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4 Examples of Business Analytics in Action

• 15 Jan 2019

Data is a valuable resource in today’s ever-changing marketplace. For business professionals, knowing how to interpret and communicate data is an indispensable skill that can inform sound decision-making.

“The ability to bring data-driven insights into decision-making is extremely powerful—all the more so given all the companies that can’t hire enough people who have these capabilities,” says Harvard Business School Professor Jan Hammond , who teaches the online course Business Analytics . “It’s the way the world is going.”

Before taking a look at how some companies are harnessing the power of data, it’s important to have a baseline understanding of what the term “business analytics” means.

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What Is Business Analytics?

Business analytics is the use of math and statistics to collect, analyze, and interpret data to make better business decisions.

There are four key types of business analytics: descriptive, predictive, diagnostic, and prescriptive. Descriptive analytics is the interpretation of historical data to identify trends and patterns, while predictive analytics centers on taking that information and using it to forecast future outcomes. Diagnostic analytics can be used to identify the root cause of a problem. In the case of prescriptive analytics , testing and other techniques are employed to determine which outcome will yield the best result in a given scenario.

Related : 4 Types of Data Analytics to Improve Decision-Making

Across industries, these data-driven approaches have been employed by professionals to make informed business decisions and attain organizational success.

Check out the video below to learn more about business analytics, and subscribe to our YouTube channel for more explainer content!

Business Analytics vs. Data Science

It’s important to highlight the difference between business analytics and data science . While both processes use big data to solve business problems they’re separate fields.

The main goal of business analytics is to extract meaningful insights from data to guide organizational decisions, while data science is focused on turning raw data into meaningful conclusions through using algorithms and statistical models. Business analysts participate in tasks such as budgeting, forecasting, and product development, while data scientists focus on data wrangling , programming, and statistical modeling.

While they consist of different functions and processes, business analytics and data science are both vital to today’s organizations. Here are four examples of how organizations are using business analytics to their benefit.

Business Analytics Examples

According to a recent survey by McKinsey , an increasing share of organizations report using analytics to generate growth. Here’s a look at how four companies are aligning with that trend and applying data insights to their decision-making processes.

1. Improving Productivity and Collaboration at Microsoft

At technology giant Microsoft , collaboration is key to a productive, innovative work environment. Following a 2015 move of its engineering group's offices, the company sought to understand how fostering face-to-face interactions among staff could boost employee performance and save money.

Microsoft’s Workplace Analytics team hypothesized that moving the 1,200-person group from five buildings to four could improve collaboration by increasing the number of employees per building and reducing the distance that staff needed to travel for meetings. This assumption was partially based on an earlier study by Microsoft , which found that people are more likely to collaborate when they’re more closely located to one another.

In an article for the Harvard Business Review , the company’s analytics team shared the outcomes they observed as a result of the relocation. Through looking at metadata attached to employee calendars, the team found that the move resulted in a 46 percent decrease in meeting travel time. This translated into a combined 100 hours saved per week across all relocated staff members and an estimated savings of $520,000 per year in employee time.

The results also showed that teams were meeting more often due to being in closer proximity, with the average number of weekly meetings per person increasing from 14 to 18. In addition, the average duration of meetings slightly declined, from 0.85 hours to 0.77 hours. These findings signaled that the relocation both improved collaboration among employees and increased operational efficiency.

For Microsoft, the insights gleaned from this analysis underscored the importance of in-person interactions and helped the company understand how thoughtful planning of employee workspaces could lead to significant time and cost savings.

2. Enhancing Customer Support at Uber

Ensuring a quality user experience is a top priority for ride-hailing company Uber. To streamline its customer service capabilities, the company developed a Customer Obsession Ticket Assistant (COTA) in early 2018—a tool that uses machine learning and natural language processing to help agents improve their speed and accuracy when responding to support tickets.

COTA’s implementation delivered positive results. The tool reduced ticket resolution time by 10 percent, and its success prompted the Uber Engineering team to explore how it could be improved.

For the second iteration of the product, COTA v2, the team focused on integrating a deep learning architecture that could scale as the company grew. Before rolling out the update, Uber turned to A/B testing —a method of comparing the outcomes of two different choices (in this case, COTA v1 and COTA v2)—to validate the upgraded tool’s performance.

Preceding the A/B test was an A/A test, during which both a control group and a treatment group used the first version of COTA for one week. The treatment group was then given access to COTA v2 to kick off the A/B testing phase, which lasted for one month.

At the conclusion of testing, it was found that there was a nearly seven percent relative reduction in average handle time per ticket for the treatment group during the A/B phase, indicating that the use of COTA v2 led to faster service and more accurate resolution recommendations. The results also showed that customer satisfaction scores slightly improved as a result of using COTA v2.

With the use of A/B testing, Uber determined that implementing COTA v2 would not only improve customer service, but save millions of dollars by streamlining its ticket resolution process.

Related : How to Analyze a Dataset: 6 Steps

3. Forecasting Orders and Recipes at Blue Apron

For meal kit delivery service Blue Apron, understanding customer behavior and preferences is vitally important to its success. Each week, the company presents subscribers with a fixed menu of meals available for purchase and employs predictive analytics to forecast demand , with the aim of using data to avoid product spoilage and fulfill orders.

To arrive at these predictions, Blue Apron uses algorithms that take several variables into account, which typically fall into three categories: customer-related features, recipe-related features, and seasonality features. Customer-related features describe historical data that depicts a given user’s order frequency, while recipe-related features focus on a subscriber’s past recipe preferences, allowing the company to infer which upcoming meals they’re likely to order. In the case of seasonality features, purchasing patterns are examined to determine when order rates may be higher or lower, depending on the time of year.

Through regression analysis—a statistical method used to examine the relationship between variables—Blue Apron’s engineering team has successfully measured the precision of its forecasting models. The team reports that, overall, the root-mean-square error—the difference between predicted and observed values—of their projection of future orders is consistently less than six percent, indicating a high level of forecasting accuracy.

By employing predictive analytics to better understand customers, Blue Apron has improved its user experience, identified how subscriber tastes change over time, and recognized how shifting preferences are impacted by recipe offerings.

Related : 5 Business Analytics Skills for Professionals

4. Targeting Consumers at PepsiCo

Consumers are crucial to the success of multinational food and beverage company PepsiCo. The company supplies retailers in more than 200 countries worldwide , serving a billion customers every day. To ensure the right quantities and types of products are available to consumers in certain locations, PepsiCo uses big data and predictive analytics.

PepsiCo created a cloud-based data and analytics platform called Pep Worx to make more informed decisions regarding product merchandising. With Pep Worx, the company identifies shoppers in the United States who are likely to be highly interested in a specific PepsiCo brand or product.

For example, Pep Worx enabled PepsiCo to distinguish 24 million households from its dataset of 110 million US households that would be most likely to be interested in Quaker Overnight Oats. The company then identified specific retailers that these households might shop at and targeted their unique audiences. Ultimately, these customers drove 80 percent of the product’s sales growth in its first 12 months after launch.

PepsiCo’s analysis of consumer data is a prime example of how data-driven decision-making can help today’s organizations maximize profits.

Developing a Data Mindset

As these companies illustrate, analytics can be a powerful tool for organizations seeking to grow and improve their services and operations. At the individual level, a deep understanding of data can not only lead to better decision-making, but career advancement and recognition in the workplace.

“Using data analytics is a very effective way to have influence in an organization,” Hammond says . “If you’re able to go into a meeting, and other people have opinions, but you have data to support your arguments and your recommendations, you’re going to be influential.”

Do you want to leverage the power of data within your organization? Explore Business Analytics —one of our online business essentials courses —to learn how to use data analysis to solve business problems.

This post was updated on March 24, 2023. It was originally published on January 15, 2019.

About the Author

We will be taking some time off to enjoy Tet Holidays, from an 24th to Feb 2nd and back to work at Feb 3rd.

Business Data Analytics: 5 Essentials

 / February 26, 2024

In today’s fast-paced world, business data analytics plays a pivotal role. It’s the secret sauce that helps businesses make informed decisions, optimize operations, and stay competitive. This article is a treasure trove for those who are curious about this exciting field.

Whether you’re a high school student exploring career options, a professional looking to upskill, or someone simply interested in business analytics, this article has something for you. 

The article unfolds: 

• Clear definition of business data analytics ;
• 5 responsibilities of business data analysts, drawing insights from industry experts at Designveloper (DSV);
• Essential skills or tools, 5 impacts of business data analytics as well as 5 relevant courses that can help you acquire these skills.

But that’s not all. The article also presents real-world examples that showcase the profound impacts of business data analytics. These examples will give you a glimpse of how data analytics can transform businesses.

So, if you’re ready to embark on an enlightening journey into the world of business data analytics, keep reading. 

What is Business Data Analytics?

Business data analytics (known as “business analytics”) is a practice that involves the use of statistical techniques, computational tools, and methodologies to analyze business data. The goal is to extract valuable insights that can aid in decision-making, improve business operations, and drive growth.

Business Data Analytics vs. Traditional Data Analytics and Business Intelligence

While all three fields involve working with data, there are key differences.

Traditional data analytics often involves analyzing historical data to understand past trends. It’s reactive, providing insights into what has happened.

On the other hand, business data analytics is more proactive. It not only analyzes historical data but also uses predictive analytics to forecast future trends. It’s about making data actionable, then using it to drive strategic decisions and business growth.

Business intelligence (BI) is another related field. It involves the use of software and services to transform data into actionable intelligence. While BI provides descriptive insights (what happened and why), business data analytics goes a step further. It provides predictive (what could happen) and prescriptive insights (what action should be taken).

In essence, business data analytics is a more comprehensive approach to data analysis. It combines the best of traditional data analytics and business intelligence, providing businesses with the insights they need to thrive in today’s data-driven world.

Importance of Business Data Analytics in Businesses

In the business world, data is a gold mine. It holds the key to understanding market trends, customer behavior, operational bottlenecks, and much more. 

Business data analytics helps businesses unlock this potential. It allows them to transform raw data into meaningful information, providing a deeper understanding of their performance, customers, and market.

According to a study by MicroStrategy , companies across the globe are leveraging data to enhance efficiency and productivity (64%), facilitate more effective decision-making (56%), and drive superior financial performance (51%). For this reason, 65% of global enterprises plan to increase their analytics spending.

The importance of business data analytics in today’s business landscape is undeniable. But will it continue to hold such significance in the future? 

The number suggests a resounding yes. According to Mordor Intelligence, the business data analytics market, valued at USD 81.46 billion in 2023, is projected to reach USD 130.95 billion in the next five years, growing at a CAGR of 8.07%.

And coupled with this growth, you may observe some outstanding trends in business analytics as follows:

Diverse Data Sources

Previously, many businesses made strategic decisions by analyzing business data that come from expansive, centrally controlled data servers or repositories. But, there has been a paradigm shift in this scenario. Today, business analysts are garnering data from an array of varied sources, especially social networking platforms.

Focus on BI & Data Visualization

The business analytics market is rapidly changing its focus on BI and Data Visualization. While real-time BI serves quicker decision-making, Data Visualization is widely used for clearer pattern recognition. 

Investment in Advanced Techs

Vendors are stepping up their game by introducing innovative business analytics solutions. They’re integrating advanced technologies (like Machine Learning) for superior data storage and visualization. For this reason, modern business analytics platforms can offer enhanced data visibility and understanding.

With the higher demand for data and advanced techs, business data analytics continues to be a vital tool for businesses in the future, driving strategic decisions and fostering growth. 

5 Responsibilities of a Business Data Analyst from Designveloper’s Insight

At DSV, a business data analyst plays a multifaceted role that involves collecting, mining, analyzing, predicting, and visualizing data. Each of these responsibilities is crucial in helping the company leverage data to drive business growth and success.

Also, this role requires a strong understanding of data analysis techniques, a keen eye for detail, and the ability to communicate complex information clearly.

To further understand how business analysts work in DSV, let’s take a closer look at their key responsibilities:

Data Collection

The first step in the data analytics process is collecting data. At DSV, business data analysts gather data from various sources, both internal and external. This could include sales figures, market research, logistics, or customer feedback. The goal is to collect high-quality data that is relevant and reliable.

Data Mining

Once the data is collected, we’ll move to the next step known as Data Mining. This involves using sophisticated statistical techniques and advanced software to sift through large data sets and uncover hidden patterns, correlations, and insights. These insights can help us make informed decisions, identify opportunities for growth, and predict future trends. 

Descriptive Analytics

Descriptive analytics is all about interpreting historical data to understand what has happened in the past. This involves examining key performance indicators (KPIs), identifying trends, and understanding the factors that have contributed to these trends. By understanding our past performance, we can identify areas for improvement and make strategic decisions for the future.

Predictive Analytics

Predictive analytics involves using statistical models and forecasts to understand the future. At DSV, we employ data and statistical algorithms to create predictive models that can anticipate customer behavior, market trends, and business outcomes. This helps the company to be proactive, quickly adapt to changing market conditions, and make data-driven decisions. 

Visualization and Reporting

To ensure all stakeholders understand what data tells, DSV uses data visualization tools built in software like Tableau or Power BI to create visual representations of the data (e.g., charts, graphs, and dashboards). We also prepare detailed reports that provide insights into our performance, trends, and forecasts. These visualizations and reports enable data-driven decision-making and strategic planning at DSV.

Essential Skills and Tools for a Business Data Analyst

In the dynamic world of business data analytics, possessing the right skills and tools is of paramount importance. 

A business data analyst serves as a vital link, transforming raw data into meaningful business insights. This transformation, however, requires a unique blend of technical prowess, tool proficiency, and soft skills. 

So, what essential skills and tools should a business data analyst have? Let’s take a look:

Technical Skills and Tools

First, consider several technical skills the analyst must hone, coupled with corresponding tools:

• Statistical Analysis: A robust understanding of statistics is vital for interpreting data. It allows analysts to identify trends, test hypotheses, and make predictions. Tools like R and Python , with libraries such as NumPy and SciPy, are commonly used for statistical analysis.
• Data Manipulation and Analysis: Analysts often work with large datasets that need to be manipulated and analyzed. Proficiency in SQL for querying databases, and tools like Excel for spreadsheet analysis are fundamental. Python’s Pandas library is also a powerful tool for data manipulation and analysis.
• Data Visualization: The ability to present data in a clear, visual format helps stakeholders understand complex data insights at a glance. Some popular tools providing this feature include Tableau and Power BI.
• Machine Learning: A basic understanding of machine learning algorithms and their applications can be beneficial. It allows analysts to build predictive models and uncover patterns in data. Mastering programming languages like R or Python and some tools like Python’s Scikit-learn or TensorFlow helps analysts in this area.
• Data Warehousing: Knowledge of data warehousing concepts and tools like Hadoop, Apache Hive or Google BigQuery can be advantageous. It enables analysts to manage and retrieve data efficiently.
• Documentation: Familiarity with documentation tools like Confluence or Microsoft Office Suite (Word, Excel, PowerPoint) is important for recording findings and methodologies. Good documentation ensures transparency and reproducibility in data analysis.

Soft Skills

Apart from technical skills, a business analyst must develop some following soft skills

• Problem-Solving: The ability to approach complex business problems, hypothesize solutions, and test them with data is crucial. It involves creativity, logical reasoning, and a systematic approach.
• Communication: Analysts need to communicate their findings to relevant stakeholders. Good communication ensures that insights are understood and acted upon.
• Critical Thinking: The ability to question assumptions, validate data, and interpret results in the context of the business is important. It prevents misinterpretation of data and ensures accurate insights.
• Attention to Detail: Precision and accuracy in data analysis are vital to avoid costly mistakes and inaccurate conclusions. It involves checking data for errors, outliers, and inconsistencies.
• Business Acumen: Understanding the business context and industry trends helps in deriving meaningful insights from data. It allows analysts to align their analysis with business objectives.
• Teamwork: Collaborating effectively with diverse teams, including data scientists, IT staff, and business executives, is often required in a data analyst role. It promotes a holistic approach to problem-solving.
• Time Management: Balancing multiple projects and meeting deadlines is a common part of a data analyst’s role. Good time management ensures productivity and efficiency.

Remember, the best data analysts are lifelong learners, constantly updating their skills and tools to keep up with the rapidly evolving data landscape. So when pursuing this career, keep learning to advance your professional knowledge and soft skills.

5 Real-World Impacts of Business Data Analytics

Having explored the responsibilities of business analysts and the skills they need to excel, it’s crucial to understand the tangible effects of their work. In this section, we’ll delve into how the data-driven insights generated by these professionals are making a significant difference in the business world.

Western Digital

As a global pioneer in data storage technology, Western Digital harnesses the power of data analytics to streamline its operations and guide strategic decision-making. 

Their proprietary Western Digital Device Analytics (WDDA) offers proactive surveillance of the storage subsystem, mitigating unforeseen disruptions. 

Their data analytics capabilities extend to managing analytics across diverse geographical locations, enhancing data protection, and establishing a real-time framework for security and cybersecurity event management. 

This empowers them to forecast demand, administer inventory, and curtail operational expenses, culminating in heightened customer satisfaction and augmented profitability.

Adventist Health

Adventist Health employs data analytics to enhance patient care and operational efficiency. They utilize AI endpoint analytics to maintain their network, identifying and profiling over 58,000 healthcare-related devices, thereby gaining unparalleled visibility and a robust foundation for formulating security policies. 

Their use of data analytics extends to financial planning, where a small team of data analysts caters to the analytics needs of 900 individuals. By scrutinizing patient data, they can discern patterns and trends, predict health outcomes, and devise personalized treatment plans, leading to improved patient outcomes and diminished healthcare costs.

American Express

American Express leverages data analytics to identify fraudulent transactions and make informed business decisions. They scrutinize historical transactions and 115 variables to predict potential churn. 

They also employ machine learning models to achieve superior discrimination and gain a deeper understanding of customer behavior. 

By examining patterns and anomalies in transaction data, they can pinpoint potential fraud and implement preventive measures, resulting in decreased financial losses and enhanced customer trust.

Goldman Sachs

Goldman Sachs, a multinational investment bank and financial services company, utilizes data analytics for regulatory compliance, gaining market insights, and developing new products. 

They employ advanced statistical software to manage and analyze vast volumes of data, enabling them to foresee the impacts of economic, market, and regulatory forces on business strategy and outcomes.

Microsoft employs data analytics to refine its software products and services. They gather and analyze user feedback and usage data to comprehend how users interact with their products and identify potential improvements. This approach has led to enhanced product quality and user satisfaction.

5 Courses for Aspiring Business Data Analysts

After gaining a basic understanding and recognizing the potential of business data analysts, it’s crucial to consider the courses that can further enhance your skills and knowledge in this field. 

They not only provide a structured way to learn the necessary skills but also add credibility to your profile. Here are some popular courses for those interested in becoming a business data analyst: 

Business Analytics, University of Harvard

Study Format: Online

Duration: 8 weeks

Tuition fees: USD 1,750 + applicable international taxes

Scholarship or Financial Aids: Yes

The course provides key knowledge on data interpretation, trend recognition, variable analysis, hypothesis development, survey design, and implementation of analytical techniques in Excel. 

It also offers an engaging learning experience with activities every three to five minutes and opportunities for collaboration with a global community of peers. Besides, the course allows you to access real-world examples to understand how business analytics is applied in industry-leading companies like Walt Disney Studios or Amazon. 

Who is this for: This course is ideal for college students or recent graduates looking to use basic statistics in real business cases. Further, it welcomes those considering higher education to advance their analytical skills or mid-career professionals seeking hands-on experience in interpreting and analyzing data. 

Certification : Upon completion, you’ll earn a Certificate of Completion to enhance your resume. But if you complete the three-course CORe curriculum and pass the HBS Online exam, you’ll obtain a Credential of Readiness to demonstrate your mastery of business basics.

Business Analytics Specialization, Wharton School at University of Pennsylvania

Duration: 4 weeks per course, 2-3 hours per week

Tuition fees: USD 79 per month

This program equips you with the essential skills to cultivate data literacy and an analytic mindset. Accordingly, you can make strategic decisions across different parts of a business, like marketing, finance, or operations. 

The program includes the following courses:

• Customer Analytics: Wharton’s esteemed marketing professors offer an overview of customer analytics through real-world business practices at leading companies like Amazon, Google, and Starbucks.
• Operations Analytics: This course focuses on leveraging data to profitably align supply with demand in various business settings.
• People Analytics: In this course, Wharton’s top professors and pioneers in people analytics will explore state-of-the-art techniques used to recruit and retain top talent, showcasing how these techniques are employed at cutting-edge companies.
• Accounting Analytics: This course gives you knowledge on how data is leveraged to evaluate the factors influencing financial performance and to predict upcoming financial conditions. 
• Business Analytics Capstone: Through this capstone project, you can use your understanding of data analytics to address a practical business problem that global tech giants (like Google or Facebook) are facing. 

Who is this for: This program is designed for managers and leaders seeking to break away from the pack. The Business Analytics online program offers an introduction to big data analytics for all business professionals, including those with no prior analytics experience.

Certification : Upon completion, you’ll earn the Specialization Certificate.

Business Analytics: From Data to Decisions, Imperial College Business School

Study Format: Online, part-time

Duration: 16 weeks 

Tuition fees: £1,880

Scholarship or Financial Aids: No, but you can get £188 off if you refer your coworkers to this course

This program empowers you to leverage descriptive, predictive, and prescriptive analytics to tackle complex business challenges. You’ll also gain the skills to critically assess recommendations and effectively communicate with technical experts, amplifying your influence within your organization.

In this 16-week online journey, you’ll collaborate with a diverse group of peers to delve into the practical implications of the analytical models you’re studying. Also, you can engage in live online teaching sessions and video lectures, enriched with interactive activities and assignments. 

No prior coding experience is necessary. It’s because the program includes two primer modules to familiarise you with mathematics, statistics, and Python.

Who is this for: This global program is tailored for seasoned professionals seeking to enhance their decision-making capabilities and propel their careers forward with data analytics.

Certification : Upon completion, you’ll earn a verified Digital Certificate from Imperial College Business School.

Bachelor of Science in Business Data Analytics, Arizona State University

Study Format: On campus or online

Duration: 3 – 4 years

Tuition fees: Not specified

The Bachelor’s degree in Business Data Analytics is a comprehensive program designed to equip you with the necessary knowledge, skills, and experience to spearhead big data initiatives and manage associated business processes. This program, thereby, enables you to practice large-scale business data analytics within organizations.

The program imparts both organizational and technical competencies, enabling you to execute data collection, cleansing, integration, and modeling tasks, as well as perform data asset analysis for business applications. 

It also provides a thorough understanding of data warehousing, dimensional modeling, big data analytics methodologies, and visualization tools and techniques, while also introducing subjects such as data mining and predictive analytics.

Who is this for: This program is ideal for those who wish to delve deeper into the field of business data analytics beyond short courses. It offers an opportunity to gain profound academic knowledge in this rapidly evolving field.

Degree: Upon completion, you’ll receive a Bachelor of Science degree in Business Data Analytics.

BS in Business Analytics, The George Washington University (GW)

Study Format: On campus

Duration: Not specified, but the degree requires students to complete 120 credits (including mandatory and elective courses)

Tuition fees: $85,740 for new, first-time students & $89,090 for continuing students (estimated in the 2023-2024 academic year)

Like Arizona State University, this BS program at the GW Business School also offers a robust curriculum that combines foundational courses in descriptive, predictive, and prescriptive analytics. Besides, it provides students with elective options in specific functional areas, soft skills, and practical exposure to industry-standard analytics tools.

Further, you’ll have a chance to implement an industry-related project that serves as a capstone experience. There, you can work on a real-world problem, typically from one of the school’s advisory board partner firms, and present your findings upon completion.

Who is this for: This degree is ideal for students who are passionate about data analytics in the business context. Depending on their academic and career aspirations, students can choose this degree as their primary or secondary major.

Degree: Upon completion, you’ll receive a Bachelor of Science in Business Analytics from the GW Business School.

Final Words

So, there you have it! You’ve explored the secrets behind business data analytics, uncovering its duties, tools, and real-world magic. 

Data is the key to unlocking hidden insights and propelling businesses forward. But this field isn’t just for tech geeks; it’s open to anyone with a curious mind and a desire to make an impact. If you’re curious, ambitious, and ready to learn, this field has a place for you. 

Start by exploring the resources mentioned earlier. Take a course, learn a new tool, or simply dive deeper into the amazing examples we shared. With each step, you’ll unlock a new piece of the puzzle. Who knows, you might even become the next business data analyst, transforming businesses and making a real impact on the world!

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Data Analytics in Business: A Complete Overview

• Written by John Terra
• Updated on February 27, 2024

In these challenging economic times, businesses seek ways to secure a competitive edge and keep their commerce thriving. One of the primary keys to business success is making sound decisions, and one of the best ways of assuring your choices are good is to base them on accurate information. And for there to be accurate information, you need business analytics.

This article explores the subject of data analytics in business. We will define the term, explain its benefits, why it’s essential, and how data analytics can improve business management. We’ll also share to learn critical skills and tools through online data analytics training .

Let’s get the ball rolling by defining data analytics in business.

What is Data Analytics in Business?

Data analytics in business describes collecting, processing, analyzing, and interpreting vast volumes of data, extracting meaningful insights, trends, and patterns that can guide and inform strategic decisions, improve operational efficiency, and foster overall business growth. Data analytics involves using diverse techniques, tools, and methodologies to change raw data into actionable information, which can then be used to make better-informed choices and optimize different aspects of the business.

How Do You Conduct Business Analytics?

Business analytics typically breaks down into the following steps:

• Data Collection . Collecting relevant data from different sources, which includes marketing campaigns, customer interactions, operational processes, sales transactions, and external market data.
• Data Processing . Cleaning, organizing, and preparing the collected data for analysis to ensure its accuracy and consistency.
• Data Analysis. Applying mathematical, statistical, and machine learning techniques to discover correlations, patterns, and insights within the processed data.
• Data Interpretation . Interpreting the data analysis results in deriving actionable insights and conclusions that can improve the likelihood of better decision-making.
• Decision Making. Employing the insights from data analysis to make informed decisions that will impact different areas of the organization, such as marketing strategies, resource allocation, product development, etc.
• Continuous Improvement . Monitoring and evaluating decision outcomes based on data analytics, then refining and adjusting the strategies over time-based on subsequently obtained data and insights.

The Difference Between Data Analytics and Business Analytics

Many people use business and data analytics interchangeably, but they are subtly different. Data analytics is a subset of business analytics, which uses data to analyze current and past business performances to obtain insights that help executives make better-informed decisions. Let’s show the differences by exploring what each analyst does:

Data Analysts

• Work with business leaders and stakeholders to define problems or business needs
• Identify and source data
• Clean and prepare data for analysis
• Analyze data, looking for patterns and trends
• Visualize data to make it easier to comprehend
• Present data so that it tells a compelling story

Business Analysts

• Evaluate the company’s current functions and IT structures
• Review processes and interview team members to identify critical areas for improvement
• Present findings and recommendations to management and other appropriate stakeholders
• Create visuals and financial models to support any business decisions
• Train and coach staff in new systems

So, while both positions use data to make better business decisions, they take different paths up the mountain.

The Four Types of Data Analytics

All forms of data analytics fall under one of the following four categories.

Descriptive Analytics

Descriptive analytics asks, “What has happened?” Descriptive analytics doesn’t look forward but provides a comprehensive picture of past events unfolding. The chief advantage of descriptive data analysis is that it helps people understand what happened and why it happened. Typical examples include:

• Sales performance
• Dashboard reporting
• Fraud detection
• Product demand forecasts

Diagnostic Analytics

Diagnostic analytics asks, “Why did this happen?” It examines the factors that led to an event to answer why an issue occurred. This kind of analysis can help organizations understand what has happened, why, and how it can be prevented from happening again. Common examples of diagnostic analytics are:

• Root cause analysis
• Retrospective analysis
• Regression analysis

Predictive Analytics

Predictive analytics asks, “What will happen in the future?” It uses existing data to forecast future outcomes or trends. Organizations typically use predictive analytics when developing new products or services since it gives them a good guess of what their customers will want in the future based on past behavior. Examples of Predictive analytics include:

• Direct Marketing
• Customer Pricing
• Retail Sales Forecasting

Prescriptive Analytics

Prescriptive analysis asks, “What should we do?” This analysis form takes predictive analytics one step further by recommending future actions based on past trends and data. This form of data analysis is especially helpful in optimizing resources and spotting new business opportunities (e.g., expansion). Also, prescriptive analytics can be employed in making decisions or providing recommendations that let others make better decisions faster. For example, prescriptive models could recommend whether a business should:

• Launch a new product line or end a current one
• Construct a new factory or shut down an existing one
• Put in a bid on a new project, and at what price
• Hire additional staff in a given department
• Send a targeted ad to particular customers

Why is Data Analytics Important for Business?

Data and business analytics lets businesses create reports and spot patterns to help organizations operate more efficiently. Analyzing relevant data can also enhance decision-making by allowing the company to predict industry trends or customers’ wants. These predictions help companies remain on the cutting edge and stay competitive.

Data analytics can also help understand the different types of customers who visit an establishment. For example, if a restaurant discovers that most customers are families with children, it may want to emphasize more family-friendly fare. On the other hand, if students make up most of the regular clientele, the restaurant may want to offer student discounts as part of their marketing incentives.

The restaurant could also use data analytics to assess employee performance based on the sales data collected from each server. If a particular server has low daily sales, the supervisor may want to check on the employee to see if they need more training or to perform up to expectations.

We live and work in the era of big data, where today’s business leaders have access to more information than ever. Analyzing (and monetizing) this information is an essential skill for any professional involved in leadership.

The Advantages of Data Analytics in Business

Data analytics can be a valuable tool today’s businesses can leverage to stay competitive and survive in rocky financial markets. The advantages of data analytics in business include:

• Improving efficiency. Data analytics lets businesses collect vast amounts of data, which can then be analyzed and used to identify weaknesses in their business models. Companies don’t often notice inefficiency immediately because it’s easy to slip into (and perpetuate) bad habits or practices that may have worked at one point but don’t anymore. Also, organizations tend to focus on other things. However, inefficiency can cause a noticeable drain on profits and perhaps even lead to the business’s end. Efficiency is critical, but it can be challenging to spot inefficiencies. Data analytics can help.
• Making better decisions. One of the primary advantages of data analytics in business is that it helps companies make better decisions. Understanding what has occurred in the past, what is happening now, and what could happen in the future can be a game-changing advantage for any business. Companies using data analytics can predict customer behaviors and needs, making them more likely to provide the kind of goods and services that customers prefer.
• Reducing costs. Using the company’s data information is a great way to stay budget-conscious and help an organization run more efficiently, typically by pointing out underperforming elements.
• Increasing revenue. Data analytics helps businesses increase their revenue by giving them insights into making better decisions regarding pricing and product offerings. Data analysis could show that most customers who buy a particular product also tend to buy another given product. A business could bundle these two products in a discounted package deal, and customers love a good deal!
• Making the business more competitive. Data analytics allows businesses to move ahead of their competition by providing better insights into their customer base and how they can best reach them. Analytics also helps organizations identify what they’re doing wrong and how to change it.

How Can Data Analytics Improve Business Management?

Let’s run through a brief list of the ways data analytics can specifically help improve business management:

• Customer insights. Data analytics offers a deeper understanding of customer behavior, buying patterns, and preferences, allowing businesses to tailor products and services to the consumers’ needs.
• Competitive advantage . Leveraging data analytics gives businesses a competitive edge by staying current on market trends, responding quickly to changes, and outperforming their competitors.
• Informed decision-making. Data analytics lets businesses strategically use real-time insights and trends, reducing reliance on guesswork, hunches, and intuition.
• Identifying hidden opportunities. Organizations can discover otherwise hidden opportunities, emerging trends, and market gaps by analyzing large datasets, letting them acquire new revenue streams.
• Operational efficiency. Businesses can use data analytics to optimize processes and workflows, identifying inefficiencies, bottlenecks, and areas for improvement.
• Performance tracking. Businesses can monitor their key performance indicators (KPIs) via data analytics, letting them measure success, identify improvement areas, and adapt strategies as necessary.
• Personalized marketing. A company can develop customized marketing campaigns that jibe with individual preferences by analyzing customer data, leading to higher engagement and conversion rates.
• Predictive insights . Data analytics lets businesses predict future trends and outcomes, which can improve long-term planning and strategy development.
• Resource allocation. Businesses can use data to assign resources more effectively, understanding which initiatives yield the best returns, thus ensuring optimal resource utilization.
• Risk management. Data analytics helps identify risks and vulnerabilities by analyzing historical data, allowing organizations to introduce proactive risk mitigation strategies.

Data Analytics Use Cases in Business

Here are a couple of use cases for data analytics in business.

Customer Service

Without customers, businesses have nothing. Companies can employ data analytics and artificial intelligence to gain deeper insights into customer behavior. Use cases include:

• Providing customers with personalized content and specifically tailored recommendations
• Identifying common complaints that customers have regarding specific products or services
• Reducing the cost of delivering support (e.g., providing self-service options)
• Resolving issues faster and more effectively using a better understanding of customer history and needs
• Predicting what products or services the customer will probably buy next
• Automating processes like payment processing and fraud detection

Marketing and Sales

Data analytics truly shines in this area. More companies are increasingly turning to data analytics to facilitate marketing and sales. Both sectors benefit from the use of data analytics in separate ways:

• Determining the effectiveness of different advertising and marketing campaigns
• Finding the best combination of products for a given customer
• Finding the best price for a specific product or product bundle
• Identifying which customers will be most likely to respond to a given offer
• Identifying new markets for existing goods and services

Human Resources

Employees are a significant investment for any business or organization, so investing in data analytics helps management assemble a more efficient workforce.

• Analyzing employee performance, retention risks, and attrition patterns
• Assessing training and development needs
• Evaluating a training program’s effectiveness
• Determining the impact of internal promotions on employee morale
• Making better hiring/promoting decisions by analyzing past employee performance and recruitment campaigns to find the best methods for attracting top talent
• Spotting trends that highlight possible issues with staff retention

Here’s How You Can Learn More About Data Analytics

If you’re interested in learning how to apply data analytics in business, consider this 24-week data analytics bootcamp . Through live online instructor-led sessions and hands-on projects, you will learn methods of transforming raw data into actionable insights using various tools and technologies. Additionally, you will study generative AI and prompt engineering and gain practical exposure to tools such as ChatGPT, DALL-E, and Midjourney.

Indeed.com reports that data analysts can earn an annual average of $76,787. If you want a career change or want to improve your data analytics skills, check out this highly instructive online course.

Caltech Data Analytics Bootcamp

• Learning Format:

Online Bootcamp

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How to Write a Business Analysis Report [Examples and Templates]

Table of contents

Business analysis reports are a lot like preparing a delicious meal.

Sometimes, the recipe is simple enough that you only need to use the basic ingredients. Other times, you will have to follow specific instructions to ensure those tasty delicacies turn out just right.

Want to make sure your business report never turns out like a chewy piece of meat? You’ve come to the right place.

Stay tuned until the end of this blog post, and we promise you won’t be hungry… for business knowledge!

What Is a Business Analysis Report?

Why is analytical reporting important, what should be included in a business analysis report, how do you write a business analysis report, business data analysis report examples and templates.

• Improve Business Reporting with Databox

A business analysis report provides information about the current situation of your company. This report is usually created by the management to help in the decision-making process and is usually used by other departments within a company.

Business analysis reports can either focus your research on the effectiveness of an existing business process or a proposed new process. Besides, an effective business analysis report should also assess the results to determine if the process changes had a positive or negative effect on the company’s goals. In fact, according to Databox’s State of business reporting , an overwhelming majority of companies said that reporting improved their performance.

Analytical reports are the bridge that connects your company to an effective, data-driven business intelligence strategy . By leveraging analytical reports , you can make informed decisions about your organization’s most critical issues. You will no longer need to rely on gut instinct or anecdotal evidence when assessing risks, threats, and opportunities. Instead, you will have access to a wealth of reliable data to inform your decisions.

Here are some essential benefits of analytical reporting:

• Improve communication and foster collaboration – The most obvious benefit of business analysis report writing is an improvement in communication between all stakeholders involved in the project. Also, analytical business reports can help you to generate more trust and foster better collaboration among your employees and colleagues. By using data analytics reporting tools , you will be able to monitor your employees’ performance on a day-to-day basis. This will allow you to hold them accountable for their actions and give them greater freedom within the business as they know that their superiors have faith in their decision-making capabilities.
• Increase productivity – Without this level of shared insight, businesses struggle to stay on top of their most important tasks and can become less efficient. An effective analytical business report provides the information needed for more efficient internal processes and helps you find more time for strategic activities such as improving your business strategy or working on long-term goals .
• Innovation – In today’s digital age, the pressure to innovate was never greater. When consumers basically have everything they want at their fingertips, stepping up to the plate with a new and improved product or service has never been more important. With an accessible dashboard in place, you will be able to create data-driven narratives for each of your business’ critical functions. For example, if you are a software company, you can use the insights gained from report analysis done with your dashboard software to tailor your product development efforts to the actual needs of your customers. By doing so, you will be able to develop products that are better tailored to specific customer groups. You can also use the same information for developing new marketing strategies and campaigns.
• Continuous business evolution – When it comes to digital businesses, data is everything. No model lasts forever, so having access to a business dashboard software that allows you to constantly keep tabs on your business’ performance will help you refine it as time goes on. If there are any glitches in your business model, or if something isn’t panning out as expected, the insight offered by a business analysis report can help you improve upon what works while scrapping what doesn’t.

A business analysis report has several components that need to be included to give a thorough description of the topic at hand. The structure and length of business analysis reports can vary depending on the needs of the project or task.

They can be broken down into different sections that include an:

• Executive summary
• Study introduction
• Methodology
• Review of statistics

Reports of this nature may also include case studies or examples in their discussion section.

A report can be written in a formal or informal tone, depending on the audience and purpose of the document. While a formal tone is best for executives , an informal tone is more appropriate for technical audiences . It is also a good idea to use something like an executive summary template to report on the results repeatedly with ease.

A good business analysis report is detailed and provides recommendations in the form of actionable steps. Here we have listed some simple steps that you need to follow to write a good business analysis report. Report writing is a major part of the business analysis process. In this section, you will learn how to write a report for your company:

Preparation

Presentation.

Obtain an overview of what you want to analyze in the business report . For example, if you are writing a business analysis report on how to improve customer service at an insurance company, you will want to look through all the customer service processes to determine where the problems lie. The more prepared you are when starting a project, the easier it will be to get results. Here is what your preparation should look like:

Set your goals

The first step in writing this document is to set your goals . What do you hope to accomplish with this paper? Do you need to assess the company’s finances? Are you looking for ways to make improvements? Or do you have outside investors who want to know if they should buy into the company? Once you know what your goal is, then you can begin setting up your project.

PRO TIP: How Well Are Your Marketing KPIs Performing?

Like most marketers and marketing managers, you want to know how well your efforts are translating into results each month. How much traffic and new contact conversions do you get? How many new contacts do you get from organic sessions? How are your email campaigns performing? How well are your landing pages converting? You might have to scramble to put all of this together in a single report, but now you can have it all at your fingertips in a single Databox dashboard.

Our Marketing Overview Dashboard includes data from Google Analytics 4 and HubSpot Marketing with key performance metrics like:

• Sessions . The number of sessions can tell you how many times people are returning to your website. Obviously, the higher the better.
• New Contacts from Sessions . How well is your campaign driving new contacts and customers?
• Marketing Performance KPIs . Tracking the number of MQLs, SQLs, New Contacts and similar will help you identify how your marketing efforts contribute to sales.
• Email Performance . Measure the success of your email campaigns from HubSpot. Keep an eye on your most important email marketing metrics such as number of sent emails, number of opened emails, open rate, email click-through rate, and more.
• Blog Posts and Landing Pages . How many people have viewed your blog recently? How well are your landing pages performing?

Now you can benefit from the experience of our Google Analytics and HubSpot Marketing experts, who have put together a plug-and-play Databox template that contains all the essential metrics for monitoring your leads. It’s simple to implement and start using as a standalone dashboard or in marketing reports, and best of all, it’s free!

You can easily set it up in just a few clicks – no coding required.

To set up the dashboard, follow these 3 simple steps:

Step 1: Get the template 

Step 2: Connect your HubSpot and Google Analytics 4 accounts with Databox. 

Step 3: Watch your dashboard populate in seconds.

Assess the Company’s Mission

It’s almost impossible to write a business analysis report without access to the company’s mission statement. Even if you don’t plan on using the mission statement as part of your business analysis summary, it can help you understand the company’s culture and goals. Mission statements are typically short and easy to read, but they may not include every area of focus that you want to include in your report.

Thus, it is important to use other sources when possible. For example, if you are writing a business analysis report for a small start-up company that is just beginning to market its product or service, review the company website or talk directly with management to learn what they believe will be most crucial in growing the company from the ground up.

Stakeholder Analysis

Who is your audience? Create the reader’s persona and tailor all information to their perspective. Create a stakeholder map that identifies all the groups, departments, functions, and individuals involved in this project (and any other projects related to this one). Your stakeholder map should include a description of each group’s role.

Review Financial Performance

Review the financing of the business and determine whether there are any potential threats to the company’s ability to meet its future financial obligations. This includes reviewing debt payments and ownership equity compared with other types of financing such as accounts receivable, cash reserves, and working capital. Determine whether there have been any changes in the funding over time, such as an increase in long-term debt or a decrease in owners’ equity.

Apart from reviewing your debt payments and ownership equity with other types of financing, wouldn’t it be great if you could compare your financial performance to companies that are exactly like yours? With Databox, this can be done in less than 3 minutes.

For example, by  joining this benchmark group , you can better understand your gross profit margin performance and see how metrics like income, gross profit, net income, net operating increase, etc compare against businesses like yours.

One piece of data that you would be able to discover is the average gross profit a month for B2B, B2C, SaaS and eCommerce. Knowing that you perform better than the median may help you evaluate your current business strategy and identify the neccessary steps towards improving it.

Instantly and Anonymously Benchmark Your Company’s Performance Against Others Just Like You

If you ever asked yourself:

• How does our marketing stack up against our competitors?
• Are our salespeople as productive as reps from similar companies?
• Are our profit margins as high as our peers?

Databox Benchmark Groups can finally help you answer these questions and discover how your company measures up against similar companies based on your KPIs.

When you join Benchmark Groups, you will:

• Get instant, up-to-date data on how your company stacks up against similar companies based on the metrics most important to you. Explore benchmarks for dozens of metrics, built on anonymized data from thousands of companies and get a full 360° view of your company’s KPIs across sales, marketing, finance, and more.
• Understand where your business excels and where you may be falling behind so you can shift to what will make the biggest impact. Leverage industry insights to set more effective, competitive business strategies. Explore where exactly you have room for growth within your business based on objective market data.
• Keep your clients happy by using data to back up your expertise. Show your clients where you’re helping them overperform against similar companies. Use the data to show prospects where they really are… and the potential of where they could be.
• Get a valuable asset for improving yearly and quarterly planning . Get valuable insights into areas that need more work. Gain more context for strategic planning.

The best part?

• Benchmark Groups are free to access.
• The data is 100% anonymized. No other company will be able to see your performance, and you won’t be able to see the performance of individual companies either.

When it comes to showing you how your performance compares to others, here is what it might look like for the metric Average Session Duration:

And here is an example of an open group you could join:

And this is just a fraction of what you’ll get. With Databox Benchmarks, you will need only one spot to see how all of your teams stack up — marketing, sales, customer service, product development, finance, and more. 

• Choose criteria so that the Benchmark is calculated using only companies like yours
• Narrow the benchmark sample using criteria that describe your company
• Display benchmarks right on your Databox dashboards

Sounds like something you want to try out? Join a Databox Benchmark Group today!

Examine the “Four P’s”

“Four P’s” — product , price , place, and promotion . Here’s how they work:

• Product — What is the product? How does it compare with those of competitors? Is it in a position to gain market share?
• Price — What is the price of the product? Is it what customers perceive as a good value?
• Place — Where will the product be sold? Will existing distribution channels suffice or should new channels be considered?
• Promotion — Are there marketing communications efforts already in place or needed to support the product launch or existing products?

Evaluate the Company Structure

A business analysis report examines the structure of a company, including its management, staff, departments, divisions, and supply chain. It also evaluates how well-managed the company is and how efficient its supply chain is. In order to develop a strong strategy, you need to be able to analyze your business structure.

When writing a business analysis report, it’s important to make sure you structure your work properly. You want to impress your readers with a clear and logical layout, so they will be able to see the strengths of your recommendations for improving certain areas of the business. A badly written report can completely ruin an impression, so follow these steps to ensure you get it right the first time.

A typical business analysis report is formatted as a cover page , an executive summary , information sections, and a summary .

• A cover page contains the title and author of the report, the date, a contact person, and reference numbers.
• The information section is backed up by data from the work you’ve done to support your findings, including charts and tables. Also, includes all the information that will help you make decisions about your project. Experience has shown that the use of reputable study materials, such as  StuDocu  and others, might serve you as a great assistant in your findings and project tasks.
• A summary is a short overview of the main points that you’ve made in the report. It should be written so someone who hasn’t read your entire document can understand exactly what you’re saying. Use it to highlight your main recommendations for how to change your project or organization in order to achieve its goals.
• The last section of a business analysis report is a short list of references that include any websites or documents that you used in your research. Be sure to note if you created or modified any of these documents — it’s important to give credit where credit is due.

The Process of Investigation

Explain the problem – Clearly identify the issue and determine who is affected by it. You should include a detailed description of the problem you are analyzing, as well as an in-depth analysis of its components and effects. If you’re analyzing a small issue on a local scale, make sure that your report reflects this scale. That way, if someone else reads your work who had no idea about its context or scope, they would still be able to understand it.

Explain research methods – There are two ways to do this. Firstly, you can list the methods you’ve used in the report to determine your actions’ success and failure. Secondly, you should add one or two new methods to try instead. Always tell readers how you came up with your answer or what data you used for your report. If you simply tell them that the company needs to improve customer service training then they won’t know what kind of data led you to that conclusion. Also, if there were several ways of addressing a problem, discuss each one and why it might not work or why it may not be appropriate for the company at this time.

Analyze data – Analyzing data is an integral part of any business decision, whether it’s related to the costs of manufacturing a product or predicting consumer behavior. Business analysis reports typically focus on one aspect of an organization and break down that aspect into several parts — all of which must be analyzed in order to come to a conclusion about the original topic.

The Outcome of Each Investigation Stage

The recommendations and actions will usually follow from the business objectives not being met. For example, if one of your goals was to decrease costs then your recommendations would include optimization strategies for cost reduction . If you have more than one suggestion you should make a list of the pros and cons of each one. You can make several recommendations in one report if they are related. In addition, make sure that every recommendation has supporting arguments to back them up.

Report Summary

Every business analysis report should start with a summary. It’s the first thing people see and it needs to capture their attention and interest. The report summary can be created in two ways, depending on the nature of the report:

• If the report is a brief one, that simply gives a summary of the findings, then it can be created as part of the executive summary.
• But if it’s a long report, it could be too wordy to summarise. In this case, you can create a more detailed overview that covers all the main aspects of the project from both an internal and external point of view.

Everything comes down to this section. A presentation is designed to inform, persuade and influence decision-makers to take the next action steps.

Sometimes a slide or two can make them change their mind or open new horizons. These days, digital dashboards are becoming increasingly popular when it comes to presenting data in business reports. Dashboards combine different visualizations into one place, allowing users to get an overview of the information they need at a glance rather than searching through a bunch of documents or spreadsheets trying.

Databox offers dynamic and accessible digital dashboards that will help you to convert raw data into a meaningful story. And the best part is that you can do it with a ‘blink of an eye’ even if you don’t have any coding or designs skills. There is also an option of individual report customization so that you can tailor any dashboard to your own needs.

Pre-made dashboard templates can be extremely useful when creating your own business analysis report. While examples serve as inspiration, templates allow you to create reports quickly and easily without having to spend time (and money) developing the underlying data models.

Databox dashboard templates come with some of the most common pre-built metrics and KPIs different types of businesses track across different departments. In order to create powerful business insights within minutes, all you need to do is download any of our free templates and connect your data source — the metrics will populate automatically.

Business Report Examples and Templates

Databox business dashboard examples are simple and powerful tools for tracking your business KPIs and performance. These dashboards can be used by executive teams and managers as well as by senior management, marketing, sales, customer support, IT, accounting, and other departments. If you are new to this kind of reporting, you may not know how to set up a dashboard or what metrics should be displayed on it. This is where a premade template for business dashboards comes in handy.

For example, this Google Ads Report Template is designed to give you a simple way to keep track of your campaigns’ performance over time, and it’s a great resource for anyone who uses Google’s advertising platform, regardless of whether they’re an SMB, an SME or an enterprise.

KPI Report Examples and Templates

KPIs are the foundation of any business analysis, and they can come in a multitude of forms. While we’ve defined KPIs as metrics or measurements that allow you to assess the effectiveness of a given process, department, or team, there are a number of ways to evaluate your KPIs. Through the use of color-coding, user-friendly graphs and charts, and an intuitive layout, your KPIs should be easy for anyone to understand. A good way to do this is by having a dedicated business analyst on your team who can take on the task of gathering data, analyzing it, and presenting it in a way that will drive actionable insights. However, if you don’t have a dedicated analyst or don’t want to spend money on one, you can still create KPI reporting dashboards using free KPI Databox templates and examples .

For example, this Sales Overview template is a great resource for managers who want to get an overview of their sales team’s performance and KPIs. It’s perfect for getting started with business analysis, as it is relatively easy to understand and put together.

Performance Report Examples and Templates

All businesses, regardless of size or industry, need to know how well they are performing in order to make the best decisions for their company and improve overall ROI. A performance dashboard is a strategic tool used to track key metrics across different departments and provide insight into the health of a business. Databox has a collection of 50+ Performance Dashboard Examples and Templates which are available for free download.

For example, if your business is investing a lot into customer support, we recommend tracking your customer service performance with this Helpscout Mailbox Dashboard which will give you insights into conversations, your team’s productivity, customer happiness score, and more.

Executive Report Examples and Templates

An executive dashboard is a visual representation of the current state of a business. The main purpose of an executive dashboard is to enable business leaders to quickly identify opportunities, identify areas for improvement, pinpoint issues, and make data-informed decisions for driving sales growth, new product launches, and overall business growth. When an executive dashboard is fully developed, as one of these 50+ Databox Free Executive Examples and Templates , it offers a single view of the most important metrics for a business at a glance.

For example, you probably have more than one set of financial data tracked using an executive dashboard software : invoices, revenue reports (for accounting), income statements, to mention a few. If you want to view all this data in one convenient place, or even create a custom report that gives you a better picture of your business’s financial health, this Stripe Dashboard Template is a perfect solution for you.

Metrics Report Examples and Templates

Choosing the right metrics for your business dashboard can be crucial to helping you meet your business objectives, evaluate your performance, and get insights into how your business is operating. Metrics dashboards are used by senior management to measure the performance of their company on a day-to-day basis. They are also used by mid-level managers to determine how their teams are performing against individual goals and objectives. Databox provides 50+ Free Metrics Dashboard Examples and Templates that you can use to create your company’s own dashboards. Each is unique and will depend on your business needs.

For example, if you are looking for ways to track the performance of your DevOps team, and get the latest updates on projects quickly – from commits, and repository status, to top contributors to your software development projects, this GitHub Overview Dashboard is for you.

Small Business Report Examples and Templates

A lot of small business owners don’t realize how important it is to have a proper dashboard in place until they actually use one. A dashboard can help you track and compare different metrics, benchmark your performance against industry averages, evaluate the effectiveness of your marketing and sales strategies, track financials, and much more. So if you’re looking for a tool to help you measure and manage your small business’ performance, try some of these 50+ Free Small Business Dashboard Examples and Templates .

For example, this Quickbooks Dashboard template can help you get a clear understanding of your business’s financial performance, ultimately allowing you to make better-informed decisions that will drive growth and profitability.

Agency Report Examples and Templates

Agency dashboards are not a new concept. They have been around for years and are used by companies all over the world. Agency dashboards can be powerful tools for improving your marketing performance, increasing client loyalty, and landing new clients. There is no single correct way to create an agency dashboard. Everyone has their own goals and objectives, which will ultimately determine which data points you choose to include or track using a client dashboard software , but with these Databox 100+ Free Agency Dashboard Examples and Templates you have plenty of options to start with.

For example, you can use this Harvest Clients Time Report to easily see how much time your employees spend working on projects for a particular client, including billable hours and billable amount split by projects.

Better Business Reporting with Databox

Business analysis is all about finding smart ways to evaluate your organization’s performance and future potential. And that’s where Databox comes in.

Databox can be a helpful tool for business leaders who are required to analyze data, hold frequent meetings, and generate change in their organizations. From improving the quality and accessibility of your reporting to tracking critical performance metrics in one place, and sharing performance metrics with your peers and team members in a cohesive, presentable way, allow Databox to be your personal assistant in these processes, minimize the burdens of reporting and ensure you always stay on top of your metrics game.

Sign up today for free to start streamlining your business reporting process.

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15 Business Analyst Project Ideas and Examples for Practice

Explore business analyst real time projects examples curated for aspiring business analysts that will help them start their professional careers.

Your search for business analyst project examples ends here. This blog contains sample projects for business analyst beginners and professionals. So, continue reading this blog to know more about different business analyst projects ideas.

Business analysts are the demand of the twenty-first century! One can easily affirm this by looking at a report by the U.S. Bureau of Labor Statistics, which has revealed that as of May 2020, the median annual salary received by management analysts is $87,660. The bureau’s report also suggests that we are likely to witness an increase in the jobs of management analysts by 11% between 2019 and 2029. The rate is pretty higher than the average for other occupations. Additionally, the bureau mentioned that there is likely to be intense competition for such jobs because the role offers handsome salaries.

Avocado Machine Learning Project Python for Price Prediction

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The role of a business analyst primarily deals with analysing the growth of a business and suggesting methods to improve the existing strategies. Thus, to play such a crucial, one needs to possess a robust set of skills. Let us discuss a few of these to give you a more clear understanding of the skills required to become a business analyst .

Excellent verbal and written communication.

Communicate with different stakeholders and hold different meetings.

Up-to-date knowledge of new technologies and methodologies.

The capability of learning different business processes.

Ability to layout different ways of improving business growth.

Strong time management skills.

Understanding of various analytical tools and their implementation in revealing insights about the business.

Host different workshops and training sessions.

Knowledge of writing formal reports.

Having motivated you with our introduction of this blog, we now present business analyst sample projects that you can try to test/enhance your skills.

Table of Contents

Business analyst practice projects for beginners, business analyst real-time projects for intermediate professionals, advanced business analyst projects examples , top 15 business analyst project ideas for practice.

This section has beginner-friendly projects for business analyst roles that newbies in this domain can start with.

1) Market Basket Analysis  

Have you heard of the Beer-and-diapers story? In 2016, Mark Madsen, a research analyst, asked if there is a correlation between the sales of diapers and beers? It turned out that when a few stores placed beers closer to the diapers section, the beer sales went up. This strategy did not work for all the stores, but for a few, it did. By reflecting on this story, we want you to understand how important it is for a business to analyse the correlation between different purchased products, also called Market Basket Analysis.

Project Idea: In this project, you will work on a retail store’s data and learn how to realize the association between different products. Additionally, you will learn how to implement Apriori and Fpgrowth algorithms over the given dataset. You will also compare the two algorithms to understand the differences between them.

Source Code: Market basket analysis using apriori and fpgrowth algorithm  

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2) Estimating Retail Prices

For any product-selling business, deciding the price of their product is one of the most crucial decisions to make. And, thus for an aspiring business analyst, it becomes essential to understand what factors influence the decision-making process of product prices.

Project Idea: Mercari is a community-driven electronics-shopping application in Japan. In this project, you will build an automated price recommendation system using Mercari’s dataset to suggest prices to their sellers for different products based on the information collected. You will learn how to use Exploratory Data Analysis (EDA) tools and implement different machine learning algorithms like Neural Networks, Support Vector Machines, and Random Forest in R programming language. If you are specifically looking for business analyst finance planning projects for beginners , this project will be a good start. 

Source Code: Machine learning for Retail Price Recommendation with R

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3) Analyzing Customer Feedback

Collecting feedback from customers has become a norm for most companies. It provides them with the user’s perspective and guides them on what changes they should make to their product to increase its sales. Additionally, if the product reviews are public, potential customers feel motivated to trust the genuineness of the seller.

Project Idea: This project deals with the analysis of reviews of products available on an eCommerce website. You will work on textual data and implement data pre-processing methods like Gibberish Detection, Language Detection, Spelling Correction, and Profanity Detection. You will learn how to use the Random Forest model for ranking different reviews. Furthermore, you will explore the method of extracting sentiments and subjectivity from the reviews.

Source Code: Ecommerce product reviews - Pairwise ranking and sentiment analysis  

Recommended Reading: How to learn NLP from scratch in 2021?

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4) Predicting Avocado Prices

Did you know that more than 3 million new photos of avocado toasts were uploaded to Instagram every day in 2107? As per the British Vogue Magazine , this is indeed true. No doubt that so many of us enjoy avocado toasts in our breakfast. If you are also one of such people, this project idea will keep you hooked as it is all about avocados.

Project Idea: In this project, you will learn how a business analyst can use data analysis methods and help promote the growth of a business. You will work on the dataset of a Mexican-based company and layout an Avocado-price-map for them as they plan to expand their reach to different regions in the US. You will be testing the implementation of various models like the Adaboost Regressor, ARIMA time series model, and Facebook Prophet model to predict the Avocado prices.

Source Code: Avocado Price Prediction

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5) Predicting the Fate of a Loan Application

Those interested in banking projects for business analysts will indeed consider this one their favorite from this section as this project deals with loans. For understanding banks’ business model, it is crucial to learn the whole process of approving a loan application.

Project Idea: In this project, you will explore the different factors that influence the eligibility of a loan application’s approval. You will utilise different machine learning algorithms for predicting the chances of success of a loan application. This project will also help you learn about various statistical metrics used widely by business analysts like ROC curve, Gradient boosting, MCC Scorer, Synthetic Minority Over-sampling Technique, and XGBoost.

Source Code: Loan Eligibility Prediction 

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6) Predicting Customer Churn Rate

When customers start declining at an unexpected rate, various stakeholders go to business analysts for guidance. It is indeed one of the critical responsibilities of a business analyst to check the rate of customers churning out.

Project Idea: This project will guide you about performing univariate and bivariate analysis on the given dataset of a bank. You will learn how different statistical methods like SHAP (SHapley Additive exPlanations), RandomSearch, GridSearch, etc. should be used and interpreted. This project is another instance of a banking project for business analysts . So, if that’s your bias in sample business analysis projects , do check this one out. Source Code: Customer Churn Prediction

Recommended Reading: 

• Is Data Science Hard to Learn? (Answer: NO!)
• 15 Machine Learning Projects GitHub for Beginners in 2021
• Access Job Recommendation System Project with Source Code

After you have completely solved the above-mentioned projects, proceed to the sample business analyst projects listed in this section to further enhance your skills. These projects are slightly more challenging as they are closer to real-world problems. So, please refer to the source code links for help.

Explore SQL Database Projects to Add them to Your Data Engineer Resume.

7) Prediction of Selling Price for different Products

You must have noticed a few brands sometimes send their loyal customers' coupon codes to attract them. These coupons are often customized according to their purchase history with the brand and thus the offer varies from customer to customer.

Project Idea: In this project, you will work on the dataset of a retail company to estimate the price at which a customer is likely to buy a specific product. Once that is complete, you will use your estimation to design offers for different customers. For the solution, you will use machine learning algorithms like Gradient Boosting Machines (GBM), XGBoost, Random Forest, and Neural Networks and use different metrics to test each of their performances.

You can add this project under the heading of business analyst finance projects on your resume to highlight the diversity of your skillset.

Source Code : Predict purchase amount of customers against various products

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8) Store Sales Prediction

In most firms, investors are usually external stakeholders that are not directly involved in the firm’s business but are definitely affected by it. And, it is the business analyst’s responsibility to keep the investors up-to-date with the existing and expected growth of the firm’s business model.

Project Idea: In this project, you will work on the dataset of 45 stores of the famous Walmart store chain. The goal is to predict the sales and revenue of different stores based on historical data. You will work with numeric and categorical feature variables and perform univariate & bivariate analysis to find the redundancy in variables. Additionally, you will learn the implementation of the ARIMA time series model and other machine learning models.

Source Code: Walmart Store Sales Forecasting

9) Analyzing Customer Churn

 It's the customer who pays the wages. --Henry Ford

Customer churn is painful for all the stakeholders in a company. A business analyst must thus look for ways in which the customer churn rate can be minimised. Additionally, they have to identify the cause behind customer churn to improving business growth. Having a fair idea of which customer is likely to churn out will help a business analyst develop better strategies.

Project Idea: In this project, you will be introduced to one of the popular classification machine learning algorithms , logistic regression. The goal is to use logistic regression for estimating the chances of churn for each customer. Through this project, you will get to explore different statistical methods, including confusion metric, recall, accuracy, precision, f1-score, AUC, and ROC.

Source Code: Churn Analysis for Streaming App using Logistic Regression

10) Estimating Future Inventory Demand

While inventory management does not directly fall in the bucket of a business analyst’s responsibilities, one may still find it there as inventory demand directly impacts several other aspects of a business including sales, marketing , finance, etc. With so many advancements taking place in the IT industry, a business analyst can easily use various tools to forecast the inventory demand. Project Idea: Through this project, you will explore the application of various machine learning models, including Bagging, Boosting, XGBoost, GBM, light GBM, and SVM for predicting the inventory demand of a bakery. This project will also introduce you to the implementation of autoML/H 2 0 and LSTM models.

Source Code: Inventory Demand Forecasting using Machine Learning in R

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11) Predicting Coupon Sales

In the previous section, we mentioned a project that will help you in creating customised coupons for a business’s customers. The next step will be to keep track of which coupons have been purchased. This will further help in understanding customer behaviour and preferences.

Project Idea: In this project, you will work on the dataset of one of Japan’s famous joint coupon websites, Recruit Ponpare. The goal is to estimate which coupons a customer is likely to buy based on their previous purchases and browsing behaviour on the website. You will use different graphical methods to visualise the data and various methods of handling missing values in a dataset. You will evaluate the cosine similarities of coupons and users and use them to make the desired predictions.

Source Code: Build a Coupon Purchase Prediction Model in R

12) Creating Product Bundles

Often when we visit a McDonald’s outlet, we intend to buy only a burger, but when we look at the meal menu, we end up buying the full mean instead of a single burger. This method of combining a few products and selling them as a single unit is called product bundling. It helps in increasing the sales of a business.

Project Idea: In this project, you will identify product bundles from the given sales data. While market basket analysis is commonly used for solving such problems, you will be using the time series clustering method. The two techniques will be compared to understand the significance of both methods.

Source Code: Identify Product Bundles from Sales Data

Recommended Reading: 50 Business Analyst Interview Questions and Answers

Professional Business Analysts planning to aim for senior roles will find business analyst projects samples in this section. A senior business analyst is often expected to possess knowledge of Big Data tools . Thus, you will find the projects described below rely on these tools.

13) Analyzing Log Files

If you are new to Big data projects and want to learn the basics of data analysis using Hive, then this project will be a good start. This simple project has been added to this section to prepare you for the next two projects.

Project Idea: This project is simply about analyzing log files of different users of a website. You will learn how to use Apache Hive to extract meaningful data insights by executing real-time queries.

Source Code: Hive Sample Projects-Learn data analysis using sample data for Hive

14) Retain Analytics

Retail Analytics refers to the complete analysis of various aspects of a business, including customer behavior and demands, supply chain analysis, sales, marketing, and inventory management. Such deeper analysis assists in deeply understanding the business model and smoothens various decision-making processes.

Project Idea: In this project, you will work with the Walmart stores dataset and use various Big Data techniques and tools to perform retail analytics. You will explore how to use tools like AWS EC2, Docker -composer, HDFS, Apache Hive, and MySQL for implementing the full solution.

Source Code: Retail Analytics Project Example using Sqoop, HDFS, and Hive

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15) Analyzing Airline Data

Data has become a huge asset for many industries, and the airline industry is no exception. They rely on big data to answer a few of the most vital questions like when the customers are likely to witness minimum delay in flight timings? Are older planes more prone to delays? etc. Project Idea: For this project, you will work on the dataset of an airline and find answers to questions like the ones mentioned above. You will be guided on how to ingest data and extract it using Cloudera VMware. After that, you will learn about preprocessing the data using Apache Pig. Next, you will use Hive for making tables and performing Exploratory Data Analysis. You will also get to explore the application of HCatloader and parquet through this project. Source Code: Hadoop Hive Project on Airline Dataset Analysis

Hey, Hey! The blog hasn’t ended yet. Going by what Steve Jobs said. “ ‘Learn continually. There's always “one more thing” to learn.’, we don’t want your learning journey to stop here. Check out more such Data Science Projects and Big Data projects from our repository to work on more exciting projects like the ones discussed in this blog.

About the Author

Manika Nagpal is a versatile professional with a strong background in both Physics and Data Science. As a Senior Analyst at ProjectPro, she leverages her expertise in data science and writing to create engaging and insightful blogs that help businesses and individuals stay up-to-date with the

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• SPSS for Business Analytics: Student's Comprehensive Guide

SPSS for Business Analytics: A Curriculum-Based Approach

In the ever-evolving landscape of business, the significance of data in decision-making cannot be overstated. As data volumes and intricacies burgeon, the demand for robust analytics tools becomes paramount. Among these, IBM's Statistical Package for the Social Sciences (SPSS) has emerged as a prominent player in the realm of business analytics. This blog is dedicated to equipping students with a comprehensive grasp of SPSS, emphasizing a curriculum-based approach tailored to empower them not only in tackling assignments but also in excelling in the multifaceted domain of business analytics. Whether you require help with your SPSS assignment or seek to master the intricacies of business analytics using SPSS, this blog serves as a valuable resource to enhance your understanding and proficiency in leveraging data for informed decision-making in the business world.

Understanding SPSS involves navigating its user-friendly interface and harnessing its diverse statistical techniques. Through a structured curriculum, students will gain proficiency in installing and setting up SPSS, mastering the basics of data entry and management, and advancing to exploratory data analysis (EDA). Building upon this foundation, the blog will delve into advanced analytics, covering inferential statistics, regression analysis, and multivariate analysis techniques. Real-world applications and case studies will illustrate how SPSS is applied to address business challenges, providing students with tangible insights.

This comprehensive curriculum-based approach ensures that students not only grasp the theoretical aspects of SPSS but also develop practical skills that are invaluable in solving assignments and making informed decisions in the dynamic landscape of business analytics.

Understanding the Basics of SPSS

SPSS, or the Statistical Package for the Social Sciences, stands as a powerful ally in the world of business analytics. To embark on a fruitful journey with SPSS, it's imperative to delve into its fundamental aspects. This section will serve as a compass, guiding students through the initial steps of installing and setting up SPSS. By understanding the system requirements and navigating the licensing process, students can ensure a smooth initiation into the realm of statistical analysis.

Once the groundwork is laid, the focus shifts to the basics of data entry and management. This involves creating a structured data file, entering information accurately, and learning the intricacies of variable types and properties. A meticulous approach to data cleaning and transformation is emphasized, as clean datasets are the bedrock for meaningful analysis.

Moving beyond mere data entry, students will explore the realm of exploratory data analysis (EDA). This involves techniques to summarize and visualize data effectively, such as generating descriptive statistics and creating visual representations. This section acts as a springboard, propelling students into the intricate world of SPSS analytics with a solid understanding of its foundational elements.

Overview of SPSS

SPSS, an acronym for the Statistical Package for the Social Sciences, stands as a testament to IBM's commitment to providing a robust statistical software package. Developed by IBM, this tool has become a cornerstone in the world of data analysis. Initially tailored for applications in the social sciences, SPSS has undergone a transformative evolution, adapting itself into a versatile analytical tool applicable across diverse disciplines, with a particular stronghold in business analytics. Its widespread adoption can be attributed not only to its origin in academia but also to its user-friendly interface and an extensive repertoire of statistical techniques.

Installation and Setup

Before students embark on their analytical journey with SPSS, a foundational step involves acquainting themselves with the installation and setup process. This phase serves as a gateway to the myriad capabilities of SPSS, ensuring that users can seamlessly integrate the software into their systems. Navigating through this section, students will be guided through a step-by-step process, demystifying the complexities of SPSS installation. Armed with this knowledge, they can confidently initiate their statistical analyses, laying a solid groundwork for their exploration of the software's capabilities in subsequent modules. This understanding of installation and setup is pivotal, forming the bedrock for a comprehensive grasp of SPSS functionalities.

1: System Requirements

Understanding the system requirements is a crucial initial step in the SPSS journey. For optimal performance, students must acquaint themselves with the specific prerequisites for running SPSS on their systems. This encompasses key details about compatible operating systems, minimum memory specifications, and required disk space. By meticulously adhering to these system requirements, students pave the way for a seamless SPSS experience. This not only prevents potential technical issues but also lays the foundation for efficient data analysis, ensuring that their focus remains on deriving meaningful insights rather than grappling with software compatibility challenges.

2: Licensing and Activation

The prowess of SPSS comes to life through a valid license and activation. This H3 subsection guides students through the crucial process of obtaining and activating their SPSS license. Clear and concise instructions will be provided, empowering students to unlock the full spectrum of SPSS capabilities for their assignments. A valid license not only ensures compliance but also grants access to the myriad statistical tools within SPSS, enabling students to explore, analyze, and derive valuable insights from their datasets. This foundational knowledge of licensing and activation serves as a gateway to a rich and comprehensive SPSS experience, facilitating a seamless integration of this powerful tool into their analytical toolkit.

Building a Strong Foundation in SPSS

In the intricate realm of data analytics, establishing a robust foundation is paramount, and this holds particularly true for mastering the capabilities of SPSS. As we venture into the various facets of building this foundation, the focus shifts to equipping students with the fundamental skills that serve as the building blocks for proficient SPSS utilization.

Basics of Data Entry and Management

Before diving into advanced analytics, students need to master the basics of data entry and management in SPSS. This section becomes the cornerstone of their SPSS journey, as it covers essential topics such as creating a data file, entering data, and organizing variables. A hands-on approach will be emphasized, allowing students to actively engage with the software and develop practical skills that extend beyond theoretical understanding.

As students delve into the nuances of data entry, they will learn the importance of maintaining data integrity and cleanliness. Creating a structured data file lays the groundwork for efficient analysis, and understanding variable types and properties becomes pivotal. This hands-on exploration ensures that students not only comprehend the theoretical aspects but also gain confidence in navigating SPSS for effective data management—a skill set indispensable in the broader landscape of business analytics.

1: Variable Types and Properties

Understanding different variable types is fundamental to effective data management. In SPSS, variables can be numeric or categorical, each serving distinct roles in analysis. This subsection elucidates these distinctions, guiding students on how to assign variable properties such as labels and formats. This knowledge proves invaluable in shaping the data for subsequent analyses, ensuring accuracy and relevance in the interpretation of results.

2: Data Cleaning and Transformation

The journey towards insightful analysis begins with clean and organized data. This subsection focuses on the critical aspect of data cleaning and transformation. Students will delve into techniques for identifying and handling missing values, outliers, and other anomalies that could skew results. Furthermore, the section delves into data transformation methods, equipping students with the skills needed to prepare datasets for advanced statistical analysis. As students progress in their SPSS journey, these foundational practices become indispensable in ensuring the reliability and validity of their analyses.

Exploratory Data Analysis (EDA) with SPSS

Exploratory Data Analysis (EDA) stands as a critical pillar in the analytics process, offering students a profound insight into the intricacies of their datasets. In this section, we will meticulously lead students through a comprehensive journey within SPSS, unraveling a diverse array of techniques designed for both data summarization and visualization. Within the realm of descriptive statistics, students will explore the nuances of mean, median, and standard deviation, gaining a nuanced understanding of their data's central tendencies and dispersion. Furthermore, this segment will delve into the realm of frequency distributions, unveiling the distribution patterns of categorical variables. The journey continues with the exploration of graphical representations, where students will harness the power of histograms, boxplots, and scatterplots to visually encapsulate the essence of their data. By fostering a deep connection between theory and practical application, this section ensures that students are well-equipped to derive meaningful insights from their datasets using the versatile tools embedded in SPSS.

1: Creating Descriptive Statistics

In the realm of statistical analysis, creating descriptive statistics serves as a foundational step for extracting meaningful insights from datasets. SPSS, as a robust tool, empowers students with a diverse set of tools to delve into the numerical characteristics of their data. This includes computing key metrics such as mean, median, and standard deviation, which not only provide a snapshot of central tendencies but also illuminate the variability within the data. By adeptly navigating these functions, students gain a deeper understanding of their datasets, enabling them to identify subtle patterns or trends that may inform subsequent analyses.

2: Data Visualization in SPSS

Moving beyond numerical summaries, the art of data visualization takes center stage in SPSS. Visualizing data is a powerful means of conveying complex information in an accessible format. Within SPSS, students can explore a plethora of visualization options, including the creation of visually compelling histograms, informative boxplots, and insightful scatterplots. This subsection will guide students on how to harness SPSS's visualization capabilities effectively, emphasizing the importance of selecting the most appropriate method based on the unique characteristics of their data and the specific analytical goals they aim to achieve. Through hands-on exploration, students will discover the transformative impact of visual representation in enhancing the interpretability of their datasets.

Advanced Analytics with SPSS

Once students have grasped the fundamental aspects of SPSS, the journey into advanced analytics becomes both exciting and essential for a comprehensive understanding of statistical methodologies. This section delves into the sophisticated tools SPSS offers, empowering students to unravel intricate insights from complex datasets.

In this advanced analytics realm, SPSS acts as a potent ally, guiding students through inferential statistics, where they transition from merely describing data to making informed predictions about populations. Through techniques like t-tests, ANOVA, and regression analysis, students gain the prowess to draw meaningful conclusions from their data.

As the learning journey progresses, students explore multivariate analysis techniques, such as factor analysis, cluster analysis, and discriminant analysis. These tools enable them to unravel hidden patterns, classify cases, and derive nuanced insights that go beyond the capabilities of basic statistical methods.

Through a curriculum-based approach, this section equips students not just with technical skills but with a strategic mindset, preparing them to tackle complex business challenges and contribute meaningfully to the evolving landscape of data-driven decision-making. Advanced analytics with SPSS becomes a gateway to unlocking the full potential of statistical analysis in diverse professional domains.

Inferential Statistics in SPSS

With a robust foundation established, students can seamlessly advance to the realm of inferential statistics within the SPSS environment. Inferential statistics serve as a powerful gateway, allowing students to extrapolate conclusions about broader populations based on carefully sampled data. In this comprehensive section, we will delve into the intricacies of various inferential statistical techniques that SPSS offers, providing students with a nuanced understanding of tools like t-tests, ANOVA, and regression analysis.

1: Conducting Hypothesis Tests

In the realm of hypothesis testing, students will learn to formulate precise hypotheses, select the appropriate test within SPSS, and interpret results effectively. Practical examples and step-by-step guidance will empower students to confidently apply hypothesis testing, a fundamental skill in drawing meaningful inferences from their data.

2: Regression Analysis in SPSS

The exploration of regression analysis will extend beyond the basics, encompassing advanced concepts such as multiple regression and logistical regression. Through hands-on exercises, students will gain proficiency in employing regression models within SPSS, enabling them to analyze complex relationships and make informed predictions based on their data. This in-depth coverage ensures that students not only grasp the theoretical underpinnings but also acquire practical skills for real-world applications.

Multivariate Analysis Techniques

To tackle complex business challenges, students need to explore multivariate analysis techniques. This section will introduce methods such as factor analysis, cluster analysis, and discriminant analysis available in SPSS, providing students with a diverse toolkit for advanced analytics.

1: Factor Analysis in SPSS

Factor analysis is employed to identify underlying factors that explain patterns of correlations among variables. Students will learn how to perform factor analysis in SPSS, interpret factor loadings, and make informed decisions based on the results.

2: Cluster Analysis and Discriminant Analysis

Cluster analysis helps identify natural groupings within data, while discriminant analysis is useful for classifying cases into predefined groups. This subsection will delve into the application of these techniques using SPSS, equipping students with the skills to solve assignments involving complex datasets.

Applying SPSS to Business Challenges

In this section, we delve into compelling real-world applications and case studies that vividly demonstrate the practical relevance of SPSS in addressing diverse business challenges. From marketing analytics optimizing customer engagement to financial analytics mitigating risks in volatile markets, each case study illuminates the strategic use of SPSS. These examples not only showcase the versatility of SPSS but also inspire students to bridge the gap between theory and application, empowering them to approach assignments with a practical mindset honed by insights from actual industry scenarios. Let's explore the transformative impact of SPSS on business analytics through these illuminating case studies.

1: Marketing Analytics with SPSS

This subsection will focus on how SPSS can be applied in marketing analytics, covering topics such as customer segmentation, market basket analysis, and predictive modeling. Students will gain insights into how businesses use SPSS to optimize marketing strategies and enhance customer engagement.

2: Financial Analytics and Risk Management

In the realm of finance, SPSS proves valuable for risk assessment, fraud detection, and financial forecasting. Case studies in this section will highlight how financial institutions utilize SPSS to make informed decisions and mitigate risks in a volatile market.

In conclusion, achieving mastery in SPSS for business analytics requires a deliberate and curriculum-focused strategy. This blog has meticulously outlined a roadmap, encompassing fundamental SPSS principles, the establishment of a robust foundation, delving into advanced analytics, and practical applications to real-world business challenges. By diligently following this comprehensive guide, students can not only navigate the intricacies of SPSS with confidence but also adeptly tackle assignments with a profound understanding of statistical methodologies. This proficiency positions them to make meaningful contributions to the evolving landscape of business analytics, where data-driven decision-making is paramount. Embracing the systematic approach outlined here empowers students to not only succeed academically but also to apply their SPSS skills pragmatically, making a lasting impact in diverse industries that increasingly rely on analytics for informed and strategic choices.

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Spss for business analytics: student's comprehensive guide submit your assignment, attached files.

6.894 : Interactive Data Visualization

Assignment 2: exploratory data analysis.

In this assignment, you will identify a dataset of interest and perform an exploratory analysis to better understand the shape & structure of the data, investigate initial questions, and develop preliminary insights & hypotheses. Your final submission will take the form of a report consisting of captioned visualizations that convey key insights gained during your analysis.

Step 1: Data Selection

First, you will pick a topic area of interest to you and find a dataset that can provide insights into that topic. To streamline the assignment, we've pre-selected a number of datasets for you to choose from.

However, if you would like to investigate a different topic and dataset, you are free to do so. If working with a self-selected dataset, please check with the course staff to ensure it is appropriate for the course. Be advised that data collection and preparation (also known as data wrangling ) can be a very tedious and time-consuming process. Be sure you have sufficient time to conduct exploratory analysis, after preparing the data.

After selecting a topic and dataset – but prior to analysis – you should write down an initial set of at least three questions you'd like to investigate.

Part 2: Exploratory Visual Analysis

Next, you will perform an exploratory analysis of your dataset using a visualization tool such as Tableau. You should consider two different phases of exploration.

In the first phase, you should seek to gain an overview of the shape & stucture of your dataset. What variables does the dataset contain? How are they distributed? Are there any notable data quality issues? Are there any surprising relationships among the variables? Be sure to also perform "sanity checks" for patterns you expect to see!

In the second phase, you should investigate your initial questions, as well as any new questions that arise during your exploration. For each question, start by creating a visualization that might provide a useful answer. Then refine the visualization (by adding additional variables, changing sorting or axis scales, filtering or subsetting data, etc. ) to develop better perspectives, explore unexpected observations, or sanity check your assumptions. You should repeat this process for each of your questions, but feel free to revise your questions or branch off to explore new questions if the data warrants.

• Final Deliverable

Your final submission should take the form of a Google Docs report – similar to a slide show or comic book – that consists of 10 or more captioned visualizations detailing your most important insights. Your "insights" can include important surprises or issues (such as data quality problems affecting your analysis) as well as responses to your analysis questions. To help you gauge the scope of this assignment, see this example report analyzing data about motion pictures . We've annotated and graded this example to help you calibrate for the breadth and depth of exploration we're looking for.

Each visualization image should be a screenshot exported from a visualization tool, accompanied with a title and descriptive caption (1-4 sentences long) describing the insight(s) learned from that view. Provide sufficient detail for each caption such that anyone could read through your report and understand what you've learned. You are free, but not required, to annotate your images to draw attention to specific features of the data. You may perform highlighting within the visualization tool itself, or draw annotations on the exported image. To easily export images from Tableau, use the Worksheet > Export > Image... menu item.

The end of your report should include a brief summary of main lessons learned.

Recommended Data Sources

To get up and running quickly with this assignment, we recommend exploring one of the following provided datasets:

World Bank Indicators, 1960–2017 . The World Bank has tracked global human developed by indicators such as climate change, economy, education, environment, gender equality, health, and science and technology since 1960. The linked repository contains indicators that have been formatted to facilitate use with Tableau and other data visualization tools. However, you're also welcome to browse and use the original data by indicator or by country . Click on an indicator category or country to download the CSV file.

Chicago Crimes, 2001–present (click Export to download a CSV file). This dataset reflects reported incidents of crime (with the exception of murders where data exists for each victim) that occurred in the City of Chicago from 2001 to present, minus the most recent seven days. Data is extracted from the Chicago Police Department's CLEAR (Citizen Law Enforcement Analysis and Reporting) system.

Daily Weather in the U.S., 2017 . This dataset contains daily U.S. weather measurements in 2017, provided by the NOAA Daily Global Historical Climatology Network . This data has been transformed: some weather stations with only sparse measurements have been filtered out. See the accompanying weather.txt for descriptions of each column .

Social mobility in the U.S. . Raj Chetty's group at Harvard studies the factors that contribute to (or hinder) upward mobility in the United States (i.e., will our children earn more than we will). Their work has been extensively featured in The New York Times. This page lists data from all of their papers, broken down by geographic level or by topic. We recommend downloading data in the CSV/Excel format, and encourage you to consider joining multiple datasets from the same paper (under the same heading on the page) for a sufficiently rich exploratory process.

The Yelp Open Dataset provides information about businesses, user reviews, and more from Yelp's database. The data is split into separate files ( business , checkin , photos , review , tip , and user ), and is available in either JSON or SQL format. You might use this to investigate the distributions of scores on Yelp, look at how many reviews users typically leave, or look for regional trends about restaurants. Note that this is a large, structured dataset and you don't need to look at all of the data to answer interesting questions. In order to download the data you will need to enter your email and agree to Yelp's Dataset License .

Additional Data Sources

If you want to investigate datasets other than those recommended above, here are some possible sources to consider. You are also free to use data from a source different from those included here. If you have any questions on whether your dataset is appropriate, please ask the course staff ASAP!

• data.boston.gov - City of Boston Open Data
• MassData - State of Masachussets Open Data
• data.gov - U.S. Government Open Datasets
• U.S. Census Bureau - Census Datasets
• IPUMS.org - Integrated Census & Survey Data from around the World
• Federal Elections Commission - Campaign Finance & Expenditures
• Federal Aviation Administration - FAA Data & Research
• fivethirtyeight.com - Data and Code behind the Stories and Interactives
• Buzzfeed News
• Socrata Open Data
• 17 places to find datasets for data science projects

Visualization Tools

You are free to use one or more visualization tools in this assignment. However, in the interest of time and for a friendlier learning curve, we strongly encourage you to use Tableau . Tableau provides a graphical interface focused on the task of visual data exploration. You will (with rare exceptions) be able to complete an initial data exploration more quickly and comprehensively than with a programming-based tool.

• Tableau - Desktop visual analysis software . Available for both Windows and MacOS; register for a free student license.
• Data Transforms in Vega-Lite . A tutorial on the various built-in data transformation operators available in Vega-Lite.
• Data Voyager , a research prototype from the UW Interactive Data Lab, combines a Tableau-style interface with visualization recommendations. Use at your own risk!
• R , using the ggplot2 library or with R's built-in plotting functions.
• Jupyter Notebooks (Python) , using libraries such as Altair or Matplotlib .

Data Wrangling Tools

The data you choose may require reformatting, transformation or cleaning prior to visualization. Here are tools you can use for data preparation. We recommend first trying to import and process your data in the same tool you intend to use for visualization. If that fails, pick the most appropriate option among the tools below. Contact the course staff if you are unsure what might be the best option for your data!

Graphical Tools

• Tableau Prep - Tableau provides basic facilities for data import, transformation & blending. Tableau prep is a more sophisticated data preparation tool
• Trifacta Wrangler - Interactive tool for data transformation & visual profiling.
• OpenRefine - A free, open source tool for working with messy data.

Programming Tools

• JavaScript data utilities and/or the Datalib JS library .
• Pandas - Data table and manipulation utilites for Python.
• dplyr - A library for data manipulation in R.
• Or, the programming language and tools of your choice...

The assignment score is out of a maximum of 10 points. Submissions that squarely meet the requirements will receive a score of 8. We will determine scores by judging the breadth and depth of your analysis, whether visualizations meet the expressivenes and effectiveness principles, and how well-written and synthesized your insights are.

We will use the following rubric to grade your assignment. Note, rubric cells may not map exactly to specific point scores.

Submission Details

This is an individual assignment. You may not work in groups.

Your completed exploratory analysis report is due by noon on Wednesday 2/19 . Submit a link to your Google Doc report using this submission form . Please double check your link to ensure it is viewable by others (e.g., try it in an incognito window).

Resubmissions. Resubmissions will be regraded by teaching staff, and you may earn back up to 50% of the points lost in the original submission. To resubmit this assignment, please use this form and follow the same submission process described above. Include a short 1 paragraph description summarizing the changes from the initial submission. Resubmissions without this summary will not be regraded. Resubmissions will be due by 11:59pm on Saturday, 3/14. Slack days may not be applied to extend the resubmission deadline. The teaching staff will only begin to regrade assignments once the Final Project phase begins, so please be patient.

• Due: 12pm, Wed 2/19
• Recommended Datasets
• Example Report
• Visualization & Data Wrangling Tools
• Submission form

DATA 275 Introduction to Data Analytics

• Getting Started with SPSS
• Variable View
• Option Suggestions
• SPSS Viewer
• Entering Data
• Cleaning & Checking Your SPSS Database
• Recoding Data: Collapsing Continuous Data
• Constructing Scales and Checking Their Reliability
• Formatting Tables in APA style
• Creating a syntax
• Public Data Sources

Data Analytics Project Assignment

• Literature Review This link opens in a new window

For your research project you will conduct data analysis and right a report summarizing your analysis and the findings from your analysis. You will accomplish this by completing a series of assignments. 

Data 275 Research Project Assignment

In this week’s assignment, you are required accomplish the following tasks:

1. Propose a topic for you project

The topic you select for your capstone depends on your interest and the data problem you want to address. Try to pick a topic that you would enjoy researching and writing about.

Your topic selection will also be influenced by data availability. Because, this is a data analytics project, you will need to have access to data. If you have access to your organization’s data, you are free to use it. If you choose to do so, all information presented must be in secure form because Davenport University does not assume any responsibility for the security of corporate data. Otherwise, you can select a topic that is amenable to publicly available data.

Click the link for some useful suggestions: Project Proposal Suggestions 

2. Find a data set of your interest and download it

There are many publicly available data sets that you can use for your project. The library has compiled a list of many possible sources of data. Click on the link below to explore these sources. 

Public Data Sources 

The data set you select must have:

At least 50 observations (50 rows) and at least 4 variables (columns) excluding identification variables At least one dependent variable

You must provide:

A proper citation of the data source using APA style format A discussion on how the data was collected and by whom The number of variables in the data set The number of observations/subjects in the data set A description of each variable together with an explanation of how it is measured (e.g. the unit of measurement).

Deliverable

A minimum of one page description of your data analytics project which must include the following:

A title for your project A brief description of the project Major stakeholders who would use the information that would be generated from your analysis and how they would use/benefit from that information A description of the dataset you will use for your project

• << Previous: Public Data Sources
• Next: Literature Review >>
• Last Updated: Mar 15, 2024 10:33 AM
• URL: https://davenport.libguides.com/data275

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.

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.

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.

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.

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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Business Analytics Assignment On The Consumption Of Cosmetics

Task 1- Background information Write a description of the selected dataset and project, and its importance for your chosen company. Information must be appropriately referenced.

Task 2 – Perform Data Mining on data view Upload the selected dataset on SAP Predictive Analysis. For your dataset, perform the relevant data analysis tasks on data uploaded using data mining techniques such as classification/association/time series/clustering and identify the BI reporting solution and/or dashboards you need to develop for the operational manager of the chosen company

Task 3 – Research Justify why you chose thee BI reporting solution/dashboards/data mining technique in Task 3 and why those data sets attributes are present and laid out in the fashion you proposed (feel free to include all other relevant justifications).

Note: To ensure that you discuss this task properly, you must include visual samples of the reports you produce (i.e. the screenshots of the BI report/dashboard must be presented and explained in the written report; use ‘Snipping tool’), and also include any assumptions that you may have made about the analysis from Task 3.

Task 4 – Recommendations for CEO The CEO of the chosen company would like to improve their operations. Based on your BI analysis and the insights gained from your “Dataset” in the lights of analysis performed in previous tasks, make some logical recommendations to the CEO, and justify why/how your proposal could assist in achieving operational/strategic objectives with the help of appropriate references from peer-reviewed sources.

Task 5 – Cover letter Write a cover letter to the CEO of the chosen firm with the important data insights and recommendations to achieve operational/strategic objectives.

Other Tasks – Please refer to the marking scheme at the end of the assignment for other tasks and expectations.

1.0 Introduction The excellence business has kept on developing and flourish as of late, with the monetary downturn doing little to decrease British purchasers' energy and hunger for new items and creative increments to their own consideration routines. Yet, it's not simply items that are developing. Clients' preferences and wishes are likewise continually in motion.

Excellence as an idea, not to mention an industry, has experienced immense changes throughout the years. What we see to be delightful, in vogue and even worthy is continually moving, starting patterns and styles that are presently everlastingly connected with a minute in time. You just need to think back to the excitement of the forties or the unmistakable style of the sixties to perceive how much changes in only a couple of decades, and usually these looks and patterns that we partner with those times as much as any verifiable or social occasion.

The drivers behind these progressions discussed in this business analytics assignment are regularly driven by the business, with design houses or famous people managing a specific style or look which is then received and spread by real brands. In any case, we ought not belittle the impact or capability of clients themselves, and how their inclinations and necessities can manage the bearing that the business takes as far as item advancement.

2.0 Project Overview With the end goal of this business analytics assignment we are concentrating exclusively on the female market. In this way our potential clients base (to create projections) does exclude any insights or arrangements for male purchasers. We have utilized the statistic report for spa goers directed by spa week by week as a reason for our suppositions. In view of this study the spa goer is overwhelmingly female (85%), knowledgeable (46% went to school), and crosses salary levels (26% gain under $35,000; 32% win somewhere in the range of $35,000 and $74,999 and 42% procure over $75,000) (Laursen and Thorlund, 2016).

Utilizing this statistic as the reason for our approach analyzed in this assignment on business analytics we built up our potential client base with the accompanying parameters: Women with some school between the ages of 25 and 65. We totally limited ladies with no school, ladies somewhere in the range of 18 and 25, ladies more than 65 and the whole male populace (Holsapple et al. 2014).. It is evaluated that the female populace will develop at a rate of 5.18% every year from 2000 to 2025 (source: the U.S. evaluation department). This information is for the whole United States. Of the 33,642,000 ladies spoke to between the ages of 25 and 65 who went to school, 29,293,000 (87%) live in major CMSA's.3.0 Analytical Solution

Restorative excellence or cosmetics items are compound blends that are utilized to improve scent or presence of the human body. Aromas, shading and cosmetics beauty care products, antiperspirants, haircare, healthy skin, and sun care are sure items that are broadly accessible and are utilized by people. Retail locations that incorporate claim to fame stores, restrictive brand outlets and general stores are the significant dissemination channels. In the present period, online channels are additionally picking up fame among the clients.

Individual consideration and magnificence item deals are on the ascent and are anticipated to enroll a development from 3.5 to 4.5% somewhere in the range of 2015 and 2020. It is foreseen to reach USD 500 billion by 2020. The Asia Pacific records for a noteworthy offer in the worldwide individual consideration industry; expanding request in the district is ascribed to its protruding populace. In the U.S, developing Hispanic populace is driving interest for sumptuous individual consideration marks and will raise amid the conjecture years (Holsapple et al. 2014).

Excellence or corrective items industry is one of the segments that stayed unaffected, regardless of the variances in the economy. Corrective deals have kept up a specific volume all through its general items. The deal can be ascribed to expanding and reliable utilization of items, particularly by people. Individual consideration organizations are making their items accessible online at focused costs. The web affects each business class be it antiperspirant or shaving items. Clients are eager to buy the products that can come legitimately to them through internet retailing (Acito and Khatri, 2014).

Various clients are worried about natural effect of the products they use. In this manner, makers are tricking their potential purchasers by promoting their items as natural and manageable. These qualities are even featured on items names and are expanding their image prominence as discussed in this assignment on business analytics. Moreover, a moral segment of the business includes client's worries about the items testing on creatures (Dubey and Gunasekaran, 2015). Makers are dealing with every one of these elements for advancing their items and profit the advantages of such worthwhile industry.

Worldwide corrective items advertise is ordered as healthy skin items, hair care items, shading beautifying agents, scents, individual consideration items, and oral consideration items. Skincare item is foreseen to overwhelm the worldwide corrective items advertise amid the conjecture time frame attributable to its various variations, for example, cosmetics remover, depilatories, hand care, and facial consideration. In light of structure, worldwide restorative items advertise is ordered into arrangements, creams, moisturizers, salves, suspensions, tablets, powders, gels, sticks, and pressurized canned products. Gels are anticipated to observe greatest increases over the conjecture time allotment inferable from rising appropriation of the item in youths for hair gel and face wash (Lim et al. 2013).

In this segment of the assignment on business analytics, the analyst has exhibited two contrasting model of symptomatic courses of action spread out with the assistance of SAP Lumira analytics apparatus. While stooping the models, the ace endeavored to show the conceivable market areas, target clients, propelling channel principal and simplicity of spreading data about the thing in end thing plot. The keen model orchestrated with the assistance of SAP Lumira picture the information amassed through this examination survey (Vera-Baquero et al. 2013). Then again, the pivot charts orchestrated with the assistance of outperform wants demonstrates numerical figures.

3.1 Analytical Solution 1 [SAP Lumira] 3.1.1 Market Opportunities: The worldwide magnificence and individual consideration items advertise measure was esteemed at USD 455.3 billion of every 2017. It is foreseen to enlist a CAGR of 5.9% amid the conjecture time frame. The market is foreseen to step along a sound development track attributable to rising inclination for normal and natural individual consideration (NOPC) items, expanding appropriation of Augmented Reality (AR) in the magnificence business, developing interest for hostile to - maturing items, and prospering prominence of men's prepping items.

This market is ready to observe critical development over the gauge time frame inferable from a few variables. One of the unmistakable variables is developing inclination for NOPC items, since buyers presently lean toward items that contain common fixings.

3.1.2 Targeted Customers

The significance of socioeconomics can't be downplayed. Truth be told, business new businesses will at first accumulate statistic data to incorporate into their field-tested strategies with an end goal to raise seed capital, which is essentially imperative to propelling a business. Statistic data can include: age, area, sex, pay level, training level, conjugal or family status, occupation, ethnic foundation.

The organisation may likewise require neighborhood socioeconomics about what number of individuals claim autos or homes, who goes to school or what level of inhabitants are web or web based life clients. Besides, you can likewise consider the psychographics of the socioeconomics you are focusing on. These may include: identity, frames of mind, values, interests/diversions, ways of life, conduct.

Regardless Notwithstanding whether the economics delineate national or adjacent markets or little social occasions, for instance, those inside an age run, the information keeps up a key separation from the hit-and-miss publicizing so routinely utilized by various associations. As you can envision, the ROI is commonly unsuitable.

The procedure of deliberately deciding socioeconomics to distinguish perfect clients can frequently be difficult. Regardless, this is pivotal as certain promoting methodologies must be put into play that incorporate focused on item bundling, notices and valuing, among different variables.

3.1.3 Channel for campaigns All things considered, how about we investigate some regular socioeconomics and how advertisers may discover these at first valuable to distinguish target markets.

Age – This is a typical client statistic that chiefs use to fragment markets. An organization selling dietary enhancements may have practical experience in at least one wellbeing classes. The promoting plan could express the age bunches that are probably going to buy each sort of item highlighted in that classification. For instance, for a cancer prevention agent equation, advertisers could target people between the ages of 40 and 60 years. In view of statistical surveying, the item may be evaluated underneath normal, have the most recent bleeding edge fixings, and brag the most recent biotechnology.

Sexual orientation – Portioning markets as demonstrated by sex is another typical exhibiting framework. As a result of social trim and physical differences, folks and females have different prerequisites. Sexual direction division is typically found in the publicizing of ordinary prosperity and greatness things. Sexual orientation jobs have changed drastically throughout the years. Advertisers need to abstain from falling into conventional generalizations when showcasing wellbeing and magnificence items in this day and age. For instance, sports sustenance has changed drastically lately. Lady, presently like never before, are using a portion of the equivalent restless games sustenance items men use. For instance, protein equations and dinner substitutions are similarly well known among people. The equivalent can be said for nitric oxide sponsors for both male and female perseverance competitors.

Pay – This is an exceptionally viable statistic advertisers use in contriving their showcasing plans. Frequently, clusters with different pay levels make different tendencies. The improvement of different tendencies is, all things considered, as a result of moderateness and access. A couple of pros fight pay isn't the most strong measurement. A lower pay social affair might be the first to purchase another broad feeding formula if it abstains from the necessity for a couple of free product things. Strangely, inclinations can likewise move when lower salary bunches want upward versatility and purchase items that intrigue to those aspirations.

Training – You may see some cover with the salary statistic. The conviction is that advanced education prompts higher normal wages. In any case, training is likewise a statistic numerous advertisers interface with social class. Social class can be genuine or seen. For instance, individuals with advanced educations may see themselves to be in the upper white collar class. For instance, instructors regularly have advanced educations however won't have a relating upper white collar class salary. These individuals still may lean toward increasingly wealthy items and way of life.

Understand that various elements are considered in choosing the perfect target showcase for an advertising effort. All in all, the objective is to pursue the market that offers the best present or long haul benefit potential. Market measure, development potential, number of contenders and friends qualities are among the key variables. The bigger the market, the more potential to win benefit. Markets that are quickly developing and less aggressive additionally offer preferences.

3.1.4 Overall Strategies

4.0 Recommendations and Conclusion The magnificence business has kept on developing and flourish lately, with the financial downturn doing little to decrease British buyers' energy and hunger for new items and imaginative augmentations to their own consideration routines. In any case, it's not simply items that are developing. Clients' preferences and wishes are additionally continually in transition.

Excellence as an idea, not to mention an industry, has experienced tremendous changes throughout the years. What we see to be lovely, chic and even adequate is continually moving, starting patterns and styles that are presently perpetually connected with a minute in time. You just need to think back to the fabulousness of the forties or the distinct style of the sixties to perceive how much changes in only a couple of decades, and usually these looks and patterns that we partner with those periods as much as any verifiable or social occasion.

The drivers behind these progressions are regularly driven by the business, with design houses or big names managing a specific style or look which is then received and spread by significant brands. In any case, we ought not to belittle the impact or capability of clients themselves, and how their inclinations and requirements can manage the course that the business takes as far as item improvement.

Webb deVlam has directed research on three particular gatherings of female excellence purchasers: the sure ager, the new to normal and the baffled beginner. Instead of simply being characterized by age, riches or status, these customer types depend on disposition and certainty, and each gathering has clear issues and worries that they need their excellence items to address and correct.

Usually essentially alluded to as the "over 50s", there is an inclination to accept that this statistic just thinks about enemy of maturing and how to annihilate wrinkles. Our exploration uncovered a solid pattern of "sure agers" who are splendidly alright with their life arrange. They are not hoping to look to days of yore or recover youth, and for them it's increasingly about accomplishing skin wellbeing.

A large number of the ladies we addressed felt that a great deal of magnificence brands attempted to over-entangle their items and showcasing materials with logical equations and cases to make individuals look more youthful. In any case, this isn't a need for this gathering – they need items which will enable them to accomplish the skin they need, not return them to what they may have once had. Delicate, clear and solid were words that continued being rehashed all through these discussions.

Brands need to handle the unaddressed skin worries that the business has all the earmarks of being awkward facing, from grown-up beginning skin inflammation to postmenopausal skin. Those with develop skin are feeling disliked and under-adjusted and it's time that brands and the business in general set aside the effort to comprehend and provide food for the entire scope of issues, bogeymen and needs that drive this gathering.

These shoppers are probably going to have cash and time to spend on magnificence routines and put resources into items over and over, yet they are additionally less inspired by complex science and entangled aromas. Sure agers hunger for straightforwardness and immaculateness, both as far as item substance and the bundling and promoting that goes with them.

It is recommended in this assignment on business analytics that this gathering comprises of sure, guaranteed ladies who comprehend what they need and what suits their skin and their way of life. They have well-created excellence routines and are beginning to search for explicit items handling skincare and maturing. Normal items are speaking to this gathering, and they have begun to explore by means of huge high road brands, for example, Lush and The Body Shop.

Brands need to strike a fragile parity here. In spite of the fact that they need direction on which regular items are directly for them, an excess of data and whine will put this gathering off, as they are sure and educated and searching for a utilitarian, reasonable answer for their magnificence issues.

They are probably going to drive or working all day and thusly requiring handy solution items that will enable them to keep up their look with least exertion. As far as discussed in this business analytics assignment bundling and brand configuration, clear correspondence about the item's substance and beginning are fundamental, and items that fill more than one need will dependably engage these purchasers. Business Analytics assignments are being prepared by our business statistics assignment help experts from top universities which let us to provide you a reliable assignment help best service.

References Acito, F. and Khatri, V., 2014. Business analytics: Why now and what next?.

Duan, L. and Xiong, Y., 2015. Big data analytics and business analytics. Journal of Management Analytics, 2(1), pp.1-21.

Dubey, R. and Gunasekaran, A., 2015. Education and training for successful career in Big Data and Business Analytics. Industrial and Commercial Training, 47(4), pp.174-181.

Laursen, G.H. and Thorlund, J., 2016. Business analytics for managers: Taking business intelligence beyond reporting. John Wiley & Sons.

Lim, E.P., Chen, H. and Chen, G., 2013. Business intelligence and analytics: Research directions. ACM Transactions on Management Information Systems (TMIS), 3(4), p.17.

Ragsdale, C., 2014. Spreadsheet Modeling and Decision Analysis: A Practical Introduction to Business Analytics. Nelson Education.

Sharma, R., Mithas, S. and Kankanhalli, A., 2014. Transforming decision-making processes: a research agenda for understanding the impact of business analytics on organisations. European Journal of Information Systems, 23(4), pp.433-441.

Shmueli, G. and Lichtendahl Jr, K.C., 2017. Data Mining for Business Analytics: Concepts, Techniques, and Applications in R. John Wiley & Sons.

Vera-Baquero, A., Colomo-Palacios, R. and Molloy, O., 2013. Business process analytics using a big data approach. IT Professional, 15(6), pp.29-35.

Wixom, B.H., Yen, B. and Relich, M., 2013. Maximizing Value from Business Analytics. MIS Quarterly Executive, 12(2).

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Business AnalyticsGroup assignment 1

The 15 Hottest AI Data And Analytics Companies: The 2024 CRN AI 100

Here are 15 data management and data analytics companies, part of the inaugural CRN AI 100, that are playing an outsized role in AI today.

AI needs data–lots of it–to work. Otherwise, the old adage of “garbage in, garbage out” applies. So it’s no surprise that IT companies in the data management space, including suppliers of tools for collecting, managing and preparing huge volumes of data, are very much part of the AI wave now sweeping the industry.

Also participating in the AI tsunami are many data analytics companies, both established vendors and startups, that are incorporating AI and generative AI into their software to go beyond traditional business intelligence and reporting to provide sophisticated, AI-powered search and analytics tools and natural language querying capabilities.

As part of CRN’s inaugural AI 100 list , here are 15 data management and data analytics companies that are playing an outsized role in AI today.

Founder, Chairman, CEO

Deep learning AI and machine learning tasks, including natural language processing, computer vision, and training LLMs, create significant data management and I/O challenges. Alluxio offers tools based on its core data orchestration and provisioning technology to help meet the data-intensive demands of AI workloads.

Kevin Rubin

Interim CEO, CFO

Alteryx is focused on leveraging AI to make data analytics more productive and make analytics available to a wider audience of users. In 2023 Alteryx launched its AiDIN generative AI engine that integrates AI, generative AI, LLMs and machine learning software with the company’s flagship Alteryx Analytics Cloud Platform.

Chair, President, CEO

Couchbase offers vector search capabilities for its Couchbase Server database and Capella cloud database service that help businesses develop generative AI adaptive applications such as chatbots, recommendation engines and semantic search. Couchbase says vector search is critical for improving response accuracy and “taming hallucinations” as AI-powered applications work with LLMs.

Co-Founder, CEO

The Databricks Data Intelligence Platform has developed significant momentum as a unified platform for data analytics and AI tasks, including developing AI-powered applications that use generative AI and LLMs. In 2023 Databricks spent $1.3 billion to acquire generative AI startup Mosaic and its technology for building and training LLMs.

Dataloop offers an AI development platform with an enterprise-grade data engine that ensures developers and the AI applications they build have access to huge volumes of high-quality, relevant data from diverse sources. The platform is particularly targeted toward vision AI applications that use unstructured data such as video, images, audio and text.

Chet Kapoor

DataStax offers its Astra DB Database as a Service, based on the massively scalable Apache Cassandra open-source database, as a real-time data engine for responsive AI applications. DataStax was recently named an AWS Generative AI Competency Partner for its ability to help customers build generative AI applications at production scale.

Domino Data Lab

Nick Elprin

Domino Data Lab provides MLOps software, including its flagship Domino Enterprise AI Platform, that data science teams and developers use to build, deploy and manage the models that power AI and predictive analytics applications. Domino Data Lab recently debuted its Domino AI Gateway to address the risks inherent in broad access to external LLMs.

Ryohei Fujimaki

Founder, CEO

DotData develops a number of AI and machine learning automation tools for data scientists including the DotData Feature Factory for reusable feature asset discovery and the DotData Ops machine learning operations management platform. Recently the company launched DotData Insight, an AI-driven insight discovery engine augmented with generative AI capabilities.

Informatica

Informatica, a longtime leader in the data management and integration space, touts the “AI-powered” capabilities of its data management offerings thanks to its CLAIRE (cloud-centric, AI-backed, real-time engine) technology. In February Informatica debuted Cloud Data Access Management, an AI-backed system for automating data access policy enforcement at scale.

Nima Negahban

Kinetica’s high-performance database enables real-time analytics and generative AI tasks using time-series and spatial data. In May the company linked its database with ChatGPT, enabling “conversational querying” by converting natural language questions into SQL. Kinetica followed that up in September with a native LLM for rapid, ad-hoc data analysis using natural language.

Mike Capone

Qlik’s data analytics, integration and quality product portfolio helps organizations prepare and manage data for a range of AI tasks. Qlik Staige, introduced in September, offers such AI capabilities as AI-assisted script generation and AI-generated insight. In January Qlik acquired Kyndi patents and technology around natural language processing and generative AI.

Jim Goodnight

SAS provides AI capabilities within its flagship Viya data, AI and analytics platform and the company’s Composite AI collection of natural language processing, computer vision and deep learning technologies for AI analytical tasks such as fraud detection and risk management. Recently introduced Software-as-a-Service products, including SAS App Factory, enable rapid AI application development.

Justin Borgman

While Starburst’s data lakehouse platform is best known for unified analytics, it’s also effective for AI and machine learning workloads that use petabyte-scale data sets—especially when that data is scattered across multiple on-premises and cloud systems. Starburst has teamed up with Dell Technologies to enable AI and machine learning workloads on Dell PowerEdge servers and Dell storage systems.

ThoughtSpot

Sudheesh Nair

ThoughtSpot touts its data analytics software as the “AI-powered analytics platform” with its AI search and natural language query capabilities. ThoughtSpot Sage provides AI-generated answers using GPT and LLM technology. ThoughtSpot acquired Mode Analytics in 2023 for $200 million in a move to bolster its generative AI analytical applications.

Weights & Biases

Lukas Biewald

Weights & Biases describes its mission as providing the best tools for machine learning, helping organizations develop machine learning models, manage ML operations and streamline ML workflows. Offerings include Launch for automating ML workflows, Models for model life-cycle management, Weave for interactive ML app development, and the recently debuted Prompts for monitoring LLM performance.

: Data Scientist/Business Analyst

Responsibilities*.

Support clinical healthcare and research activities through business intelligence data analysis, database marketing, data mining, dashboard creation, and data management. Deliver reports, dashboards and write advanced SQL queries. Design and support automated workflows to extract, structure, and store data. Develop descriptive and predictive models and perform analysis to support planning and decision making. Collect and maintain key performance indicator data. Collaborate to identify technology-based process improvements and lead change management efforts. Manage pre-awards submissions and create financial models to monitor the budget and forecast spending for faculty. Up to 100% remote (optional within Michigan).

Required Qualifications*

Bachelors degree in Data Science, Economics, Business, Statistics, or related field +1 years of experience using SAP Crystal Reports, Tableau, SQL, Python, R, and SPSS to support business intelligence reporting and analysis. Experience must include assisting with academic research grant pre-award data management, research proposal preparation and budgeting, and post-award changes, extensions, and renewals. The University of Michigan is an equal opportunity/affirmative action employer. Background check & drug screen required upon offer acceptance.

Background Screening

Michigan Medicine conducts background screening and pre-employment drug testing on job candidates upon acceptance of a contingent job offer and may use a third party administrator to conduct background screenings.  Background screenings are performed in compliance with the Fair Credit Report Act. Pre-employment drug testing applies to all selected candidates, including new or additional faculty and staff appointments, as well as transfers from other U-M campuses.

Application Deadline

Job openings are posted for a minimum of seven calendar days.  The review and selection process may begin as early as the eighth day after posting. This opening may be removed from posting boards and filled anytime after the minimum posting period has ended.

U-M EEO/AA Statement

The University of Michigan is an equal opportunity/affirmative action employer.

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