for its OT software. In an industry where a single unproductive day on a platform can cost a liquified natural gas (LNG) facility as much as $25 million, the holy grail for oil and gas is minimizing “unplanned downtime” — time that equipment is unable to operate due to a malfunction. Ashley Haynes-Gaspar, software and services general manager at GE Oil & Gas, notes that refining operations are typically tightly run but are in hard-to-access, remote locations. Increasing uptime is critical — particularly with oil prices at their lowest in six years. “An average midsize LNG facility sees five down days a year. That’s $125 million to $150 million. For an offshore platform, it can be $7 million per day, including oil deferrals, and these assets are never down for a single day. They have got to figure out how to drive productivity in their existing assets,” she says, “especially now that they are facing declining revenues from lower energy prices.”

What Is the Industrial Internet?

Ge is developing an industrial internet platform for the oil and gas sector..

Improving the productivity of existing assets by even a single percentage point can generate significant benefits in the oil and gas sector (and in other sectors). “The average recovery rate of an oil well is 35%, meaning 65% of a well’s potential draw is left in the earth because available technology makes it too expensive,” explains Haynes-Gaspar. “If we can help raise that 35% to 36%, the world’s output will increase by 80 billion barrels — the equivalent of three years of global supply. The economic implications are huge.”

GE executives believe software, data, and analytics will be central to the company’s ability to differentiate itself within the oil and gas industry. “I think the race is on from a competition perspective,” says Haynes-Gaspar, “and everybody understands the size of the Industrial Internet prize.”

The Software Behind GE’s Industrial Internet

In September 2015, GE projected its revenue from software products would reach $15 billion by 2020 — three times its 2015 bookings. While software sales today are derived largely from traditional measurement and control offerings, GE expects that by 2020, most software revenue will come from its Predix 1 software, a cloud-based platform for creating Industrial Internet applications.

GE has long had the ability to collect machine data: Sensors have been riding on GE machines for years. But these pre-Internet of Things (IoT) sensors were used to conduct real-time operational performance monitoring, such as displaying a pressure reading on a machine, not to collect data. Indeed, a technician would often take a reading from a machine to check its performance and then discard the data.

GE researched companies that were producing high-quality data analytics quickly and inexpensively, but it wasn’t the traditional IT companies that were excelling; it was the consumer-facing Internet giants. GE drew lessons from these companies around speed and cost, though the scale and data were different. Indeed, the sheer volume of data that GE hoped to collect — 50 million data variables from 10 million sensors installed on its machines — would be many times more than most social and retail sites could ever generate. “Machines generate time-series data, which is very different than social or transactional data. We had to optimize for the kinds of analytics that would help us understand the behavior of machines,” says Bill Ruh, GE’s chief digital officer.

To handle these massive data sets, GE needed a new platform for connecting, securing, and analyzing data. They began developing their solution in 2012, a cloud-based software platform named Predix that could provide machine operators and maintenance engineers with real-time information to schedule maintenance checks, improve machine efficiency, and reduce downtime. Initially developed for GE, not only would this data inform their own product development activities, but it would also lower costs in its service agreements. “When we agree to provide service for a customer’s machine, it often comes with a performance guarantee,” explains Kate Johnson, vice president and chief commercial officer, GE Digital. “Proactive identification of potential issues that also take the cost out of shop visits helps the customer and helps GE.”

It didn’t take long for GE engineers to realize that they could find interesting and unique patterns in the data. They thought the patterns of sensor data could be used to provide an early — albeit weak — signal of future performance problems and better predict when its machines should be scheduled for maintenance. In early 2013, GE began to use Predix to analyze data across its fleet of machines. By analyzing what differentiated one machine’s performance from another — what made one more efficient, for example — GE could more tightly hone its operational parameters. “We’re moving from physics-based modeling, where you create maintenance manuals based on generic operating models, to combining it with very high-performance analytics,” says Ruh. When GE combined the physics modeling and the data modeling, it found that, in Ruh’s words, it could “do what no one’s ever done in the world before for industry.”

Predix: GE's Platform for the Industrial Internet

Ge develops an internet of things platform to drive productivity..

For example, in the last few years, GE started to notice that some of its jet aircraft engines were beginning to require more frequent unscheduled maintenance. “If you only look at an engine’s operating parameters, it just tells you there’s a problem,” says Ruh. But by pulling in massive amounts of data and using fleet analytics, GE was able to cluster engine data by operating environment. The company learned that the hot and harsh environments in places like the Middle East and China clogged engines, causing them to heat up and lose efficiency, thus driving the need for more maintenance. GE learned that if it washed the engines more frequently, they stayed much healthier. “We’re increasing the lifetime of the engine, which now requires less maintenance, and we think we can save a customer an average of $7 million of jet airplane fuel annually because the engine’s more efficient,” Ruh explains. “And all of that was done because we could use data across every GE engine, across the world and cluster fleet data.” Johnson credits Predix directly with improving the productivity of these engines, as this would not have been possible without a robust data and analytics platform.

That same year, GE executives began to think there could be a market opportunity for Predix, much as Amazon.com Inc. created a market for its cloud-computing platform, Amazon Web Services Inc. “We realized that there were three developing markets for cloud platforms — consumer, enterprise, and industrial. Industrial was essentially being treated as an extension of enterprise, which we knew wouldn’t work. There were no credible cloud-based platforms for industrial being developed, and we saw that as a potential opportunity for growth,” says Ruh. Why now? GE executives say the economics of amassing, storing, and running analytics on large lakes of data — pools of customer data that combine maintenance and repair data with time-series performance information — have dropped dramatically in the last 10 years, making the market viable.

The driving force behind taking Predix to market was the scope of the opportunity: GE determined that the market for a platform and applications in the industrial segment could reach $225 billion by 2020. 2 GE spent a year evaluating the market, all the while using Predix 1.0 to further develop its offerings and collect internal and external feedback. The company built a team to develop the commercial version, Predix 2.0, and in October 2015 made the platform directly available to channel and technology partners as well as customers who could use the platform to build their own set of analytics. “We feel we’ve got it right for ourselves, and now we’re taking it out to customers and partners,” says Ruh.

A New Approach to Oil and Gas

GE has enjoyed success as a physical infrastructure provider, known worldwide as the company that “brings good things to life.” But that corporate identity is beginning to shift. “When we think about the future, digitizing our customers’ businesses requires a technology shift, a business model shift, and a skill shift,” says Ruh, noting that GE and its customers will need to optimize all three to be successful. Indeed, in the oil and gas sector, where customers have been struggling to improve productivity amid declining revenues, GE is using Predix to transform what it’s selling, how it’s selling, and who is invited to the negotiating table.

Delivering New Services to a Conservative Market

GE entered the oil and gas industry in 1994 through the acquisition of Italy-based Nuovo Pignone, a manufacturer of turbo machinery, compressors, pumps, static equipment, and metering systems. Just over 20 years later, GE’s oil and gas subsidiary has become a roughly $20 billion business, ranging from oil and gas drilling equipment and subsea systems to turbo machinery solutions and downstream processing. The company considers itself a “full-stream” provider, operating across the entire oil and gas value chain: upstream exploration and production, midstream transportation (via pipeline, oil tanker, or the like) and storage of crude petroleum products, and downstream refining of petroleum crude oil and processing of natural gas.

GE’s customers in the oil and gas market are known for being conservative, a necessity in the highly dynamic and sometimes harsh environments in which they work. “They wait for their peers to try something new, and if it’s successful and they see there’s value in it without added risk, they jump all over it,” says Steve Schmid, GE senior product manager. As a result, GE runs pilots — lots of pilots. “I tell customers about a new product and a pilot we’re doing with a highly respected operator in their industry. They say, ‘Fantastic, keep us up to speed on your progress. Once you’ve got a product released, we want to know the results, and then we’ll be interested in entertaining moving forward with a proposal.’ But it’s difficult to take that first leap,” says Schmid. “However, those that do so are the first to gain an incredible competitive advantage in the market — and others are soon a fast follow.”

Jeff Monk, GE’s global key account executive for North America, adds: “In general, oil and gas companies like to keep things close to their chests. They don’t see a lot of value in publicizing the details of their operations — whether those be big wins or big saves. If a customer can save $100 million as a result of data analytics, that’s great, but they will be concerned about publicizing that because they believe stakeholders will think they might not have avoided those costs. Oil and gas companies have to come to terms with the market’s need for them to be more transparent, and the added value that data and analytics provide is relatively new, so that issue is somewhat mitigated.”

GE’s Intelligent Pipeline

Ge uses the industrial internet to improve safety and lower risk for pipeline operators., new service value propositions.

GE believes Predix can help the oil and gas industry address four of its most pressing challenges: improving asset productivity; creating a real-time picture of the status of an entire operation; stemming the costly loss of tacit knowledge from an aging workforce; and building an Industrial Internet platform that meets customer needs.

Asset Productivity

GE had spent years developing analytic applications to improve the productivity and reliability of its own equipment, with oversight from GE global monitoring centers. GE’s strategy is to deploy these solutions and then expand to include non-GE plant equipment as part of the solution. GE’s work with RasGas Company Limited, one of the world’s foremost integrated LNG enterprises, is an example of that approach. RasGas’s LNG production facility in Ras Laffan, Qatar — where over 2,000 critical assets are installed — has the capacity to produce approximately 37 million tons of LNG per year.

GE’s equipment specialists and analytic scientists, who were already monitoring the GE turbine components of the RasGas production power trains, worked closely with RasGas operations experts to enhance LNG productivity overall, including the non-GE components. This close collaboration identified critical components, failure modes, and process challenges.

In an initial proof of concept, the team focused on three LNG trains. Their primary goal was to demonstrate that a suite of next-generation predictive analytics tools could enhance asset reliability and maintenance effectiveness while optimizing processes. In early results of the asset performance management (APM) solution implementation, the team identified areas of improvement to eliminate wastes in production on one of the trains, which will translate to a significant LNG production improvement. Today, GE and RasGas are working in conjunction toward a full-plant APM solution deployment at the Ras Laffan site.

Operations Productivity

GE wants to go beyond helping its customers manage the performance of individual GE machines to managing the data on all of the machines in a customer’s entire operation. For example, if an oil and gas customer has a problem with a turbo compressor, a heat exchanger upstream from that compressor may be the original cause of the problem. Analyzing data from the turbo compressor will thus only tell part of the story. “We’re selling equipment that sits side by side with competitors’ equipment. Our customer cares about running the whole plant, not just our turbine,” says Johnson. GE is in discussions with some customers about managing sensor data from all of the machine assets in their operation.

Customers are asking GE to analyze non-GE equipment because those machines comprise about 80% of the equipment in their facilities. “They want GE to help keep the whole plant running. They’re saying, ‘It does me no good if my $10 million gas turbine runs at 98% reliability if I have a valve that fails and shuts my entire plant down,’” explains Dan Brennan, executive director for the Industrial Internet for GE Oil & Gas. Indeed, capital-intensive equipment such as gas turbines are already well-instrumented with sensors and data controls. “They’re well-protected to make sure they’re reliable,” he says. The supporting, smaller-investment equipment often did not warrant the cost of instrumentation and data gathering. But industry thinking has evolved as the cost of getting data from those less expensive assets has declined logarithmically, opening up a whole new world of monitoring by looking at the system not as a collection of critical equipment but as an ecosystem. For one pilot program with a major customer, GE is analyzing data on all their rotating and static equipment — regardless of the machines’ original equipment manufacturer (OEM). The first phase is on all 160 of the customer’s gas turbines across the world, even though only 40% of them are GE gas turbines. GE characterizes it as an “agnostic” solution.

How do the OEMs feel about having their equipment monitored by GE? Erik Lindhjem, executive product line leader for GE Oil & Gas, acknowledges that some competitors are more comfortable with the idea than others. “I think there’s an uneasiness for some of the GE OEM competitors about how the data’s collected and our reach into it. However, ultimately it’s the end user — our mutual customer — who drives the use of the technology and the data,” says Lindhjem. GE also points out that its competitors are quietly exploring the same strategy, albeit not by building it themselves but by partnering with other software providers. Siemens has entered into a partnership with SAP, while Solar Turbines, a Caterpillar company, has partnered with a startup, Uptake, to try to equalize the value proposition that GE is bringing to market.

Because Predix is an open platform, GE Oil & Gas Digital Solutions CDO Heilmann emphasizes that GE encourages developers to write applications to support their own needs. “I can see a day in the future where we would encourage other OEMs, whether it’s a pump or a fan or a valve manufacturer, to participate in the Predix ecosystem and write applications for their own equipment and use the data to improve the operation of their own equipment,” he says.

In today’s oil and gas industry, operators seldom share data and collaborate with one another. “Operators view that data as a source of competitive advantage,” says Brennan. Schmid agrees, noting that oil and gas operators differentiate themselves by the way they operate: “They can buy the same tools, the same type of equipment. And they have very few choices in suppliers. We’re not at a point where they’re going to start sharing how their operational excellence gets them a lower cost per barrel of oil than their competitors. Today, the only cross-operator data they are interested in is baselining against their competitors on how they perform, but not sharing how they actually drive up their efficiency over their competitors.”

Brennan says oil and gas companies are unlikely to ever share data around their exploration activities, but he could imagine a day when they might be willing to share operational data. “Maybe five or six years from now, we’ll begin to see companies more willing to share data that could unlock new levels of collaboration across the entire supply chain. They might start to release pockets of data if they realize they can learn from each other and drive efficiencies back into the entire industry,” he says. One path forward would be by sharing anonymized data. Maybe. “I’m hopeful because I think that’s where you’re going to see the next tranche of value.”

GE has already begun to discuss the idea of anonymizing data with customers, but without much success. “We’ve said, ‘What if you could see how you were doing relative to everybody else in the industry on a completely anonymized basis?’ And everybody’s response has been, ‘That’s great, but my data won’t be in the set,’” says Haynes-Gaspar, though she, too, is optimistic that given time and perspective, the industry will likely come to embrace sharing. “Because the things that are differentiating for them today will change based on how the Industrial Internet shapes where we’re going,” she says.

GE Digitizes Experience-Based Knowledge

Ge develops a software solution to meet the challenge of a workforce in flux., support for an aging workforce.

A large portion of the oil and gas workforce — by some estimates as much as 50% — will be retiring by 2025, taking years and even decades of domain knowledge with them. “There’s a lot of knowledge in people’s heads that has not been digitized, documented, and built into practice,” says Brad Smith, business leader for the GE Oil & Gas Intelligent Pipeline business unit. He says some companies with multimillion-dollar operations are dependent upon too few individuals whose domain knowledge can be very difficult to replace, which will challenge the industry to maintain or improve workforce productivity.

The retirement problem is most visible upstream, during the exploration process and at the oil wells themselves. Ron Holsey, Digital Commercial Leader, Surface, GE Oil & Gas, explains:

As we bring a well into production, the natural pressure of the reservoir makes the oil flow to the surface. Over time, that pressure subsides and operators need artificial lift to pull the oil out of the ground. The engineers in the field have 20, 30, 40 years of experience — they’re like pumping-unit whisperers. They can walk up to a unit, and they can hear it creak and groan and grind and understand the stress points. Today, if you’re at a university studying to be a petroleum engineer, you get maybe one textbook chapter during your four years on artificial lift. We just don’t have the next wave of engineers coming up.

To stanch the brain drain over the past few years, many companies have been forced to hire back retiring workers to serve as consultants — an unsustainable practice. Brennan says that the industry needs a way to capture and codify the departing knowledge. “But there’s also got to be a refresh so that you can attract new talent and arm them with tools, technology, and the capability to get their work done efficiently,” he says. “The tools that a 26- or 28-year-old PhD in petroleum engineering wants to use are fundamentally different than ones preferred by somebody who’s leaving the workforce,” he adds, noting that Millennials expect that cutting-edge analytics and tools will be available in the workplace. “When I entered the workforce, I had better tools at work than I’d ever had access to at home, but most of the employees we bring into GE have access to better technology than we allow them here, and we’ve been playing catch-up. The same thing is happening with our customers,” says Brennan.

GE hopes to use Predix to help its oil and gas customers fill the industry’s talent and knowledge gap, but there are challenges associated with trying to essentially supplant human experience with analytics. For one, the industry is extremely slow to adopt new technology. A related problem is getting engineers to trust the data. GE had well-analysis software that could determine if a pump was operating at its optimal range and adjust its speed to run faster or slower to optimize production. Despite several case studies and a pilot on 30 wells showing an incremental gain of 1 million barrels of oil over two years, GE’s customers will not allow the software to change pump speeds on its own. “We still have to have the human intervention. Why? The customer and the industry are reluctant to adopt the new technology,” says Holsey.

Platform Benefits

Predix was designed to be a software platform, not just a tool for collecting, analyzing, and managing sensor data. For GE customers, this approach is expected to have several benefits. The platform has open standards and protocols that allow customers to more easily and quickly connect their machines to the Industrial Internet. The platform can accommodate the size and scale of industrial data for every customer at current levels of use, but it also has been designed to scale up as demand grows. The number and variety of Predix-related apps are not limited to what GE offers. Whereas customers may develop their own custom applications for use on the Predix platform, GE executives are working to build a developer community and create a new market for apps that can be hosted on the Predix platform. Finally, data security, a concern for many companies considering IoT applications, is embedded at all platform application layers: services enablement, data orchestration, and infrastructure layers.

Transforming the Sales Process

GE’s traditional sales model within the oil and gas industry has been big-ticket and transactional: Customers purchased machines, as well as parts, maintenance, and repair service contracts at fixed prices — what Brennan refers to as a tactical, product-centric sales model.

Adding Predix software to the mix has made the sales process more complex and much less product-centric, even though the new software represents a relatively small sell — a “tiny little sprinkling on top of the deal,” says GE Digital’s Johnson. GE’s salespeople, for instance, have to engage in more strategic conversations around solutions rather than product features. Software technicians are involved earlier in the selling process. “Now, when we sell an electronic submersible pump,” says Johnson, “the equipment sales manager brings along an application engineer who understands technically how that pump is going to operate in the environment in which it’s going to be placed. You need the same exact set of capabilities when you’re talking about software. You need to know how it fits into the overall solution architecture of hardware, software, and service. Your typical hardware sales leader doesn’t have the Predix domain knowledge to have that conversation for software.”

Not surprisingly, GE has had to address internal resistance to the shift away from a productcentric sales focus. “It was definitely a change in how our sales personnel approach customer conversations,” says Lorenzo Simonelli, president and CEO of GE Oil & Gas. “Instituting software-solution selling techniques required us to embark on both a training exercise and an effort to infuse our organization with software selling expertise, working with our GE industry experts who have held customer relationships for decades. Additionally, these types of digital conversations and sales decisions now involve new customer participants. We needed to understand these roles so each could become Industrial Internet champions in their respective organizations.”

GE is finding that its traditional buyer — typically a VP of operations or plant manager — is changing too, as increasingly the CIO now takes a seat at the table. In the past, GE’s salespeople never brought customers’ CIOs into the selling process; they spent most of their time in the production and engineering offices. But selling software is completely different than selling hardware. “You’ve got different stakeholders, like the CIO and the CTO, suddenly coming into the story,” says Johnson. Smith agrees that CIOs and CTOs are becoming critical stakeholders and have places at the table — though he still believes the decision making will reside with the COO. “Operations will define the requirements and agree on the scope because the IT teams will inherently not bring the domain expertise that their operational partners have acquired through years of practice,” he says. “But the programs, large and small, will be governed, purchased, and project-managed out of the IT organization.” Indeed, Johnson says CIOs may end up being the true heroes of the digital industrial space: “CIOs are starting to wake up to the enormous opportunity they have in the operational technology space where they have traditionally not been a part of decision making.”

Though many of GE Oil & Gas customers are already familiar with the company’s software offerings, Predix adds another dimension to the conversation. “Generally we’re first met with, ‘Well, what is this Industrial Internet?’” says Brennan. Typically, a discussion around system architecture ensues. “CIOs’ concerns are around connectivity,” says Holsey. “The challenge is, instead of the old ‘rack it and stack it and have servers sitting in the client’s office,’ do I go to a cloud-based system?” Customers are starting to come around, particularly U.S. companies. “They have started to evolve from, ‘That’s the most insane thing I’ve ever heard of, putting it on a cloud,’ to, ‘Wow, it’s easier to maintain and I don’t have to worry about keeping my servers up to date.’ There’s more acceptance now,” says Holsey. However, he thinks it will take another three to five years to become commonplace: “Then, there won’t be a question of whether a cloud or not a cloud.”

GE sales teams were finding that one of the most significant value propositions they had to offer customers was the creation of data lakes that sit on the cloud and enable data sharing and analytics. “Today, those systems don’t talk. But with Predix, customers are able to have a data lake, their data in a central repository,” says Haynes-Gaspar. As a result, oil and gas operators with operations in disparate parts of the world can allow local operators to share vital, problem-solving data.

Selling by Pilot

For GE, a pilot is often an essential step of the adoption process. In early 2015, GE executed a four-week engagement with one of the largest global energy companies, which wanted to reinvent how it manages its “static” equipment — specifically, its storage tanks used during oil and gas processing. Like other producers, this company spends heavily on maintaining these assets from corrosion and other threats but because they aren’t instrumented, they are inspected infrequently, often once every two or three years. GE positioned Predix to help the customer rethink how it manages them. During the four-week exercise, GE’s designers met with the customer’s subject-matter experts, corrosion engineers, and reliability engineers. At the end of the four weeks, GE developed a software solution that took the form of a scripted narrative on a static set of screens. The customer used the screens to “walk through” how its reliability engineers could use them to better manage its static assets. Though the project was successful in its own right, it also offered GE a way to discuss future engagements.

GE hopes to have three more customers booked by early 2016 to run pilots — or what Brennan refers to as “lightweight proof of concept” — for Predix offerings. GE executives see the pilots as a way to bring customers onto the selling team. “To get anywhere in the oil and gas industry, we need help selling. We need customer voices out in the industry with success stories, or we’re just not going to come to the table with the credibility that we need. So we need to inspire our customers to want to do that.”

Outcome-Based Pricing

Whether a customer buys an aircraft engine or a heavy-duty gas turbine, GE and the customer often arrange a 10- to 15-year contractual services agreement that allows the company to connect to and monitor that machine, perform basic maintenance and diagnostics, and provide fixed-interval repairs. If GE keeps the equipment running at a certain agreed-upon threshold, the company receives a bonus payment. Such outcome-based pricing may also be applied to coverage of non-GE machines.

Moving forward, GE will use a subscription model to commercialize its software, relying less on traditional licensing models. “We believe deeply in subscription models being the future, so we’re trying to build subscriptions that are priced based on the value that they deliver,” says Johnson. “If we’re talking about taking cost out of the service agreement and providing a subscription to an app that has a set of analytics to help do that, then it’s outcome-based.”

Holsey says the pricing model is evolving from a capital expenditure that bundles equipment with service and software to an operational expense model. “We’ve got some customers that don’t ever want to actually buy the equipment. They just want us to come out and get paid based on production. It’s a service contract that wraps up equipment, services, software, and all the analytics,” says Holsey. He thinks it represents GE putting its money where its mouth is. “If we improve, say, their power consumption by X, we get $1; by Y, we get $1.50,” he says, noting that customers are becoming more open to this type of arrangement and to sharing the data necessary to establish the baseline measurements that makes the model work. “In order for us to leverage the analytics, customers are asking us to put a little bit more risk on the table ourselves, and that’s the difference that we’ve seen in the market,” says Holsey.

Where it can get a little muddy is in trying to assign value to what oil and gas operators bring to the table. “With a company like GE, our intellectual property is a very tangible asset for us. You can look at a drawing. You can see a piece of equipment. For an oil and gas operator, their intellectual property is the way they operate. So when they participate in a program like this with GE, we truly respect the fact that they’re giving us insights into their operations, and that’s really their intellectual property,” says GE’s global key account executive for North America Monk.

In agreeing to outcome-based pricing, customers will also need to cede some control to GE. “If we’re going to work with an oil and gas drilling contractor and commit to a certain amount of nonproductive time decrease, then we have to have some contractual agreements with both the driller and the contracting oil company on what levers we can pull. For example, what data do we have access to? What operational influence do we have in terms of calling for scheduled downtime?” says Jeremiah Stone, general manager for GE Digital’s Industrial Data Intelligence business.

Racing to Lead the Industrial Internet

For GE, its big bet on data coincided with widespread investment in the Industrial Internet. Global spending on the Industrial Internet was $20 billion in 2012. Analysts were forecasting that number would reach $514 billion by 2020, creating nearly $1.3 trillion in value. 3

Both traditional information technology and operations technology players might have posed a competitive threat to GE's emerging Industrial Internet business. But GE executives do not worry much about these competitors because, they say, these companies make moves that are highly visible. While acknowledging that big technology stalwarts are very good at analyzing historical data and in areas like artificial intelligence, Brennan adds that the work of connecting and monitoring large volumes of real-time data is not their core strength. GE's manufacturing expertise, he says, is also a source of market advantage especially against traditional IT companies that try to adapt their enterprise IT software to a heavy-industrial operations technology environment.

What Haynes-Gaspar does lose sleep over, though, are the nimble Silicon Valley startups that are creeping into the space — and have the full attention of venture capital firms. One such example is a startup that offers cloud-based production optimization solutions for the oil and gas industry. “These guys came to talk to us and they had just graduated with PhDs in advanced mathematics from Stanford and they told us, ‘You’re big GE. Why don’t you let us take the data and the analytics, and you guys can be the equipment guys.’ The hubris was breathtaking, but their company was valued for $100 million a year later,” says Haynes-Gaspar. They may not have GE’s billion dollars, but they received millions in round A funding, which, “focused in the right way, could get them market share fast,” she says. Indeed, more than $1.6 billion in venture capital has been pumped into the Internet of Things space, specifically to capitalize on software, data, and analytics opportunities. “It keeps me up at night, too,” says Brennan.

Despite GE’s $1 billion investment in the Industrial Internet, the company won’t be going it alone and wants to partner with startups. “We can’t do everything ourselves, nor should we. There are things that we need to be unapologetically awesome at, and there are areas where we need to partner,” says Haynes-Gaspar. GE wants to be “unapologetically awesome” at data and analytics. “That’s where we’re making our organic investment.” She cites oil and gas market and trading systems as an example of a space where GE does not have domain knowledge and therefore prefers to partner with companies that do. GE intends to build up a large base of partners and together create a robust ecosystem capable of managing every facet of an oil and gas operator’s operation through Predix. “That’s the vision,” says Haynes-Gaspar.

GE also has one very significant advantage over the startups: deep, established relationships with virtually every major industrial company in its competitors’ crosshairs. “Yes, in the past we may have been perceived as big and slow. We may not have had the same reputation as nimble startups out here in Silicon Valley, but we’ve got access to customers,” says Brennan. “With the establishment of GE Digital, we now have access to resources at a scale that will allow us to speed innovation around our customers’ biggest problems. We believe this combination of speed and customer access is one that our competitors won’t be able to touch.”

Simonelli says GE’s days of being seen solely as an equipment provider are drawing short: “Software puts us at the table as a true partner at the heart of the discussion around outcomes our customers are trying to deliver. We will be there for the entire life cycle of a project and solution for 20 to 30 years.”

Undoubtedly, as GE brings more and more of its customers’ data under its management, new kinds of business models will be developed. “Instead of assets under management, the new metrics will become data under management,” says Stone. He envisions a future similar to the one taken by GE’s commercial financing teams. “They actually created a pipeline for the equipment businesses, by virtue of the capital business. We think Predix can play that role as well as we demonstrate the value of a digital industrial approach.”

“The race is on,” Schmid says, admitting that, realistically, GE is taking its initial steps on a very long road. “First, GE needs to be seen as a leader in the industrial enterprise software space.” GE executives believe the company can follow in Google’s footsteps and become the entrenched, established platform player for the Industrial Internet — and in less than the 10 years it took Google to reach that status. “I think that our investment appetite combined with the lessons that we’ve learned on the consumer side, we ought to do it in less time, but that’s a journey that we’re on. First we have to continue on our path to being a great application provider,” says Brennan.

GE’s Calculated Bet on Analytics

Sam Ransbotham

By now, the imminent onset of the Internet of Things isn’t a “dark horse” anymore. Nor is it a mystery that one of the biggest players in the emerging IoT game is GE . But most people aren’t just wondering whether GE’s IoT gamble will pan out in the end — they’re also wondering what playing a similar hand might mean for their organization. Fortunately, GE’s “big bet” gives us insight into four transitions stemming from the soon-to-be ubiquitous stream of IoT data.

Transition #1: The transition from reactive to proactive. The standard mantra of IoT goes like this: With countless of interdependent parts, something is bound to break, fail, or decrease in performance — whether it’s an oil refinery, a jet engine, or a small patch of material within miles upon miles of natural gas or oil pipeline, there is no question in anyone’s mind that entropy or the environment will somewhere create a breakdown in the system. The IoT’s ultimate raison d’être — one that GE is investing in heavily — is to give everyone a heads up on what specific thing will break and when its performance will sag, so that those who make repairs can be in the right place at the right time to avoid an Aliso Canyon-type equipment failure that leads to a natural, economic, and public health disaster .

The concept of anticipating isn’t new; organizations have long prepared for problems by preordering parts according to an average life expectancy, servicing equipment at routine intervals, or locating repair stations so that average response time is low. But GE’s work with sensor data underscores the depth of evidence that can bolster companies’ preparation as well as the breadth of interconnected organizations that can benefit from it — and those are the new factors that IoT allows. In the case, this depth and breadth let GE and others know the ways in which specific pieces of equipment and machines are not average, profiting by being precisely prepared for (or even preventing) adverse events — meaning, they spend exactly the time and resources needed to address them, no more and no less, in contrast to the outlays made by other organizations expensively over-preparing or cleaning up after a disastrous equipment failure.

Transition #2: Analytics helps organizations transition from myopic to holistic. GE knows that, best case, their equipment is a small fraction of the total equipment at a site, yet all of these other components work together with GE’s components. As processing, storage, and communication costs continue their relentless decline, it is feasible to gather data on more and more equipment. Combining data from more equipment lets GE help their customers avoid analytical myopia , where pursuit of isolated gains can distract from the bigger picture. For GE, this holistic approach prompted them to work to integrate data from multiple customers — even competitors’ machines. Unsurprisingly, GE has struggled with this step, as everyone loves the idea of benefiting from everyone else’s data, but is far less excited about sharing their own — a tragedy of the commons . The potential is there, but incentives are not well aligned.

Transition #3: Analytics helps organizations transition from physical to digital. GE has long been known for physical products — lighting, radios, televisions, turbines, industrial automation, motors, locomotives, jet engines, etc. But the company’s big bet on digital is, well, big. Huge, even.

Moving away from a product-centric sales focus is a radical change — and it’s not without risks. Certainly the Predix platform offers GE another way to provide additional value . In GE’s case, a complete switch from atoms (physical) to bits (digital) is unlikely and unnecessary — the optimal solution will likely be a blend, where the physical and digital products create the most value by working together. If it’s done well, GE may create a virtuous cycle of physical and digital lock-in as customers embed GE components not only in their infrastructure, but also their processes. Done poorly, GE may find itself casting aside a century of experience to become just another cloud vendor.

Transition #4: Analytics helps organizations transition from people to machines. As society races to build smarter and smarter machines, not every retiring member of the “aging workforce” will be replaced by another worker, or at least not one doing the same job. This also isn’t new; the type of work people do is always changing. What has changed is the efficacy of ways to capture and codify the “departing knowledge” — which, for GE, starts with capturing vast data. Not all of it will be immediately useable or eliminate the need for workers. However, data and analytics will change the mix of people that GE needs going forward as more and more operational knowledge is embedded into algorithms.

So is GE actually making a “big bet”? Betting involves chance and randomness, and GE’s future in this arena is far from certain — so there is a chance this wager may be lost.

But it isn’t that simple. Betting involves both calculating the stake and assessing the odds.

Consider the stake: In GE’s case, the stake of their bet on data and analytics can’t be measured in isolation. The alternative of not pursuing data and analytics also has a sizeable stake. Refusing to go in this direction may not be a safe alternative, as data from devices has the potential to radically redistribute market power.

Now consider the odds: GE can do (and is doing) a lot to improve their odds of success. Instead of waiting to see how everything turns out, GE is working to minimize uncertainty. This work isn’t dramatic or glamorous. It requires vigilance with data governance, attention to details of massive infrastructure, and adjustments in culture. They show efficacy with small pilots, make a small change, and then show efficacy again. Every small step is work, but it adds up to advantage. In this way, GE is not only making their odds better — it’s also making the potential jackpot bigger. Thus, their IoT strategy is by no means a game of Russian roulette — it’s more like a fast-flowing game of poker.

What can other organizations learn from observing GE’s “big bet”?

First, keep in mind that GE isn’t starting from zero; they have an ace in the hole. They are building digital advantage from competence in complementary physical assets. Other organizations must carefully assess their baseline capabilities to know what their unique advantage might be.

Second, GE has paid their ante to get a seat at this table. The company has a history of exploratory initiatives around analytics that gave them the experience necessary to even consider these transitions. Other organizations must put in the effort required to build basic analytical capabilities.

Third, GE knows their cards, and they are not bluffing. Before putting in their chips, they learned what they were playing for. They learned from existing data about oil platforms to quantify the potential savings from changing unplanned maintenance to planned. They learned from consumer-facing Internet giants but recognized differences in their own context. They learned from using Predix internally to “tightly hone” their own operations before introducing it externally. Other organizations likewise need to learn where data and analytics could make a difference… and where there is less potential.

Sam Ransbotham is an associate professor of information systems at the Carroll School of Management at Boston College and the MIT Sloan Management Review guest editor for the Data and Analytics Big Idea Initiative. He can be reached at [email protected] and on Twitter at @ransbotham.

About the Author

Laura Winig is a contributing editor to MIT Sloan Management Review .

1. Predix is a trademark of General Electric Company.

2. M. LaWell, “Building the Industrial Internet With GE,” IndustryWeek, October 5, 2015.

3. D. Floyer, “Defining and Sizing the Industrial Internet,” June 27, 2013, http://wikibon.org.

i. S. Higginbotham, “BP Teams Up With GE to Make Its Oil Wells Smart,” Fortune, July 8, 2015.

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8 case studies and real world examples of how Big Data has helped keep on top of competition

Fast, data-informed decision-making can drive business success. Managing high customer expectations, navigating marketing challenges, and global competition – many organizations look to data analytics and business intelligence for a competitive advantage.

Using data to serve up personalized ads based on browsing history, providing contextual KPI data access for all employees and centralizing data from across the business into one digital ecosystem so processes can be more thoroughly reviewed are all examples of business intelligence.

Organizations invest in data science because it promises to bring competitive advantages.

Data is transforming into an actionable asset, and new tools are using that reality to move the needle with ML. As a result, organizations are on the brink of mobilizing data to not only predict the future but also to increase the likelihood of certain outcomes through prescriptive analytics.

Here are some case studies that show some ways BI is making a difference for companies around the world:

1) Starbucks:

With 90 million transactions a week in 25,000 stores worldwide the coffee giant is in many ways on the cutting edge of using big data and artificial intelligence to help direct marketing, sales and business decisions

Through its popular loyalty card program and mobile application, Starbucks owns individual purchase data from millions of customers. Using this information and BI tools, the company predicts purchases and sends individual offers of what customers will likely prefer via their app and email. This system draws existing customers into its stores more frequently and increases sales volumes.

The same intel that helps Starbucks suggest new products to try also helps the company send personalized offers and discounts that go far beyond a special birthday discount. Additionally, a customized email goes out to any customer who hasn’t visited a Starbucks recently with enticing offers—built from that individual’s purchase history—to re-engage them.

2) Netflix:

The online entertainment company’s 148 million subscribers give it a massive BI advantage.

Netflix has digitized its interactions with its 151 million subscribers. It collects data from each of its users and with the help of data analytics understands the behavior of subscribers and their watching patterns. It then leverages that information to recommend movies and TV shows customized as per the subscriber’s choice and preferences.

As per Netflix, around 80% of the viewer’s activity is triggered by personalized algorithmic recommendations. Where Netflix gains an edge over its peers is that by collecting different data points, it creates detailed profiles of its subscribers which helps them engage with them better.

The recommendation system of Netflix contributes to more than 80% of the content streamed by its subscribers which has helped Netflix earn a whopping one billion via customer retention. Due to this reason, Netflix doesn’t have to invest too much on advertising and marketing their shows. They precisely know an estimate of the people who would be interested in watching a show.

3) Coca-Cola:

Coca Cola is the world’s largest beverage company, with over 500 soft drink brands sold in more than 200 countries. Given the size of its operations, Coca Cola generates a substantial amount of data across its value chain – including sourcing, production, distribution, sales and customer feedback which they can leverage to drive successful business decisions.

Coca Cola has been investing extensively in research and development, especially in AI, to better leverage the mountain of data it collects from customers all around the world. This initiative has helped them better understand consumer trends in terms of price, flavors, packaging, and consumer’ preference for healthier options in certain regions.

With 35 million Twitter followers and a whopping 105 million Facebook fans, Coca-Cola benefits from its social media data. Using AI-powered image-recognition technology, they can track when photographs of its drinks are posted online. This data, paired with the power of BI, gives the company important insights into who is drinking their beverages, where they are and why they mention the brand online. The information helps serve consumers more targeted advertising, which is four times more likely than a regular ad to result in a click.

Coca Cola is increasingly betting on BI, data analytics and AI to drive its strategic business decisions. From its innovative free style fountain machine to finding new ways to engage with customers, Coca Cola is well-equipped to remain at the top of the competition in the future. In a new digital world that is increasingly dynamic, with changing customer behavior, Coca Cola is relying on Big Data to gain and maintain their competitive advantage.

4) American Express GBT

The American Express Global Business Travel company, popularly known as Amex GBT, is an American multinational travel and meetings programs management corporation which operates in over 120 countries and has over 14,000 employees.

Challenges:

Scalability – Creating a single portal for around 945 separate data files from internal and customer systems using the current BI tool would require over 6 months to complete. The earlier tool was used for internal purposes and scaling the solution to such a large population while keeping the costs optimum was a major challenge

Performance – Their existing system had limitations shifting to Cloud. The amount of time and manual effort required was immense

Data Governance – Maintaining user data security and privacy was of utmost importance for Amex GBT

The company was looking to protect and increase its market share by differentiating its core services and was seeking a resource to manage and drive their online travel program capabilities forward. Amex GBT decided to make a strategic investment in creating smart analytics around their booking software.

The solution equipped users to view their travel ROI by categorizing it into three categories cost, time and value. Each category has individual KPIs that are measured to evaluate the performance of a travel plan.

Reducing travel expenses by 30%

Time to Value – Initially it took a week for new users to be on-boarded onto the platform. With Premier Insights that time had now been reduced to a single day and the process had become much simpler and more effective.

Savings on Spends – The product notifies users of any available booking offers that can help them save on their expenditure. It recommends users of possible saving potential such as flight timings, date of the booking, date of travel, etc.

Adoption – Ease of use of the product, quick scale-up, real-time implementation of reports, and interactive dashboards of Premier Insights increased the global online adoption for Amex GBT

5) Airline Solutions Company: BI Accelerates Business Insights

Airline Solutions provides booking tools, revenue management, web, and mobile itinerary tools, as well as other technology, for airlines, hotels and other companies in the travel industry.

Challenge: The travel industry is remarkably dynamic and fast paced. And the airline solution provider’s clients needed advanced tools that could provide real-time data on customer behavior and actions.

They developed an enterprise travel data warehouse (ETDW) to hold its enormous amounts of data. The executive dashboards provide near real-time insights in user-friendly environments with a 360-degree overview of business health, reservations, operational performance and ticketing.

Results: The scalable infrastructure, graphic user interface, data aggregation and ability to work collaboratively have led to more revenue and increased client satisfaction.

6) A specialty US Retail Provider: Leveraging prescriptive analytics

Challenge/Objective: A specialty US Retail provider wanted to modernize its data platform which could help the business make real-time decisions while also leveraging prescriptive analytics. They wanted to discover true value of data being generated from its multiple systems and understand the patterns (both known and unknown) of sales, operations, and omni-channel retail performance.

We helped build a modern data solution that consolidated their data in a data lake and data warehouse, making it easier to extract the value in real-time. We integrated our solution with their OMS, CRM, Google Analytics, Salesforce, and inventory management system. The data was modeled in such a way that it could be fed into Machine Learning algorithms; so that we can leverage this easily in the future.

The customer had visibility into their data from day 1, which is something they had been wanting for some time. In addition to this, they were able to build more reports, dashboards, and charts to understand and interpret the data. In some cases, they were able to get real-time visibility and analysis on instore purchases based on geography!

7) Logistics startup with an objective to become the “Uber of the Trucking Sector” with the help of data analytics

Challenge: A startup specializing in analyzing vehicle and/or driver performance by collecting data from sensors within the vehicle (a.k.a. vehicle telemetry) and Order patterns with an objective to become the “Uber of the Trucking Sector”

Solution: We developed a customized backend of the client’s trucking platform so that they could monetize empty return trips of transporters by creating a marketplace for them. The approach used a combination of AWS Data Lake, AWS microservices, machine learning and analytics.

Reduced fuel costs
Optimized Reloads
More accurate driver / truck schedule planning
Smarter Routing
Fewer empty return trips
Deeper analysis of driver patterns, breaks, routes, etc.

8) Challenge/Objective: A niche segment customer competing against market behemoths looking to become a “Niche Segment Leader”

Solution: We developed a customized analytics platform that can ingest CRM, OMS, Ecommerce, and Inventory data and produce real time and batch driven analytics and AI platform. The approach used a combination of AWS microservices, machine learning and analytics.

Reduce Customer Churn
Optimized Order Fulfillment
More accurate demand schedule planning
Improve Product Recommendation
Improved Last Mile Delivery

How can we help you harness the power of data?

At Systems Plus our BI and analytics specialists help you leverage data to understand trends and derive insights by streamlining the searching, merging, and querying of data. From improving your CX and employee performance to predicting new revenue streams, our BI and analytics expertise helps you make data-driven decisions for saving costs and taking your growth to the next level.

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The Convergence Blog

The convergence - an online community space that's dedicated to empowering operators in the data industry by providing news and education about evergreen strategies, late-breaking data & ai developments, and free or low-cost upskilling resources that you need to thrive as a leader in the data & ai space., data analysis case study: learn from humana’s automated data analysis project.

Lillian Pierson, P.E.

Playback speed:

Got data? Great! Looking for that perfect data analysis case study to help you get started using it? You’re in the right place.

If you’ve ever struggled to decide what to do next with your data projects, to actually find meaning in the data, or even to decide what kind of data to collect, then KEEP READING…

Deep down, you know what needs to happen. You need to initiate and execute a data strategy that really moves the needle for your organization. One that produces seriously awesome business results.

But how you’re in the right place to find out..

As a data strategist who has worked with 10 percent of Fortune 100 companies, today I’m sharing with you a case study that demonstrates just how real businesses are making real wins with data analysis.

In the post below, we’ll look at:

A shining data success story;
What went on ‘under-the-hood’ to support that successful data project; and
The exact data technologies used by the vendor, to take this project from pure strategy to pure success

If you prefer to watch this information rather than read it, it’s captured in the video below:

Here’s the url too: https://youtu.be/xMwZObIqvLQ

3 Action Items You Need To Take

To actually use the data analysis case study you’re about to get – you need to take 3 main steps. Those are:

Reflect upon your organization as it is today (I left you some prompts below – to help you get started)
Review winning data case collections (starting with the one I’m sharing here) and identify 5 that seem the most promising for your organization given it’s current set-up
Assess your organization AND those 5 winning case collections. Based on that assessment, select the “QUICK WIN” data use case that offers your organization the most bang for it’s buck

Step 1: Reflect Upon Your Organization

Whenever you evaluate data case collections to decide if they’re a good fit for your organization, the first thing you need to do is organize your thoughts with respect to your organization as it is today.

Before moving into the data analysis case study, STOP and ANSWER THE FOLLOWING QUESTIONS – just to remind yourself:

What is the business vision for our organization?
What industries do we primarily support?
What data technologies do we already have up and running, that we could use to generate even more value?
What team members do we have to support a new data project? And what are their data skillsets like?
What type of data are we mostly looking to generate value from? Structured? Semi-Structured? Un-structured? Real-time data? Huge data sets? What are our data resources like?

Jot down some notes while you’re here. Then keep them in mind as you read on to find out how one company, Humana, used its data to achieve a 28 percent increase in customer satisfaction. Also include its 63 percent increase in employee engagement! (That’s such a seriously impressive outcome, right?!)

Step 2: Review Data Case Studies

Here we are, already at step 2. It’s time for you to start reviewing data analysis case studies (starting with the one I’m sharing below). I dentify 5 that seem the most promising for your organization given its current set-up.

Humana’s Automated Data Analysis Case Study

The key thing to note here is that the approach to creating a successful data program varies from industry to industry .

Let’s start with one to demonstrate the kind of value you can glean from these kinds of success stories.

Humana has provided health insurance to Americans for over 50 years. It is a service company focused on fulfilling the needs of its customers. A great deal of Humana’s success as a company rides on customer satisfaction, and the frontline of that battle for customers’ hearts and minds is Humana’s customer service center.

Call centers are hard to get right. A lot of emotions can arise during a customer service call, especially one relating to health and health insurance. Sometimes people are frustrated. At times, they’re upset. Also, there are times the customer service representative becomes aggravated, and the overall tone and progression of the phone call goes downhill. This is of course very bad for customer satisfaction.

Humana wanted to use artificial intelligence to improve customer satisfaction (and thus, customer retention rates & profits per customer).

Humana wanted to find a way to use artificial intelligence to monitor their phone calls and help their agents do a better job connecting with their customers in order to improve customer satisfaction (and thus, customer retention rates & profits per customer ).

In light of their business need, Humana worked with a company called Cogito, which specializes in voice analytics technology.

Cogito offers a piece of AI technology called Cogito Dialogue. It’s been trained to identify certain conversational cues as a way of helping call center representatives and supervisors stay actively engaged in a call with a customer.

The AI listens to cues like the customer’s voice pitch.

If it’s rising, or if the call representative and the customer talk over each other, then the dialogue tool will send out electronic alerts to the agent during the call.

Humana fed the dialogue tool customer service data from 10,000 calls and allowed it to analyze cues such as keywords, interruptions, and pauses, and these cues were then linked with specific outcomes. For example, if the representative is receiving a particular type of cues, they are likely to get a specific customer satisfaction result.

The Outcome

Customers were happier, and customer service representatives were more engaged..

This automated solution for data analysis has now been deployed in 200 Humana call centers and the company plans to roll it out to 100 percent of its centers in the future.

The initiative was so successful, Humana has been able to focus on next steps in its data program. The company now plans to begin predicting the type of calls that are likely to go unresolved, so they can send those calls over to management before they become frustrating to the customer and customer service representative alike.

What does this mean for you and your business?

Well, if you’re looking for new ways to generate value by improving the quantity and quality of the decision support that you’re providing to your customer service personnel, then this may be a perfect example of how you can do so.

Humana’s Business Use Cases

Humana’s data analysis case study includes two key business use cases:

Analyzing customer sentiment; and
Suggesting actions to customer service representatives.

Analyzing Customer Sentiment

First things first, before you go ahead and collect data, you need to ask yourself who and what is involved in making things happen within the business.

In the case of Humana, the actors were:

The health insurance system itself
The customer, and
The customer service representative

As you can see in the use case diagram above, the relational aspect is pretty simple. You have a customer service representative and a customer. They are both producing audio data, and that audio data is being fed into the system.

Humana focused on collecting the key data points, shown in the image below, from their customer service operations.

By collecting data about speech style, pitch, silence, stress in customers’ voices, length of call, speed of customers’ speech, intonation, articulation, silence, and representatives’ manner of speaking, Humana was able to analyze customer sentiment and introduce techniques for improved customer satisfaction.

Having strategically defined these data points, the Cogito technology was able to generate reports about customer sentiment during the calls.

Suggesting actions to customer service representatives.

The second use case for the Humana data program follows on from the data gathered in the first case.

In Humana’s case, Cogito generated a host of call analyses and reports about key call issues.

In the second business use case, Cogito was able to suggest actions to customer service representatives, in real-time , to make use of incoming data and help improve customer satisfaction on the spot.

The technology Humana used provided suggestions via text message to the customer service representative, offering the following types of feedback:

The tone of voice is too tense
The speed of speaking is high
The customer representative and customer are speaking at the same time

These alerts allowed the Humana customer service representatives to alter their approach immediately , improving the quality of the interaction and, subsequently, the customer satisfaction.

The preconditions for success in this use case were:

The call-related data must be collected and stored
The AI models must be in place to generate analysis on the data points that are recorded during the calls

Evidence of success can subsequently be found in a system that offers real-time suggestions for courses of action that the customer service representative can take to improve customer satisfaction.

Thanks to this data-intensive business use case, Humana was able to increase customer satisfaction, improve customer retention rates, and drive profits per customer.

The Technology That Supports This Data Analysis Case Study

I promised to dip into the tech side of things. This is especially for those of you who are interested in the ins and outs of how projects like this one are actually rolled out.

Here’s a little rundown of the main technologies we discovered when we investigated how Cogito runs in support of its clients like Humana.

For cloud data management Cogito uses AWS, specifically the Athena product
For on-premise big data management, the company used Apache HDFS – the distributed file system for storing big data
They utilize MapReduce, for processing their data
And Cogito also has traditional systems and relational database management systems such as PostgreSQL
In terms of analytics and data visualization tools, Cogito makes use of Tableau
And for its machine learning technology, these use cases required people with knowledge in Python, R, and SQL, as well as deep learning (Cogito uses the PyTorch library and the TensorFlow library)

These data science skill sets support the effective computing, deep learning , and natural language processing applications employed by Humana for this use case.

If you’re looking to hire people to help with your own data initiative, then people with those skills listed above, and with experience in these specific technologies, would be a huge help.

Step 3: S elect The “Quick Win” Data Use Case

Still there? Great!

It’s time to close the loop.

Remember those notes you took before you reviewed the study? I want you to STOP here and assess. Does this Humana case study seem applicable and promising as a solution, given your organization’s current set-up…

YES ▶ Excellent!

Earmark it and continue exploring other winning data use cases until you’ve identified 5 that seem like great fits for your businesses needs. Evaluate those against your organization’s needs, and select the very best fit to be your “quick win” data use case. Develop your data strategy around that.

NO , Lillian – It’s not applicable. ▶ No problem.

Discard the information and continue exploring the winning data use cases we’ve categorized for you according to business function and industry. Save time by dialing down into the business function you know your business really needs help with now. Identify 5 winning data use cases that seem like great fits for your businesses needs. Evaluate those against your organization’s needs, and select the very best fit to be your “quick win” data use case. Develop your data strategy around that data use case.

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Big Data Use Case: How Amazon uses Big Data to drive eCommerce revenue

Amazon is no stranger to big data. In this big data use case, we’ll look at how Amazon is leveraging data analytic technologies to improve products and services and drive overall revenue.

Big data has changed how we interact with the world and continue strengthening its hold on businesses worldwide. New data sets can be mined, managed, and analyzed using a combination of technologies.

These applications leverage the fallacy-prone human brain with computers. If you can think of applications for machine learning to predict things, optimize systems/processes, or automatically sequence tasks – this is relevant to big data.

Amazon’s algorithm is another secret to its success. The online shop has not only made it possible to order products with just one mouse click, but it also uses personalization data combined with big data to achieve excellent conversion rates.

On this page:

Amazon and Big data

Amazon’s big data strategy, amazon collection of data and its use, big data use case: the key points.

The fascinating world of Big Data can help you gain a competitive edge over your competitors. The data collected by networks of sensors, smart meters, and other means can provide insights into customer spending behavior and help retailers better target their services and products.

RELATED: Big Data Basics: Understanding Big Data

Machine Learning (a type of artificial intelligence) processes data through a learning algorithm to spot trends and patterns while continually refining the algorithms.

Amazon is one of the world’s largest businesses, estimated to have over 310 million active customers worldwide. They recently accomplished transactions that reached a value of $90 billion. This shows the popularity of online shopping on different continents. They provide services like payments, shipping, and new ideas for their customers.

Amazon is a giant – it has its own clouds. Amazon Web Services (AWS) offers individuals, companies, and governments cloud computing platforms . Amazon became interested in cloud computing after its Amazon Web Services was launched in 2003.

Amazon Web Services has expanded its business lines since then. Amazon hired some brilliant minds in the field of analytics and predictive modeling to aid in further data mining of Amazon’s massive volume of data that it has accumulated. Amazon innovates by introducing new products and strategies based on customer experience and feedback.

Big Data has assisted Amazon in ascending to the top of the e-commerce heap.

Amazon uses an anticipatory delivery model that predicts the products most likely to be purchased by its customers based on vast amounts of data.

This leads to Amazon assessing your purchase pattern and shipping things to your closest warehouse, which you may use in the future.

Amazon stores and processes as much customer and product information as possible – collecting specific information on every customer who visits its website. It also monitors the products a customer views, their shipping address, and whether or not they post reviews.

Amazon optimizes the prices on its websites by considering other factors, such as user activity, order history, rival prices, product availability, etc., providing discounts on popular items and earning a profit on less popular things using this strategy. This is how Amazon utilizes big data in its business operations.

Data science has established its preeminent place in industries and contributed to industries’ growth and improvement.

RELATED: How Artificial Intelligence Is Used for Data Analytics

Ever wonder how Amazon knows what you want before you even order it? The answer is mathematics, but you know that.

You may not know that the company has been running a data-gathering program for almost 15 years now that reaches back to the site’s earliest days.

In the quest to make every single interaction between buyers and sellers as efficient as possible, getting down to the most minute levels of detail has been essential, with data collection coming from a variety of sources – from sellers themselves and customers with apps on their phones – giving Amazon insights into every step along the way.

Voice recording by Alexa

Alexa is a speech interaction service developed by Amazon.com. It uses a cloud-based service to create voice-controlled smart devices. Through voice commands, Alexa can respond to queries, play music, read the news, and manage smart home devices such as lights and appliances.

Users may subscribe to an Alexa Voice Service (AVS) or use AWS Lambda to embed the system into other hardware and software.

You can spend all day with your microphone, smartphone, or barcode scanner recording every interaction, receipt, and voice note. But you don’t have to with tools like Amazon Echo.

With its always-on Alexa Voice Service, say what you need to add to your shopping list when you need it. It’s fast and straightforward.

Single click order

There is a big competition between companies using big data. Using big data, Amazon realized that customers might prefer alternative vendors if they experience a delay in their orders. So, Amazon has created Single click ordering.

You need to mention the address and payment method by this method. Every customer is given a time of 30 minutes to decide whether to place the order or not. After that, it is automatically determined.

Persuade Customers

Persuasive technology is a new area at Amazon. It’s an intersection of AI, UX, and the business goal of getting customers to take action at any point in the shopping journey.

One of the most significant ways Amazon utilizes data is through its recommendation engine. When a client searches for a specific item, Amazon can better anticipate other items the buyer may be interested in.

Consequently, Amazon can expedite the process of convincing a buyer to purchase the product. It is estimated that its personalized recommendation system accounts for 35 percent of the company’s annual sales.

The Amazon Assistant helps you discover new and exciting products, browse best sellers, and shop by department—there’s no place on the web with a better selection of stuff. Plus, it automatically notifies you when price drops or items you’ve been watching get marked down, so customers get the best deal possible.

Price dropping

Amazon constantly changes the price of its products by using Big data trends. On many competitor sites, the product’s price remains the same.

But Amazon has created another way to attract customers by constantly changing the price of the products. Amazon continually updates prices to deliver you the best deals.

Customers now check the site constantly that the price of the product they want can be low at any time, and they can buy it easily.

Shipping optimization

Shipping optimization by Amazon allows you to choose your preferred carrier, service options, and expected delivery time for millions of items on Amazon.com. With Shipping optimization by Amazon, you can end surprises like unexpected carrier selection, unnecessary service fees, or delays that can happen with even standard shipping.

Today, Amazon offers customers the choice to pick up their packages at over 400 U.S. locations. Whether you need one-day delivery or same-day pickup in select metro areas, Prime members can choose how fast they want to get their goods in an easy-to-use mobile app.

RELATED: Amazon Supply Chain: Understanding how Amazon’s supply chain works

Using shipping partners makes this selection possible, allowing Amazon to offer the most comprehensive selection in the industry and provide customers with multiple options for picking up their orders.

To better serve the customer, Amazon has adopted a technology that allows them to receive information from shoppers’ web browsing habits and use it to improve existing products and introduce new ones.

Amazon is only one example of a corporation that uses big data. Airbnb is another industry leader that employs big data in its operations; you can also review their case study. Below are four ways big data plays a significant role in every organization.

1. Helps you understand the market condition: Big Data assists you in comprehending market circumstances, trends, and wants, as well as your competitors, through data analysis.

It helps you to research customer interests and behaviors so that you may adjust your products and services to their requirements.

2. It helps you increase customer satisfaction: Using big data analytics, you may determine the demographics of your target audience, the products and services they want, and much more.

This information enables you to design business plans and strategies with the needs and demands of customers in mind. Customer satisfaction will grow immediately if your business strategy is based on consumer requirements.

3. Increase sales: Once you thoroughly understand the market environment and client needs, you can develop products, services, and marketing tactics accordingly. This helps you dramatically enhance your sales.

4. Optimize costs: By analyzing the data acquired from client databases, services, and internet resources, you may determine what prices benefit customers, how cost increases or decreases will impact your business, etc.

You can determine the optimal price for your items and services, which will benefit your customers and your company.

Businesses need to adapt to the ever-changing needs of their customers. Within this dynamic online marketplace, competitive advantage is often gained by those players who can adapt to market changes faster than others. Big data analytics provides that advantage.

RELATED: Top 5 Big Data Privacy Issues Businesses Must Consider

However, the sheer volume of data generated at all levels — from individual consumer click streams to the aggregate public opinions of millions of individuals — provides a considerable barrier to companies that would like to customize their offerings or efficiently interact with customers.

James joined BusinessTechWeekly.com in 2018, following a 19-year career in IT where he covered a wide range of support, management and consultancy roles across a wide variety of industry sectors. He has a broad technical knowledge base backed with an impressive list of technical certifications. with a focus on applications, cloud and infrastructure.

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Case studies & examples

Articles, use cases, and proof points describing projects undertaken by data managers and data practitioners across the federal government

Agencies Mobilize to Improve Emergency Response in Puerto Rico through Better Data

Federal agencies' response efforts to Hurricanes Irma and Maria in Puerto Rico was hampered by imperfect address data for the island. In the aftermath, emergency responders gathered together to enhance the utility of Puerto Rico address data and share best practices for using what information is currently available.

Federal Data Strategy

BUILDER: A Science-Based Approach to Infrastructure Management

The Department of Energy’s National Nuclear Security Administration (NNSA) adopted a data-driven, risk-informed strategy to better assess risks, prioritize investments, and cost effectively modernize its aging nuclear infrastructure. NNSA’s new strategy, and lessons learned during its implementation, will help inform other federal data practitioners’ efforts to maintain facility-level information while enabling accurate and timely enterprise-wide infrastructure analysis.

Department of Energy

data management , data analysis , process redesign , Federal Data Strategy

Business case for open data

Six reasons why making your agency's data open and accessible is a good business decision.

CDO Council Federal HR Dashboarding Report - 2021

The CDO Council worked with the US Department of Agriculture, the Department of the Treasury, the United States Agency for International Development, and the Department of Transportation to develop a Diversity Profile Dashboard and to explore the value of shared HR decision support across agencies. The pilot was a success, and identified potential impact of a standardized suite of HR dashboards, in addition to demonstrating the value of collaborative analytics between agencies.

Federal Chief Data Officer's Council

data practices , data sharing , data access

CDOC Data Inventory Report

The Chief Data Officers Council Data Inventory Working Group developed this paper to highlight the value proposition for data inventories and describe challenges agencies may face when implementing and managing comprehensive data inventories. It identifies opportunities agencies can take to overcome some of these challenges and includes a set of recommendations directed at Agencies, OMB, and the CDO Council (CDOC).

data practices , metadata , data inventory

DSWG Recommendations and Findings

The Chief Data Officer Council (CDOC) established a Data Sharing Working Group (DSWG) to help the council understand the varied data-sharing needs and challenges of all agencies across the Federal Government. The DSWG reviewed data-sharing across federal agencies and developed a set of recommendations for improving the methods to access and share data within and between agencies. This report presents the findings of the DSWG’s review and provides recommendations to the CDOC Executive Committee.

data practices , data agreements , data sharing , data access

Data Skills Training Program Implementation Toolkit

The Data Skills Training Program Implementation Toolkit is designed to provide both small and large agencies with information to develop their own data skills training programs. The information provided will serve as a roadmap to the design, implementation, and administration of federal data skills training programs as agencies address their Federal Data Strategy’s Agency Action 4 gap-closing strategy training component.

data sharing , Federal Data Strategy

Data Standdown: Interrupting process to fix information

Although not a true pause in operations, ONR’s data standdown made data quality and data consolidation the top priority for the entire organization. It aimed to establish an automated and repeatable solution to enable a more holistic view of ONR investments and activities, and to increase transparency and effectiveness throughout its mission support functions. In addition, it demonstrated that getting top-level buy-in from management to prioritize data can truly advance a more data-driven culture.

Office of Naval Research

data governance , data cleaning , process redesign , Federal Data Strategy

Data.gov Metadata Management Services Product-Preliminary Plan

Status summary and preliminary business plan for a potential metadata management product under development by the Data.gov Program Management Office

data management , Federal Data Strategy , metadata , open data

PDF (7 pages)

Department of Transportation Case Study: Enterprise Data Inventory

In response to the Open Government Directive, DOT developed a strategic action plan to inventory and release high-value information through the Data.gov portal. The Department sustained efforts in building its data inventory, responding to the President’s memorandum on regulatory compliance with a comprehensive plan that was recognized as a model for other agencies to follow.

Department of Transportation

data inventory , open data

Department of Transportation Model Data Inventory Approach

This document from the Department of Transportation provides a model plan for conducting data inventory efforts required under OMB Memorandum M-13-13.

data inventory

PDF (5 pages)

FEMA Case Study: Disaster Assistance Program Coordination

In 2008, the Disaster Assistance Improvement Program (DAIP), an E-Government initiative led by FEMA with support from 16 U.S. Government partners, launched DisasterAssistance.gov to simplify the process for disaster survivors to identify and apply for disaster assistance. DAIP utilized existing partner technologies and implemented a services oriented architecture (SOA) that integrated the content management system and rules engine supporting Department of Labor’s Benefits.gov applications with FEMA’s Individual Assistance Center application. The FEMA SOA serves as the backbone for data sharing interfaces with three of DAIP’s federal partners and transfers application data to reduce duplicate data entry by disaster survivors.

Federal Emergency Management Agency

data sharing

Federal CDO Data Skills Training Program Case Studies

This series was developed by the Chief Data Officer Council’s Data Skills & Workforce Development Working Group to provide support to agencies in implementing the Federal Data Strategy’s Agency Action 4 gap-closing strategy training component in FY21.

FederalRegister.gov API Case Study

This case study describes the tenets behind an API that provides access to all data found on FederalRegister.gov, including all Federal Register documents from 1994 to the present.

National Archives and Records Administration

PDF (3 pages)

Fuels Knowledge Graph Project

The Fuels Knowledge Graph Project (FKGP), funded through the Federal Chief Data Officers (CDO) Council, explored the use of knowledge graphs to achieve more consistent and reliable fuel management performance measures. The team hypothesized that better performance measures and an interoperable semantic framework could enhance the ability to understand wildfires and, ultimately, improve outcomes. To develop a more systematic and robust characterization of program outcomes, the FKGP team compiled, reviewed, and analyzed multiple agency glossaries and data sources. The team examined the relationships between them, while documenting the data management necessary for a successful fuels management program.

metadata , data sharing , data access

Government Data Hubs

A list of Federal agency open data hubs, including USDA, HHS, NASA, and many others.

Helping Baltimore Volunteers Find Where to Help

Bloomberg Government analysts put together a prototype through the Census Bureau’s Opportunity Project to better assess where volunteers should direct litter-clearing efforts. Using Census Bureau and Forest Service information, the team brought a data-driven approach to their work. Their experience reveals how individuals with data expertise can identify a real-world problem that data can help solve, navigate across agencies to find and obtain the most useful data, and work within resource constraints to provide a tool to help address the problem.

Census Bureau

geospatial , data sharing , Federal Data Strategy

How USDA Linked Federal and Commercial Data to Shed Light on the Nutritional Value of Retail Food Sales

Purchase-to-Plate Crosswalk (PPC) links the more than 359,000 food products in a comercial company database to several thousand foods in a series of USDA nutrition databases. By linking existing data resources, USDA was able to enrich and expand the analysis capabilities of both datasets. Since there were no common identifiers between the two data structures, the team used probabilistic and semantic methods to reduce the manual effort required to link the data.

Department of Agriculture

data sharing , process redesign , Federal Data Strategy

How to Blend Your Data: BEA and BLS Harness Big Data to Gain New Insights about Foreign Direct Investment in the U.S.

A recent collaboration between the Bureau of Economic Analysis (BEA) and the Bureau of Labor Statistics (BLS) helps shed light on the segment of the American workforce employed by foreign multinational companies. This case study shows the opportunities of cross-agency data collaboration, as well as some of the challenges of using big data and administrative data in the federal government.

Bureau of Economic Analysis / Bureau of Labor Statistics

data sharing , workforce development , process redesign , Federal Data Strategy

Implementing Federal-Wide Comment Analysis Tools

The CDO Council Comment Analysis pilot has shown that recent advances in Natural Language Processing (NLP) can effectively aid the regulatory comment analysis process. The proof-ofconcept is a standardized toolset intended to support agencies and staff in reviewing and responding to the millions of public comments received each year across government.

Improving Data Access and Data Management: Artificial Intelligence-Generated Metadata Tags at NASA

NASA’s data scientists and research content managers recently built an automated tagging system using machine learning and natural language processing. This system serves as an example of how other agencies can use their own unstructured data to improve information accessibility and promote data reuse.

National Aeronautics and Space Administration

metadata , data management , data sharing , process redesign , Federal Data Strategy

Investing in Learning with the Data Stewardship Tactical Working Group at DHS

The Department of Homeland Security (DHS) experience forming the Data Stewardship Tactical Working Group (DSTWG) provides meaningful insights for those who want to address data-related challenges collaboratively and successfully in their own agencies.

Department of Homeland Security

data governance , data management , Federal Data Strategy

Leveraging AI for Business Process Automation at NIH

The National Institute of General Medical Sciences (NIGMS), one of the twenty-seven institutes and centers at the NIH, recently deployed Natural Language Processing (NLP) and Machine Learning (ML) to automate the process by which it receives and internally refers grant applications. This new approach ensures efficient and consistent grant application referral, and liberates Program Managers from the labor-intensive and monotonous referral process.

National Institutes of Health

standards , data cleaning , process redesign , AI

FDS Proof Point

National Broadband Map: A Case Study on Open Innovation for National Policy

The National Broadband Map is a tool that provide consumers nationwide reliable information on broadband internet connections. This case study describes how crowd-sourcing, open source software, and public engagement informs the development of a tool that promotes government transparency.

Federal Communications Commission

National Renewable Energy Laboratory API Case Study

This case study describes the launch of the National Renewable Energy Laboratory (NREL) Developer Network in October 2011. The main goal was to build an overarching platform to make it easier for the public to use NREL APIs and for NREL to produce APIs.

National Renewable Energy Laboratory

Open Energy Data at DOE

This case study details the development of the renewable energy applications built on the Open Energy Information (OpenEI) platform, sponsored by the Department of Energy (DOE) and implemented by the National Renewable Energy Laboratory (NREL).

open data , data sharing , Federal Data Strategy

Pairing Government Data with Private-Sector Ingenuity to Take on Unwanted Calls

The Federal Trade Commission (FTC) releases data from millions of consumer complaints about unwanted calls to help fuel a myriad of private-sector solutions to tackle the problem. The FTC’s work serves as an example of how agencies can work with the private sector to encourage the innovative use of government data toward solutions that benefit the public.

Federal Trade Commission

data cleaning , Federal Data Strategy , open data , data sharing

Profile in Data Sharing - National Electronic Interstate Compact Enterprise

The Federal CDO Council’s Data Sharing Working Group highlights successful data sharing activities to recognize mature data sharing practices as well as to incentivize and inspire others to take part in similar collaborations. This Profile in Data Sharing focuses on how the federal government and states support children who are being placed for adoption or foster care across state lines. It greatly reduces the work and time required for states to exchange paperwork and information needed to process the placements. Additionally, NEICE allows child welfare workers to communicate and provide timely updates to courts, relevant private service providers, and families.

Profile in Data Sharing - National Health Service Corps Loan Repayment Programs

The Federal CDO Council’s Data Sharing Working Group highlights successful data sharing activities to recognize mature data sharing practices as well as to incentivize and inspire others to take part in similar collaborations. This Profile in Data Sharing focuses on how the Health Resources and Services Administration collaborates with the Department of Education to make it easier to apply to serve medically underserved communities - reducing applicant burden and improving processing efficiency.

Profile in Data Sharing - Roadside Inspection Data

The Federal CDO Council’s Data Sharing Working Group highlights successful data sharing activities to recognize mature data sharing practices as well as to incentivize and inspire others to take part in similar collaborations. This Profile in Data Sharing focuses on how the Department of Transportation collaborates with the Customs and Border Patrol and state partners to prescreen commercial motor vehicles entering the US and to focus inspections on unsafe carriers and drivers.

Profiles in Data Sharing - U.S. Citizenship and Immigration Service

The Federal CDO Council’s Data Sharing Working Group highlights successful data sharing activities to recognize mature data sharing practices as well as to incentivize and inspire others to take part in similar collaborations. This Profile in Data Sharing focuses on how the U.S. Citizenship and Immigration Service (USCIS) collaborated with the Centers for Disease Control to notify state, local, tribal, and territorial public health authorities so they can connect with individuals in their communities about their potential exposure.

SBA’s Approach to Identifying Data, Using a Learning Agenda, and Leveraging Partnerships to Build its Evidence Base

Through its Enterprise Learning Agenda, Small Business Administration’s (SBA) staff identify essential research questions, a plan to answer them, and how data held outside the agency can help provide further insights. Other agencies can learn from the innovative ways SBA identifies data to answer agency strategic questions and adopt those aspects that work for their own needs.

Small Business Administration

process redesign , Federal Data Strategy

Supercharging Data through Validation as a Service

USDA's Food and Nutrition Service restructured its approach to data validation at the state level using an open-source, API-based validation service managed at the federal level.

data cleaning , data validation , API , data sharing , process redesign , Federal Data Strategy

The Census Bureau Uses Its Own Data to Increase Response Rates, Helps Communities and Other Stakeholders Do the Same

The Census Bureau team produced a new interactive mapping tool in early 2018 called the Response Outreach Area Mapper (ROAM), an application that resulted in wider use of authoritative Census Bureau data, not only to improve the Census Bureau’s own operational efficiency, but also for use by tribal, state, and local governments, national and local partners, and other community groups. Other agency data practitioners can learn from the Census Bureau team’s experience communicating technical needs to non-technical executives, building analysis tools with widely-used software, and integrating efforts with stakeholders and users.

open data , data sharing , data management , data analysis , Federal Data Strategy

The Mapping Medicare Disparities Tool

The Centers for Medicare & Medicaid Services’ Office of Minority Health (CMS OMH) Mapping Medicare Disparities Tool harnessed the power of millions of data records while protecting the privacy of individuals, creating an easy-to-use tool to better understand health disparities.

Centers for Medicare & Medicaid Services

geospatial , Federal Data Strategy , open data

The Veterans Legacy Memorial

The Veterans Legacy Memorial (VLM) is a digital platform to help families, survivors, and fellow veterans to take a leading role in honoring their beloved veteran. Built on millions of existing National Cemetery Administration (NCA) records in a 25-year-old database, VLM is a powerful example of an agency harnessing the potential of a legacy system to provide a modernized service that better serves the public.

Veterans Administration

data sharing , data visualization , Federal Data Strategy

Transitioning to a Data Driven Culture at CMS

This case study describes how CMS announced the creation of the Office of Information Products and Data Analytics (OIPDA) to take the lead in making data use and dissemination a core function of the agency.

data management , data sharing , data analysis , data analytics

PDF (10 pages)

U.S. Department of Labor Case Study: Software Development Kits

The U.S. Department of Labor sought to go beyond merely making data available to developers and take ease of use of the data to the next level by giving developers tools that would make using DOL’s data easier. DOL created software development kits (SDKs), which are downloadable code packages that developers can drop into their apps, making access to DOL’s data easy for even the most novice developer. These SDKs have even been published as open source projects with the aim of speeding up their conversion to SDKs that will eventually support all federal APIs.

Department of Labor

open data , API

U.S. Geological Survey and U.S. Census Bureau collaborate on national roads and boundaries data

It is a well-kept secret that the U.S. Geological Survey and the U.S. Census Bureau were the original two federal agencies to build the first national digital database of roads and boundaries in the United States. The agencies joined forces to develop homegrown computer software and state of the art technologies to convert existing USGS topographic maps of the nation to the points, lines, and polygons that fueled early GIS. Today, the USGS and Census Bureau have a longstanding goal to leverage and use roads and authoritative boundary datasets.

U.S. Geological Survey and U.S. Census Bureau

data management , data sharing , data standards , data validation , data visualization , Federal Data Strategy , geospatial , open data , quality

USA.gov Uses Human-Centered Design to Roll Out AI Chatbot

To improve customer service and give better answers to users of the USA.gov website, the Technology Transformation and Services team at General Services Administration (GSA) created a chatbot using artificial intelligence (AI) and automation.

General Services Administration

AI , Federal Data Strategy

resources.data.gov

An official website of the Office of Management and Budget, the General Services Administration, and the Office of Government Information Services.

This section contains explanations of common terms referenced on resources.data.gov.

Data Analytics Case Study Guide (Updated for 2024)

What are data analytics case study interviews.

When you’re trying to land a data analyst job, the last thing to stand in your way is the data analytics case study interview.

One reason they’re so challenging is that case studies don’t typically have a right or wrong answer.

Instead, case study interviews require you to come up with a hypothesis for an analytics question and then produce data to support or validate your hypothesis. In other words, it’s not just about your technical skills; you’re also being tested on creative problem-solving and your ability to communicate with stakeholders.

This article provides an overview of how to answer data analytics case study interview questions. You can find an in-depth course in the data analytics learning path .

How to Solve Data Analytics Case Questions

Check out our video below on How to solve a Data Analytics case study problem:

With data analyst case questions, you will need to answer two key questions:

What metrics should I propose?
How do I write a SQL query to get the metrics I need?

In short, to ace a data analytics case interview, you not only need to brush up on case questions, but you also should be adept at writing all types of SQL queries and have strong data sense.

These questions are especially challenging to answer if you don’t have a framework or know how to answer them. To help you prepare, we created this step-by-step guide to answering data analytics case questions.

We show you how to use a framework to answer case questions, provide example analytics questions, and help you understand the difference between analytics case studies and product metrics case studies .

Data Analytics Cases vs Product Metrics Questions

Product case questions sometimes get lumped in with data analytics cases.

Ultimately, the type of case question you are asked will depend on the role. For example, product analysts will likely face more product-oriented questions.

Product metrics cases tend to focus on a hypothetical situation. You might be asked to:

Investigate Metrics - One of the most common types will ask you to investigate a metric, usually one that’s going up or down. For example, “Why are Facebook friend requests falling by 10 percent?”

Measure Product/Feature Success - A lot of analytics cases revolve around the measurement of product success and feature changes. For example, “We want to add X feature to product Y. What metrics would you track to make sure that’s a good idea?”

With product data cases, the key difference is that you may or may not be required to write the SQL query to find the metric.

Instead, these interviews are more theoretical and are designed to assess your product sense and ability to think about analytics problems from a product perspective. Product metrics questions may also show up in the data analyst interview , but likely only for product data analyst roles.

Data Analytics Case Study Question: Sample Solution

Data Analytics Case Study Sample Solution

Let’s start with an example data analytics case question :

You’re given a table that represents search results from searches on Facebook. The query column is the search term, the position column represents each position the search result came in, and the rating column represents the human rating from 1 to 5, where 5 is high relevance, and 1 is low relevance.

Each row in the search_events table represents a single search, with the has_clicked column representing if a user clicked on a result or not. We have a hypothesis that the CTR is dependent on the search result rating.

Write a query to return data to support or disprove this hypothesis.

search_results table:

search_events table

Step 1: With Data Analytics Case Studies, Start by Making Assumptions

Hint: Start by making assumptions and thinking out loud. With this question, focus on coming up with a metric to support the hypothesis. If the question is unclear or if you think you need more information, be sure to ask.

Answer. The hypothesis is that CTR is dependent on search result rating. Therefore, we want to focus on the CTR metric, and we can assume:

If CTR is high when search result ratings are high, and CTR is low when the search result ratings are low, then the hypothesis is correct.
If CTR is low when the search ratings are high, or there is no proven correlation between the two, then our hypothesis is not proven.

Step 2: Provide a Solution for the Case Question

Hint: Walk the interviewer through your reasoning. Talking about the decisions you make and why you’re making them shows off your problem-solving approach.

Answer. One way we can investigate the hypothesis is to look at the results split into different search rating buckets. For example, if we measure the CTR for results rated at 1, then those rated at 2, and so on, we can identify if an increase in rating is correlated with an increase in CTR.

First, I’d write a query to get the number of results for each query in each bucket. We want to look at the distribution of results that are less than a rating threshold, which will help us see the relationship between search rating and CTR.

This CTE aggregates the number of results that are less than a certain rating threshold. Later, we can use this to see the percentage that are in each bucket. If we re-join to the search_events table, we can calculate the CTR by then grouping by each bucket.

Step 3: Use Analysis to Backup Your Solution

Hint: Be prepared to justify your solution. Interviewers will follow up with questions about your reasoning, and ask why you make certain assumptions.

Answer. By using the CASE WHEN statement, I calculated each ratings bucket by checking to see if all the search results were less than 1, 2, or 3 by subtracting the total from the number within the bucket and seeing if it equates to 0.

I did that to get away from averages in our bucketing system. Outliers would make it more difficult to measure the effect of bad ratings. For example, if a query had a 1 rating and another had a 5 rating, that would equate to an average of 3. Whereas in my solution, a query with all of the results under 1, 2, or 3 lets us know that it actually has bad ratings.

Product Data Case Question: Sample Solution

In product metrics interviews, you’ll likely be asked about analytics, but the discussion will be more theoretical. You’ll propose a solution to a problem, and supply the metrics you’ll use to investigate or solve it. You may or may not be required to write a SQL query to get those metrics.

We’ll start with an example product metrics case study question :

Let’s say you work for a social media company that has just done a launch in a new city. Looking at weekly metrics, you see a slow decrease in the average number of comments per user from January to March in this city.

The company has been consistently growing new users in the city from January to March.

What are some reasons why the average number of comments per user would be decreasing and what metrics would you look into?

Step 1: Ask Clarifying Questions Specific to the Case

Hint: This question is very vague. It’s all hypothetical, so we don’t know very much about users, what the product is, and how people might be interacting. Be sure you ask questions upfront about the product.

Answer: Before I jump into an answer, I’d like to ask a few questions:

Who uses this social network? How do they interact with each other?
Has there been any performance issues that might be causing the problem?
What are the goals of this particular launch?
Has there been any changes to the comment features in recent weeks?

For the sake of this example, let’s say we learn that it’s a social network similar to Facebook with a young audience, and the goals of the launch are to grow the user base. Also, there have been no performance issues and the commenting feature hasn’t been changed since launch.

Step 2: Use the Case Question to Make Assumptions

Hint: Look for clues in the question. For example, this case gives you a metric, “average number of comments per user.” Consider if the clue might be helpful in your solution. But be careful, sometimes questions are designed to throw you off track.

Answer: From the question, we can hypothesize a little bit. For example, we know that user count is increasing linearly. That means two things:

The decreasing comments issue isn’t a result of a declining user base.
The cause isn’t loss of platform.

We can also model out the data to help us get a better picture of the average number of comments per user metric:

January: 10000 users, 30000 comments, 3 comments/user
February: 20000 users, 50000 comments, 2.5 comments/user
March: 30000 users, 60000 comments, 2 comments/user

One thing to note: Although this is an interesting metric, I’m not sure if it will help us solve this question. For one, average comments per user doesn’t account for churn. We might assume that during the three-month period users are churning off the platform. Let’s say the churn rate is 25% in January, 20% in February and 15% in March.

Step 3: Make a Hypothesis About the Data

Hint: Don’t worry too much about making a correct hypothesis. Instead, interviewers want to get a sense of your product initiation and that you’re on the right track. Also, be prepared to measure your hypothesis.

Answer. I would say that average comments per user isn’t a great metric to use, because it doesn’t reveal insights into what’s really causing this issue.

That’s because it doesn’t account for active users, which are the users who are actually commenting. A better metric to investigate would be retained users and monthly active users.

What I suspect is causing the issue is that active users are commenting frequently and are responsible for the increase in comments month-to-month. New users, on the other hand, aren’t as engaged and aren’t commenting as often.

Step 4: Provide Metrics and Data Analysis

Hint: Within your solution, include key metrics that you’d like to investigate that will help you measure success.

Answer: I’d say there are a few ways we could investigate the cause of this problem, but the one I’d be most interested in would be the engagement of monthly active users.

If the growth in comments is coming from active users, that would help us understand how we’re doing at retaining users. Plus, it will also show if new users are less engaged and commenting less frequently.

One way that we could dig into this would be to segment users by their onboarding date, which would help us to visualize engagement and see how engaged some of our longest-retained users are.

If engagement of new users is the issue, that will give us some options in terms of strategies for addressing the problem. For example, we could test new onboarding or commenting features designed to generate engagement.

Step 5: Propose a Solution for the Case Question

Hint: In the majority of cases, your initial assumptions might be incorrect, or the interviewer might throw you a curveball. Be prepared to make new hypotheses or discuss the pitfalls of your analysis.

Answer. If the cause wasn’t due to a lack of engagement among new users, then I’d want to investigate active users. One potential cause would be active users commenting less. In that case, we’d know that our earliest users were churning out, and that engagement among new users was potentially growing.

Again, I think we’d want to focus on user engagement since the onboarding date. That would help us understand if we were seeing higher levels of churn among active users, and we could start to identify some solutions there.

Tip: Use a Framework to Solve Data Analytics Case Questions

Analytics case questions can be challenging, but they’re much more challenging if you don’t use a framework. Without a framework, it’s easier to get lost in your answer, to get stuck, and really lose the confidence of your interviewer. Find helpful frameworks for data analytics questions in our data analytics learning path and our product metrics learning path .

Once you have the framework down, what’s the best way to practice? Mock interviews with our coaches are very effective, as you’ll get feedback and helpful tips as you answer. You can also learn a lot by practicing P2P mock interviews with other Interview Query students. No data analytics background? Check out how to become a data analyst without a degree .

Finally, if you’re looking for sample data analytics case questions and other types of interview questions, see our guide on the top data analyst interview questions .

Data Analytics Case Study Guide 2024

by Sam McKay, CFA | Data Analytics

Data analytics case studies reveal how businesses harness data for informed decisions and growth.

For aspiring data professionals, mastering the case study process will enhance your skills and increase your career prospects.

So, how do you approach a case study?

Use these steps to process a data analytics case study:

Understand the Problem: Grasp the core problem or question addressed in the case study.

Collect Relevant Data: Gather data from diverse sources, ensuring accuracy and completeness.

Apply Analytical Techniques: Use appropriate methods aligned with the problem statement.

Visualize Insights: Utilize visual aids to showcase patterns and key findings.

Derive Actionable Insights: Focus on deriving meaningful actions from the analysis.

This article will give you detailed steps to navigate a case study effectively and understand how it works in real-world situations.

By the end of the article, you will be better equipped to approach a data analytics case study, strengthening your analytical prowess and practical application skills.

Let’s dive in!

Table of Contents

What is a Data Analytics Case Study?

A data analytics case study is a real or hypothetical scenario where analytics techniques are applied to solve a specific problem or explore a particular question.

It’s a practical approach that uses data analytics methods, assisting in deciphering data for meaningful insights. This structured method helps individuals or organizations make sense of data effectively.

Additionally, it’s a way to learn by doing, where there’s no single right or wrong answer in how you analyze the data.

So, what are the components of a case study?

Key Components of a Data Analytics Case Study

A data analytics case study comprises essential elements that structure the analytical journey:

Problem Context: A case study begins with a defined problem or question. It provides the context for the data analysis , setting the stage for exploration and investigation.

Data Collection and Sources: It involves gathering relevant data from various sources , ensuring data accuracy, completeness, and relevance to the problem at hand.

Analysis Techniques: Case studies employ different analytical methods, such as statistical analysis, machine learning algorithms, or visualization tools, to derive meaningful conclusions from the collected data.

Insights and Recommendations: The ultimate goal is to extract actionable insights from the analyzed data, offering recommendations or solutions that address the initial problem or question.

Now that you have a better understanding of what a data analytics case study is, let’s talk about why we need and use them.

Why Case Studies are Integral to Data Analytics

Case studies serve as invaluable tools in the realm of data analytics, offering multifaceted benefits that bolster an analyst’s proficiency and impact:

Real-Life Insights and Skill Enhancement: Examining case studies provides practical, real-life examples that expand knowledge and refine skills. These examples offer insights into diverse scenarios, aiding in a data analyst’s growth and expertise development.

Validation and Refinement of Analyses: Case studies demonstrate the effectiveness of data-driven decisions across industries, providing validation for analytical approaches. They showcase how organizations benefit from data analytics. Also, this helps in refining one’s own methodologies

Showcasing Data Impact on Business Outcomes: These studies show how data analytics directly affects business results, like increasing revenue, reducing costs, or delivering other measurable advantages. Understanding these impacts helps articulate the value of data analytics to stakeholders and decision-makers.

Learning from Successes and Failures: By exploring a case study, analysts glean insights from others’ successes and failures, acquiring new strategies and best practices. This learning experience facilitates professional growth and the adoption of innovative approaches within their own data analytics work.

Including case studies in a data analyst’s toolkit helps gain more knowledge, improve skills, and understand how data analytics affects different industries.

Using these real-life examples boosts confidence and success, guiding analysts to make better and more impactful decisions in their organizations.

But not all case studies are the same.

Let’s talk about the different types.

Types of Data Analytics Case Studies

Data analytics encompasses various approaches tailored to different analytical goals:

Exploratory Case Study: These involve delving into new datasets to uncover hidden patterns and relationships, often without a predefined hypothesis. They aim to gain insights and generate hypotheses for further investigation.

Predictive Case Study: These utilize historical data to forecast future trends, behaviors, or outcomes. By applying predictive models, they help anticipate potential scenarios or developments.

Diagnostic Case Study: This type focuses on understanding the root causes or reasons behind specific events or trends observed in the data. It digs deep into the data to provide explanations for occurrences.

Prescriptive Case Study: This case study goes beyond analytics; it provides actionable recommendations or strategies derived from the analyzed data. They guide decision-making processes by suggesting optimal courses of action based on insights gained.

Each type has a specific role in using data to find important insights, helping in decision-making, and solving problems in various situations.

Regardless of the type of case study you encounter, here are some steps to help you process them.

Roadmap to Handling a Data Analysis Case Study

Embarking on a data analytics case study requires a systematic approach, step-by-step, to derive valuable insights effectively.

Here are the steps to help you through the process:

Step 1: Understanding the Case Study Context: Immerse yourself in the intricacies of the case study. Delve into the industry context, understanding its nuances, challenges, and opportunities.

Identify the central problem or question the study aims to address. Clarify the objectives and expected outcomes, ensuring a clear understanding before diving into data analytics.

Step 2: Data Collection and Validation: Gather data from diverse sources relevant to the case study. Prioritize accuracy, completeness, and reliability during data collection. Conduct thorough validation processes to rectify inconsistencies, ensuring high-quality and trustworthy data for subsequent analysis.

Data Collection and Validation in case study

Step 3: Problem Definition and Scope: Define the problem statement precisely. Articulate the objectives and limitations that shape the scope of your analysis. Identify influential variables and constraints, providing a focused framework to guide your exploration.

Step 4: Exploratory Data Analysis (EDA): Leverage exploratory techniques to gain initial insights. Visualize data distributions, patterns, and correlations, fostering a deeper understanding of the dataset. These explorations serve as a foundation for more nuanced analysis.

Step 5: Data Preprocessing and Transformation: Cleanse and preprocess the data to eliminate noise, handle missing values, and ensure consistency. Transform data formats or scales as required, preparing the dataset for further analysis.

Data Preprocessing and Transformation in case study

Step 6: Data Modeling and Method Selection: Select analytical models aligning with the case study’s problem, employing statistical techniques, machine learning algorithms, or tailored predictive models.

In this phase, it’s important to develop data modeling skills. This helps create visuals of complex systems using organized data, which helps solve business problems more effectively.

Understand key data modeling concepts, utilize essential tools like SQL for database interaction, and practice building models from real-world scenarios.

Furthermore, strengthen data cleaning skills for accurate datasets, and stay updated with industry trends to ensure relevance.

Data Modeling and Method Selection in case study

Step 7: Model Evaluation and Refinement: Evaluate the performance of applied models rigorously. Iterate and refine models to enhance accuracy and reliability, ensuring alignment with the objectives and expected outcomes.

Step 8: Deriving Insights and Recommendations: Extract actionable insights from the analyzed data. Develop well-structured recommendations or solutions based on the insights uncovered, addressing the core problem or question effectively.

Step 9: Communicating Results Effectively: Present findings, insights, and recommendations clearly and concisely. Utilize visualizations and storytelling techniques to convey complex information compellingly, ensuring comprehension by stakeholders.

Step 10: Reflection and Iteration: Reflect on the entire analysis process and outcomes. Identify potential improvements and lessons learned. Embrace an iterative approach, refining methodologies for continuous enhancement and future analyses.

This step-by-step roadmap provides a structured framework for thorough and effective handling of a data analytics case study.

Now, after handling data analytics comes a crucial step; presenting the case study.

Presenting Your Data Analytics Case Study

Presenting a data analytics case study is a vital part of the process. When presenting your case study, clarity and organization are paramount.

To achieve this, follow these key steps:

Structuring Your Case Study: Start by outlining relevant and accurate main points. Ensure these points align with the problem addressed and the methodologies used in your analysis.

Crafting a Narrative with Data: Start with a brief overview of the issue, then explain your method and steps, covering data collection, cleaning, stats, and advanced modeling.

Visual Representation for Clarity: Utilize various visual aids—tables, graphs, and charts—to illustrate patterns, trends, and insights. Ensure these visuals are easy to comprehend and seamlessly support your narrative.

Highlighting Key Information: Use bullet points to emphasize essential information, maintaining clarity and allowing the audience to grasp key takeaways effortlessly. Bold key terms or phrases to draw attention and reinforce important points.

Addressing Audience Queries: Anticipate and be ready to answer audience questions regarding methods, assumptions, and results. Demonstrating a profound understanding of your analysis instills confidence in your work.

Integrity and Confidence in Delivery: Maintain a neutral tone and avoid exaggerated claims about findings. Present your case study with integrity, clarity, and confidence to ensure the audience appreciates and comprehends the significance of your work.

By organizing your presentation well, telling a clear story through your analysis, and using visuals wisely, you can effectively share your data analytics case study.

This method helps people understand better, stay engaged, and draw valuable conclusions from your work.

We hope by now, you are feeling very confident processing a case study. But with any process, there are challenges you may encounter.

Key Challenges in Data Analytics Case Studies

A data analytics case study can present various hurdles that necessitate strategic approaches for successful navigation:

Challenge 1: Data Quality and Consistency

Challenge: Inconsistent or poor-quality data can impede analysis, leading to erroneous insights and flawed conclusions.

Solution: Implement rigorous data validation processes, ensuring accuracy, completeness, and reliability. Employ data cleansing techniques to rectify inconsistencies and enhance overall data quality.

Challenge 2: Complexity and Scale of Data

Challenge: Managing vast volumes of data with diverse formats and complexities poses analytical challenges.

Solution: Utilize scalable data processing frameworks and tools capable of handling diverse data types. Implement efficient data storage and retrieval systems to manage large-scale datasets effectively.

Challenge 3: Interpretation and Contextual Understanding

Challenge: Interpreting data without contextual understanding or domain expertise can lead to misinterpretations.

Solution: Collaborate with domain experts to contextualize data and derive relevant insights. Invest in understanding the nuances of the industry or domain under analysis to ensure accurate interpretations.

Interpretation and Contextual Understanding

Challenge 4: Privacy and Ethical Concerns

Challenge: Balancing data access for analysis while respecting privacy and ethical boundaries poses a challenge.

Solution: Implement robust data governance frameworks that prioritize data privacy and ethical considerations. Ensure compliance with regulatory standards and ethical guidelines throughout the analysis process.

Challenge 5: Resource Limitations and Time Constraints

Challenge: Limited resources and time constraints hinder comprehensive analysis and exhaustive data exploration.

Solution: Prioritize key objectives and allocate resources efficiently. Employ agile methodologies to iteratively analyze and derive insights, focusing on the most impactful aspects within the given timeframe.

Recognizing these challenges is key; it helps data analysts adopt proactive strategies to mitigate obstacles. This enhances the effectiveness and reliability of insights derived from a data analytics case study.

Now, let’s talk about the best software tools you should use when working with case studies.

Top 5 Software Tools for Case Studies

In the realm of case studies within data analytics, leveraging the right software tools is essential.

Here are some top-notch options:

Tableau : Renowned for its data visualization prowess, Tableau transforms raw data into interactive, visually compelling representations, ideal for presenting insights within a case study.

Python and R Libraries: These flexible programming languages provide many tools for handling data, doing statistics, and working with machine learning, meeting various needs in case studies.

Microsoft Excel : A staple tool for data analytics, Excel provides a user-friendly interface for basic analytics, making it useful for initial data exploration in a case study.

SQL Databases : Structured Query Language (SQL) databases assist in managing and querying large datasets, essential for organizing case study data effectively.

Statistical Software (e.g., SPSS , SAS ): Specialized statistical software enables in-depth statistical analysis, aiding in deriving precise insights from case study data.

Choosing the best mix of these tools, tailored to each case study’s needs, greatly boosts analytical abilities and results in data analytics.

Final Thoughts

Case studies in data analytics are helpful guides. They give real-world insights, improve skills, and show how data-driven decisions work.

Using case studies helps analysts learn, be creative, and make essential decisions confidently in their data work.

Check out our latest clip below to further your learning!

Frequently Asked Questions

What are the key steps to analyzing a data analytics case study.

When analyzing a case study, you should follow these steps:

Clarify the problem : Ensure you thoroughly understand the problem statement and the scope of the analysis.

Make assumptions : Define your assumptions to establish a feasible framework for analyzing the case.

Gather context : Acquire relevant information and context to support your analysis.

Analyze the data : Perform calculations, create visualizations, and conduct statistical analysis on the data.

Provide insights : Draw conclusions and develop actionable insights based on your analysis.

How can you effectively interpret results during a data scientist case study job interview?

During your next data science interview, interpret case study results succinctly and clearly. Utilize visual aids and numerical data to bolster your explanations, ensuring comprehension.

Frame the results in an audience-friendly manner, emphasizing relevance. Concentrate on deriving insights and actionable steps from the outcomes.

How do you showcase your data analyst skills in a project?

To demonstrate your skills effectively, consider these essential steps. Begin by selecting a problem that allows you to exhibit your capacity to handle real-world challenges through analysis.

Methodically document each phase, encompassing data cleaning, visualization, statistical analysis, and the interpretation of findings.

Utilize descriptive analysis techniques and effectively communicate your insights using clear visual aids and straightforward language. Ensure your project code is well-structured, with detailed comments and documentation, showcasing your proficiency in handling data in an organized manner.

Lastly, emphasize your expertise in SQL queries, programming languages, and various analytics tools throughout the project. These steps collectively highlight your competence and proficiency as a skilled data analyst, demonstrating your capabilities within the project.

Can you provide an example of a successful data analytics project using key metrics?

A prime illustration is utilizing analytics in healthcare to forecast hospital readmissions. Analysts leverage electronic health records, patient demographics, and clinical data to identify high-risk individuals.

Implementing preventive measures based on these key metrics helps curtail readmission rates, enhancing patient outcomes and cutting healthcare expenses.

This demonstrates how data analytics, driven by metrics, effectively tackles real-world challenges, yielding impactful solutions.

Why would a company invest in data analytics?

Companies invest in data analytics to gain valuable insights, enabling informed decision-making and strategic planning. This investment helps optimize operations, understand customer behavior, and stay competitive in their industry.

Ultimately, leveraging data analytics empowers companies to make smarter, data-driven choices, leading to enhanced efficiency, innovation, and growth.

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What is big data, the five ‘v’s of big data, what does facebook do with its big data, big data case study, challenges of big data, challenges of big data visualisation, security management challenges, cloud security governance challenges, challenges of big data: basic concepts, case study, and more.

Evolving constantly, the data management and architecture field is in an unprecedented state of sophistication. Globally, more than 2.5 quintillion bytes of data are created every day, and 90 percent of all the data in the world got generated in the last couple of years ( Forbes ). Data is the fuel for machine learning and meaningful insights across industries, so organizations are getting serious about how they collect, curate, and manage information.

This article will help you learn more about the vast world of Big Data, and the challenges of Big Data . And in case you thing challenges of Big Data and Big data as a concept is not a big deal, here are some facts that will help you reconsider:

About 300 billion emails get exchanged every day (Campaign Monitor)
400 hours of video are uploaded to YouTube every minute (Brandwatch)
Worldwide retail eCommerce accounts for more than $4 billion in revenue (Shopify)
Google receives more than 63,000 search inquiries every minute (SEO Tribunal)
By 2025, real-time data will account for more than a quarter of all data (IDC)

To get a handle on challenges of big data, you need to know what the word "Big Data" means. When we hear "Big Data," we might wonder how it differs from the more common "data." The term "data" refers to any unprocessed character or symbol that can be recorded on media or transmitted via electronic signals by a computer. Raw data, however, is useless until it is processed somehow.

Before we jump into the challenges of Big Data, let’s start with the five ‘V’s of Big Data.

Big Data is simply a catchall term used to describe data too large and complex to store in traditional databases. The “five ‘V’s” of Big Data are:

Volume – The amount of data generated
Velocity - The speed at which data is generated, collected and analyzed
Variety - The different types of structured, semi-structured and unstructured data
Value - The ability to turn data into useful insights
Veracity - Trustworthiness in terms of quality and accuracy

Facebook collects vast volumes of user data (in the range of petabytes, or 1 million gigabytes) in the form of comments, likes, interests, friends, and demographics. Facebook uses this information in a variety of ways:

To create personalized and relevant news feeds and sponsored ads
For photo tag suggestions
Flashbacks of photos and posts with the most engagement
Safety check-ins during crises or disasters

Next up, let us look at a Big Data case study, understand it’s nuances and then look at some of the challenges of Big Data.

As the number of Internet users grew throughout the last decade, Google was challenged with how to store so much user data on its traditional servers. With thousands of search queries raised every second, the retrieval process was consuming hundreds of megabytes and billions of CPU cycles. Google needed an extensive, distributed, highly fault-tolerant file system to store and process the queries. In response, Google developed the Google File System (GFS).

GFS architecture consists of one master and multiple chunk servers or slave machines. The master machine contains metadata, and the chunk servers/slave machines store data in a distributed fashion. Whenever a client on an API wants to read the data, the client contacts the master, which then responds with the metadata information. The client uses this metadata information to send a read/write request to the slave machines to generate a response.

The files are divided into fixed-size chunks and distributed across the chunk servers or slave machines. Features of the chunk servers include:

Each piece has 64 MB of data (128 MB from Hadoop version 2 onwards)
By default, each piece is replicated on multiple chunk servers three times
If any chunk server crashes, the data file is present in other chunk servers

Next up let us take a look at the challenges of Big Data, and the probable outcomes too!

With vast amounts of data generated daily, the greatest challenge is storage (especially when the data is in different formats) within legacy systems. Unstructured data cannot be stored in traditional databases.

Processing big data refers to the reading, transforming, extraction, and formatting of useful information from raw information. The input and output of information in unified formats continue to present difficulties.

Security is a big concern for organizations. Non-encrypted information is at risk of theft or damage by cyber-criminals. Therefore, data security professionals must balance access to data against maintaining strict security protocols.

Finding and Fixing Data Quality Issues

Many of you are probably dealing with challenges related to poor data quality, but solutions are available. The following are four approaches to fixing data problems:

Correct information in the original database.
Repairing the original data source is necessary to resolve any data inaccuracies.
You must use highly accurate methods of determining who someone is.

Scaling Big Data Systems

Database sharding, memory caching, moving to the cloud and separating read-only and write-active databases are all effective scaling methods. While each one of those approaches is fantastic on its own, combining them will lead you to the next level.

Evaluating and Selecting Big Data Technologies

Companies are spending millions on new big data technologies, and the market for such tools is expanding rapidly. In recent years, however, the IT industry has caught on to big data and analytics potential. The trending technologies include the following:

Hadoop Ecosystem

Apache Spark
NoSQL Databases
Predictive Analytics
Prescriptive Analytics

Big Data Environments

In an extensive data set, data is constantly being ingested from various sources, making it more dynamic than a data warehouse. The people in charge of the big data environment will fast forget where and what each data collection came from.

Real-Time Insights

The term "real-time analytics" describes the practice of performing analyses on data as a system is collecting it. Decisions may be made more efficiently and with more accurate information thanks to real-time analytics tools, which use logic and mathematics to deliver insights on this data quickly.

Data Validation

Before using data in a business process, its integrity, accuracy, and structure must be validated. The output of a data validation procedure can be used for further analysis, BI, or even to train a machine learning model.

Healthcare Challenges

Electronic health records (EHRs), genomic sequencing, medical research, wearables, and medical imaging are just a few examples of the many sources of health-related big data.

Barriers to Effective Use Of Big Data in Healthcare

The price of implementation
Compiling and polishing data
Disconnect in communication

Other issues with massive data visualisation include:

Distracting visuals; the majority of the elements are too close together. They are inseparable on the screen and cannot be separated by the user.
Reducing the publicly available data can be helpful; however, it also results in data loss.
Rapidly shifting visuals make it impossible for viewers to keep up with the action on screen.

The term "big data security" is used to describe the use of all available safeguards about data and analytics procedures. Both online and physical threats, including data theft, denial-of-service assaults, ransomware, and other malicious activities, can bring down an extensive data system.

It consists of a collection of regulations that must be followed. Specific guidelines or rules are applied to the utilisation of IT resources. The model focuses on making remote applications and data as secure as possible.

Some of the challenges are below mentioned:

Methods for Evaluating and Improving Performance
Governance/Control
Managing Expenses

And now that we know the challenges of Big Data, let’s take a look at the solutions too!

Hadoop as a Solution

Hadoop , an open-source framework for storing data and running applications on clusters of commodity hardware, is comprised of two main components:

Hadoop HDFS

Hadoop Distributed File System (HDFS) is the storage unit of Hadoop. It is a fault-tolerant, reliable, scalable layer of the Hadoop cluster. Designed for use on commodity machines with low-cost hardware, Hadoop allows access to data across multiple Hadoop clusters on various servers. HDFS has a default block size of 128 MB from Hadoop version 2 onwards, which can be increased based on requirements.

Hadoop MapReduce

Become a big data professional.

11.5 M Expected New Jobs For Data Analytics And Science Related Roles
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I completed Simplilearn's Post-Graduate Program in Data Engineering, with Purdue University. I gained knowledge on critical topics like the Hadoop framework, Data Processing using Spark, Data Pipelines with Kafka, Big Data and more. The live sessions, industry projects, masterclasses, and IBM hackathons were very useful.

Hadoop features Big Data security, providing end-to-end encryption to protect data while at rest within the Hadoop cluster and when moving across networks. Each processing layer has multiple processes running on different machines within a cluster. The components of the Hadoop ecosystem , while evolving every day, include:

Sqoop For ingestion of structured data from a Relational Database Management System (RDBMS) into the HDFS (and export back).
Flume For ingestion of streaming or unstructured data directly into the HDFS or a data warehouse system (such as Hive
Hive A data warehouse system on top of HDFS in which users can write SQL queries to process data
HCatalog Enables the user to store data in any format and structure
Oozie A workflow manager used to schedule jobs on the Hadoop cluster
Apache Zookeeper A centralized service of the Hadoop ecosystem, responsible for coordinating large clusters of machines
Pig A language allowing concise scripting to analyze and query datasets stored in HDFS
Apache Drill Supports data-intensive distributed applications for interactive analysis of large-scale datasets
Mahout For machine learning

MapReduce Algorithm

Hadoop MapReduce is among the oldest and most mature processing frameworks. Google introduced the MapReduce programming model in 2004 to store and process data on multiple servers, and analyze in real-time. Developers use MapReduce to manage data in two phases:

Map Phase In which data gets sorted by applying a function or computation on every element. It sorts and shuffles data and decides how much data to process at a time.
Reduce Phase Segregating data into logical clusters, removing bad data, and retaining necessary information.

Now that you have understood the five ‘V’s of Big Data, Big Data case study, challenges of Big Data, and some of the solutions too, it’s time you scale up your knowledge and become industry ready. Most organizations are making use of big data to draw insights and support strategic business decisions. Simplilearn's Caltech Post Graduate Program in Data Science will help you get ahead in your career!

If you have any questions, feel free to post them in the comments below. Our team will get back to you at the earliest.

Our Big Data Courses Duration And Fees

Big Data Courses typically range from a few weeks to several months, with fees varying based on program and institution.

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PMP, PMI, PMBOK, CAPM, PgMP, PfMP, ACP, PBA, RMP, SP, and OPM3 are registered marks of the Project Management Institute, Inc.

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(PDF) Conceptualizing Big Data: Analysis of Case Studies
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Top 10 Big Data Case Studies that You Should Know
Top 10 Big Data Case Studies. 1. Big data in Netflix. Netflix implements data analytics models to discover customer behavior and buying patterns. Then, using this information it recommends movies and TV shows to their customers. That is, it analyzes the customer's choice and preferences and suggests shows and movies accordingly.
20+ Most Effective Big Data Analytics Use Cases
1. Cost Reduction. Big data analytics offers data-driven insights for the business stakeholders and they can take better strategic decisions, streamline and optimize the operational processes and understand their customers better. All this helps in cost-cutting and adds efficiency to the business model.
Big data case study: How UPS is using analytics to improve ...
Perez says UPS is using technology to improve its flexibility, capability, and efficiency, and that the right insight at the right time helps line-of-business managers to improve performance. The ...
Amazon: Big Data Analysis Case Study
A big data analysis case study of amazon. Big data is one of the advanced technologies mainly used for evaluating and integrating the collected data in the companies. The use of big data is ...
10 Real World Data Science Case Studies Projects with Example
Analysis of Big data also helps them understand new item sales, make decisions on discontinuing products, and the performance of brands. ... A case study in data science is an in-depth analysis of a real-world problem using data-driven approaches. It involves collecting, cleaning, and analyzing data to extract insights and solve challenges ...
PDF case study collection 7 get big data
big data - case study collection 5 cameras, tracking devices and coupling this with on-board and real-time data analysis from Google Maps, Streetview and other sources allows the Google car to safely drive on the roads without any input from a human driver. Perhaps the most astounding use Google have found for their
5 Big Data Case Studies
Following are the interesting big data case studies -. 1. Big Data Case Study - Walmart. Walmart is the largest retailer in the world and the world's largest company by revenue, with more than 2 million employees and 20000 stores in 28 countries. It started making use of big data analytics much before the word Big Data came into the picture.
Companies Using Big Data
Review big data case studies & learn more about big data applications in business now. Sunday, April 21, 2024 ... Optimized big data analysis to analyze flight angle, take-off speed, and landing speed, maximizing predictive analytics for engine and flight safety ... Read the full mLogica on SAP HANA Cloud case study here. Read next: Big Data ...
How companies are using big data and analytics
Few dispute that organizations have more data than ever at their disposal. But actually deriving meaningful insights from that data—and converting knowledge into action—is easier said than done. We spoke with six senior leaders from major organizations and asked them about the challenges and opportunities involved in adopting advanced analytics: Murli Buluswar, chief science officer at AIG ...
Big Data Analytics
With big data analytics, you can ultimately fuel better and faster decision-making, modelling and predicting of future outcomes and enhanced business intelligence. As you build your big data solution, consider open source software such as Apache Hadoop, Apache Spark and the entire Hadoop ecosystem as cost-effective, flexible data processing and ...
GE's Big Bet on Data and Analytics
GE has bet big on the Industrial Internet — the convergence of industrial machines, data, and the Internet. The company is putting sensors on gas turbines, jet engines, and other machines; connecting them to the cloud; and analyzing the resulting flow of data. The goal: identify ways to improve machine productivity and reliability.
8 case studies and real world examples of how Big Data has helped keep
Here are some case studies that show some ways BI is making a difference for companies around the world: 1) Starbucks: ... Coca Cola is relying on Big Data to gain and maintain their competitive advantage. 4) American Express GBT. The American Express Global Business Travel company, popularly known as Amex GBT, is an American multinational ...
Netflix Recommender System
The V's of Big Data . Volume: As of May 2019, Netflix has around 13,612 titles (Gaël, 2019). Their US library alone consists of 5087 titles. As of 2016, Netflix has completed its migration to Amazon Web Services. Their data of tens of petabytes of data was moved to AWS (Brodkin et al., 2016).
What Is Big Data Analytics? Definition, Benefits, and More
What is big data analytics? Big data analytics is the process of collecting, examining, and analyzing large amounts of data to discover market trends, insights, and patterns that can help companies make better business decisions. This information is available quickly and efficiently so that companies can be agile in crafting plans to maintain their competitive advantage.
Big Data Statistics: 40 Use Cases and Real-life Examples
Though the majority of big data use cases are about data storage and processing, they cover multiple business aspects, such as customer analytics, risk assessment and fraud detection. So, each business can find the relevant use case to satisfy their particular needs. References [1] 2017 Big Data Analytics Market Study by Dresner Advisory Services
Data Analysis Case Study: Learn From These Winning Data Projects
Humana's Automated Data Analysis Case Study. The key thing to note here is that the approach to creating a successful data program varies from industry to industry. Let's start with one to demonstrate the kind of value you can glean from these kinds of success stories. Humana has provided health insurance to Americans for over 50 years.
8 big data use cases for businesses and industry examples
However, the rule sets quickly became difficult to manage, especially in the world of online transactions. 4. Improved forecasting and price optimization. While it might not be possible to know what will happen tomorrow with certainty, big data is giving organizations the power to spot patterns and trends early.
Google Data Analytics Capstone: Complete a Case Study
There are 4 modules in this course. This course is the eighth and final course in the Google Data Analytics Certificate. You'll have the opportunity to complete a case study, which will help prepare you for your data analytics job hunt. Case studies are commonly used by employers to assess analytical skills. For your case study, you'll ...
Big Data Use Case: How Amazon uses Big Data to drive eCommerce revenue
1. Helps you understand the market condition: Big Data assists you in comprehending market circumstances, trends, and wants, as well as your competitors, through data analysis. It helps you to research customer interests and behaviors so that you may adjust your products and services to their requirements. 2.
Case studies & examples
This case study shows the opportunities of cross-agency data collaboration, as well as some of the challenges of using big data and administrative data in the federal government. Source Bureau of Economic Analysis / Bureau of Labor Statistics
Data Analytics Case Study Guide (Updated for 2024)
Step 1: With Data Analytics Case Studies, Start by Making Assumptions. Hint: Start by making assumptions and thinking out loud. With this question, focus on coming up with a metric to support the hypothesis. If the question is unclear or if you think you need more information, be sure to ask.
Data Analytics Case Study Guide 2024
A data analytics case study comprises essential elements that structure the analytical journey: Problem Context: A case study begins with a defined problem or question. It provides the context for the data analysis, setting the stage for exploration and investigation.. Data Collection and Sources: It involves gathering relevant data from various sources, ensuring data accuracy, completeness ...
Challenges of Big Data: Basic Concepts, Case Study, and More
The Five 'V's of Big Data. Big Data is simply a catchall term used to describe data too large and complex to store in traditional databases. The "five 'V's" of Big Data are: Volume - The amount of data generated. Velocity - The speed at which data is generated, collected and analyzed. Variety - The different types of structured ...
Latest science news, discoveries and analysis
Find breaking science news and analysis from the world's leading research journal.

Effective Big Data Analytics Use Cases in 20+ Industries

What is Big Data Analytics?

Core Strengths of Big Data Analytics

1. Cost Reduction

2. Reliable and Continuous Data

3. New Products and Services

4. Improved Efficiency

Actionable Advice for Data-Driven Leaders

5. Better Monitoring and Tracking

6. Better Remote Resource Management

7. Taking Right Organizational Decisions

Big Data Analytics Use Cases in Various Industries

2. Accounting

3. Aviation

4. Agriculture

5. Automotive

6. Biomedical Research and Healthcare (Cancer, Genomic medicine, COVID-19 Management)

7. Business and Management

8. Cloud Computing

9. Cybersecurity

10. Government and Law Enforcement

11. Oil, Gas & Renewable Energy

12. Manufacturing & Supply Chain Management

13. Retail

14. Stock Market

15. Telecom

16. Media and Entertainment

17. Education

18. Pharmacy

19. Psychology

20. Project Management

21. Marketing and Sales (Advertising)

22. Social Media Management

23. Hospitality, Restaurants, and Tourism

24. Miscellaneous Use Cases

Image Processing

Big Data Analytics: Laying the Road for Future-Ready Businesses

Arya Bharti

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