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7 Data Collection Methods in Business Analytics

• 02 Dec 2021

Data is being generated at an ever-increasing pace. According to Statista , the total volume of data was 64.2 zettabytes in 2020; it’s predicted to reach 181 zettabytes by 2025. This abundance of data can be overwhelming if you aren’t sure where to start.

So, how do you ensure the data you use is relevant and important to the business problems you aim to solve? After all, a data-driven decision is only as strong as the data it’s based on. One way is to collect data yourself.

Here’s a breakdown of data types, why data collection is important, what to know before you begin collecting, and seven data collection methods to leverage.

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What Is Data Collection?

Data collection is the methodological process of gathering information about a specific subject. It’s crucial to ensure your data is complete during the collection phase and that it’s collected legally and ethically . If not, your analysis won’t be accurate and could have far-reaching consequences.

In general, there are three types of consumer data:

• First-party data , which is collected directly from users by your organization
• Second-party data , which is data shared by another organization about its customers (or its first-party data)
• Third-party data , which is data that’s been aggregated and rented or sold by organizations that don’t have a connection to your company or users

Although there are use cases for second- and third-party data, first-party data (data you’ve collected yourself) is more valuable because you receive information about how your audience behaves, thinks, and feels—all from a trusted source.

Data can be qualitative (meaning contextual in nature) or quantitative (meaning numeric in nature). Many data collection methods apply to either type, but some are better suited to one over the other.

In the data life cycle , data collection is the second step. After data is generated, it must be collected to be of use to your team. After that, it can be processed, stored, managed, analyzed, and visualized to aid in your organization’s decision-making.

Before collecting data, there are several factors you need to define:

• The question you aim to answer
• The data subject(s) you need to collect data from
• The collection timeframe
• The data collection method(s) best suited to your needs

The data collection method you select should be based on the question you want to answer, the type of data you need, your timeframe, and your company’s budget.

The Importance of Data Collection

Collecting data is an integral part of a business’s success; it can enable you to ensure the data’s accuracy, completeness, and relevance to your organization and the issue at hand. The information gathered allows organizations to analyze past strategies and stay informed on what needs to change.

The insights gleaned from data can make you hyperaware of your organization’s efforts and give you actionable steps to improve various strategies—from altering marketing strategies to assessing customer complaints.

Basing decisions on inaccurate data can have far-reaching negative consequences, so it’s important to be able to trust your own data collection procedures and abilities. By ensuring accurate data collection, business professionals can feel secure in their business decisions.

Explore the options in the next section to see which data collection method is the best fit for your company.

7 Data Collection Methods Used in Business Analytics

Surveys are physical or digital questionnaires that gather both qualitative and quantitative data from subjects. One situation in which you might conduct a survey is gathering attendee feedback after an event. This can provide a sense of what attendees enjoyed, what they wish was different, and areas in which you can improve or save money during your next event for a similar audience.

While physical copies of surveys can be sent out to participants, online surveys present the opportunity for distribution at scale. They can also be inexpensive; running a survey can cost nothing if you use a free tool. If you wish to target a specific group of people, partnering with a market research firm to get the survey in front of that demographic may be worth the money.

Something to watch out for when crafting and running surveys is the effect of bias, including:

• Collection bias : It can be easy to accidentally write survey questions with a biased lean. Watch out for this when creating questions to ensure your subjects answer honestly and aren’t swayed by your wording.
• Subject bias : Because your subjects know their responses will be read by you, their answers may be biased toward what seems socially acceptable. For this reason, consider pairing survey data with behavioral data from other collection methods to get the full picture.

Related: 3 Examples of Bad Survey Questions & How to Fix Them

2. Transactional Tracking

Each time your customers make a purchase, tracking that data can allow you to make decisions about targeted marketing efforts and understand your customer base better.

Often, e-commerce and point-of-sale platforms allow you to store data as soon as it’s generated, making this a seamless data collection method that can pay off in the form of customer insights.

3. Interviews and Focus Groups

Interviews and focus groups consist of talking to subjects face-to-face about a specific topic or issue. Interviews tend to be one-on-one, and focus groups are typically made up of several people. You can use both to gather qualitative and quantitative data.

Through interviews and focus groups, you can gather feedback from people in your target audience about new product features. Seeing them interact with your product in real-time and recording their reactions and responses to questions can provide valuable data about which product features to pursue.

As is the case with surveys, these collection methods allow you to ask subjects anything you want about their opinions, motivations, and feelings regarding your product or brand. It also introduces the potential for bias. Aim to craft questions that don’t lead them in one particular direction.

One downside of interviewing and conducting focus groups is they can be time-consuming and expensive. If you plan to conduct them yourself, it can be a lengthy process. To avoid this, you can hire a market research facilitator to organize and conduct interviews on your behalf.

4. Observation

Observing people interacting with your website or product can be useful for data collection because of the candor it offers. If your user experience is confusing or difficult, you can witness it in real-time.

Yet, setting up observation sessions can be difficult. You can use a third-party tool to record users’ journeys through your site or observe a user’s interaction with a beta version of your site or product.

While less accessible than other data collection methods, observations enable you to see firsthand how users interact with your product or site. You can leverage the qualitative and quantitative data gleaned from this to make improvements and double down on points of success.

5. Online Tracking

To gather behavioral data, you can implement pixels and cookies. These are both tools that track users’ online behavior across websites and provide insight into what content they’re interested in and typically engage with.

You can also track users’ behavior on your company’s website, including which parts are of the highest interest, whether users are confused when using it, and how long they spend on product pages. This can enable you to improve the website’s design and help users navigate to their destination.

Inserting a pixel is often free and relatively easy to set up. Implementing cookies may come with a fee but could be worth it for the quality of data you’ll receive. Once pixels and cookies are set, they gather data on their own and don’t need much maintenance, if any.

It’s important to note: Tracking online behavior can have legal and ethical privacy implications. Before tracking users’ online behavior, ensure you’re in compliance with local and industry data privacy standards .

Online forms are beneficial for gathering qualitative data about users, specifically demographic data or contact information. They’re relatively inexpensive and simple to set up, and you can use them to gate content or registrations, such as webinars and email newsletters.

You can then use this data to contact people who may be interested in your product, build out demographic profiles of existing customers, and in remarketing efforts, such as email workflows and content recommendations.

Related: What Is Marketing Analytics?

7. Social Media Monitoring

Monitoring your company’s social media channels for follower engagement is an accessible way to track data about your audience’s interests and motivations. Many social media platforms have analytics built in, but there are also third-party social platforms that give more detailed, organized insights pulled from multiple channels.

You can use data collected from social media to determine which issues are most important to your followers. For instance, you may notice that the number of engagements dramatically increases when your company posts about its sustainability efforts.

Building Your Data Capabilities

Understanding the variety of data collection methods available can help you decide which is best for your timeline, budget, and the question you’re aiming to answer. When stored together and combined, multiple data types collected through different methods can give an informed picture of your subjects and help you make better business decisions.

Do you want to become a data-driven professional? Explore our eight-week Business Analytics course and our three-course Credential of Readiness (CORe) program to deepen your analytical skills and apply them to real-world business problems. Not sure which course is right for you? Download our free flowchart .

This post was updated on October 17, 2022. It was originally published on December 2, 2021.

About the Author

Research Methods and Data Analysis for Business Decisions

A Primer Using SPSS

• © 2021
• James E. Sallis 0 ,
• Geir Gripsrud 1 ,
• Ulf Henning Olsson 2 ,
• Ragnhild Silkoset 3

Department of Business Studies, Uppsala University, Uppsala, Sweden

You can also search for this author in PubMed   Google Scholar

Department of Marketing, BI Norwegian Business School, Oslo, Norway

Department of economics, bi norwegian business school, oslo, norway.

• Presents research methods and data analysis tools in non-technical language, using numerous step-by-step examples
• Uses QDA Miner Lite for qualitative and IBM SPSS Statistics for quantitative data analysis
• Benefits business and social science students and managers alike

Part of the book series: Classroom Companion: Business (CCB)

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Table of contents (13 chapters)

Front matter, designing the study, research methods and philosophy of science.

• James E. Sallis, Geir Gripsrud, Ulf Henning Olsson, Ragnhild Silkoset

The Research Process and Problem Formulation

Research design, data collection, secondary data and observation, qualitative methods, questionnaire surveys, quantitative data analysis, simple analysis techniques, hypothesis testing, regression analysis, cluster analysis and segmentation, factor analysis, reporting findings, back matter.

• research methodology
• data analysis
• decision making in business
• quantitative data analysis
• qualitative data analysis
• data analysis software
• research skills
• QDA Miner Lite
• research process
• research design
• statistical methods

About this book

This introductory textbook presents research methods and data analysis tools in non-technical language. It explains the research process and the basics of qualitative and quantitative data analysis, including procedures and methods, analysis, interpretation, and applications using hands-on data examples in QDA Miner Lite and IBM SPSS Statistics software. The book is divided into four parts that address study and research design; data collection, qualitative methods and surveys; statistical methods, including hypothesis testing, regression, cluster and factor analysis; and reporting. The intended audience is business and social science students learning scientific research methods, however, given its business context, the book will be equally useful for decision-makers in businesses and organizations.

Authors and Affiliations

James E. Sallis

Geir Gripsrud, Ragnhild Silkoset

Ulf Henning Olsson

About the authors

James Sallis is a Professor at the Department of Business Studies at Uppsala University, Sweden. He teaches marketing, research methods, and statistics in the undergraduate, graduate, and executive education programs. He is statistical advisor for the department's faculty and students and is a frequent guest lecturer at business schools worldwide.

Geir Gripsrud is Professor Emeritus at the Department of Marketing at BI Norwegian Business School in Oslo, Norway, where he has acted as Dean of both Bachelor and Master Programs. An experienced teacher of marketing, marketing research, distribution channels, and international marketing, he is a co-author of widely used Norwegian textbooks on Research Methods as well as on Distribution Channels and Supply Chains.

Ulf H. Olsson is a Professor at the Department of Economics at BI Norwegian Business School in Oslo, Norway. He has held the position as Provost with responsibility for research and academic resources. Working mainly on structural equation modeling, statistical modeling and psychometrics, he has published research articles in leading statistics and psychometric journals and has also authored textbooks on statistics and mathematics.

Ragnhild Silkoset is a Professor of Marketing at BI Norwegian Business School in Oslo, Norway. She has held the position as the Head of the Department of Marketing, as well as Dean for the Executive Programs. Her areas of interest include marketing, marketing research, pricing strategy, network analysis and blockchain technology.

Bibliographic Information

Book Title : Research Methods and Data Analysis for Business Decisions

Book Subtitle : A Primer Using SPSS

Authors : James E. Sallis, Geir Gripsrud, Ulf Henning Olsson, Ragnhild Silkoset

Series Title : Classroom Companion: Business

DOI : https://doi.org/10.1007/978-3-030-84421-9

Publisher : Springer Cham

eBook Packages : Mathematics and Statistics , Mathematics and Statistics (R0)

Copyright Information : The Editor(s) (if applicable) and The Author(s), under exclusive licence to Springer Nature Switzerland AG 2021

Hardcover ISBN : 978-3-030-84420-2 Published: 31 October 2021

Softcover ISBN : 978-3-030-84423-3 Published: 01 November 2022

eBook ISBN : 978-3-030-84421-9 Published: 30 October 2021

Series ISSN : 2662-2866

Series E-ISSN : 2662-2874

Edition Number : 1

Number of Pages : XI, 258

Number of Illustrations : 26 b/w illustrations, 112 illustrations in colour

Topics : Statistics for Business, Management, Economics, Finance, Insurance , Management Education , Statistics, general , Statistics for Social Sciences, Humanities, Law , Statistics and Computing/Statistics Programs

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• 11 May 2021
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• 01 Nov 2018

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• 29 Apr 2018

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• 06 Jul 2017

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Visualizing and Measuring Software Portfolio Architectures: A Flexibility Analysis

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• 10 Jun 2013

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• 25 Apr 2012
• What Do You Think?

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• 15 Apr 2010

The Consequences of Entrepreneurial Finance: A Regression Discontinuity Analysis

What difference do angel investors make for the success and growth of new ventures? William R. Kerr and Josh Lerner of HBS and Antoinette Schoar of MIT provide fresh evidence to address this crucial question in entrepreneurial finance, quantifying the positive impact that angel investors make to the companies they fund. Angel investors as research subjects have received much less attention than venture capitalists, even though some estimates suggest that these investors are as significant a force for high-potential start-up investments as venture capitalists, and are even more significant as investors elsewhere. This study demonstrates the importance of angel investments to the success and survival of entrepreneurial firms. It also offers an empirical foothold for analyzing many other important questions in entrepreneurial finance. Key concepts include: Angel-funded firms are significantly more likely to survive at least four years (or until 2010) and to raise additional financing outside the angel group. Angel-funded firms are also more likely to show improved venture performance and growth as measured through growth in Web site traffic and Web site rankings. The improvement gains typically range between 30 and 50 percent. Investment success is highly predicated by the interest level of angels during the entrepreneur's initial presentation and by the angels' subsequent due diligence. Access to capital per se may not be the most important value-added that angel groups bring. Some of the "softer" features, such as angels' mentoring or business contacts, may help new ventures the most. Closed for comment; 0 Comments.

• 22 Aug 2005

Data Analysis in Research: Types & Methods

Content Index

Why analyze data in research?

Types of data in research, finding patterns in the qualitative data, methods used for data analysis in qualitative research, preparing data for analysis, methods used for data analysis in quantitative research, considerations in research data analysis, what is data analysis in research.

Definition of research in data analysis: According to LeCompte and Schensul, research data analysis is a process used by researchers to reduce data to a story and interpret it to derive insights. The data analysis process helps reduce a large chunk of data into smaller fragments, which makes sense. 

Three essential things occur during the data analysis process — the first is data organization . Summarization and categorization together contribute to becoming the second known method used for data reduction. It helps find patterns and themes in the data for easy identification and linking. The third and last way is data analysis – researchers do it in both top-down and bottom-up fashion.

LEARN ABOUT: Research Process Steps

On the other hand, Marshall and Rossman describe data analysis as a messy, ambiguous, and time-consuming but creative and fascinating process through which a mass of collected data is brought to order, structure and meaning.

We can say that “the data analysis and data interpretation is a process representing the application of deductive and inductive logic to the research and data analysis.”

Researchers rely heavily on data as they have a story to tell or research problems to solve. It starts with a question, and data is nothing but an answer to that question. But, what if there is no question to ask? Well! It is possible to explore data even without a problem – we call it ‘Data Mining’, which often reveals some interesting patterns within the data that are worth exploring.

Irrelevant to the type of data researchers explore, their mission and audiences’ vision guide them to find the patterns to shape the story they want to tell. One of the essential things expected from researchers while analyzing data is to stay open and remain unbiased toward unexpected patterns, expressions, and results. Remember, sometimes, data analysis tells the most unforeseen yet exciting stories that were not expected when initiating data analysis. Therefore, rely on the data you have at hand and enjoy the journey of exploratory research. 

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Every kind of data has a rare quality of describing things after assigning a specific value to it. For analysis, you need to organize these values, processed and presented in a given context, to make it useful. Data can be in different forms; here are the primary data types.

• Qualitative data: When the data presented has words and descriptions, then we call it qualitative data . Although you can observe this data, it is subjective and harder to analyze data in research, especially for comparison. Example: Quality data represents everything describing taste, experience, texture, or an opinion that is considered quality data. This type of data is usually collected through focus groups, personal qualitative interviews , qualitative observation or using open-ended questions in surveys.
• Quantitative data: Any data expressed in numbers of numerical figures are called quantitative data . This type of data can be distinguished into categories, grouped, measured, calculated, or ranked. Example: questions such as age, rank, cost, length, weight, scores, etc. everything comes under this type of data. You can present such data in graphical format, charts, or apply statistical analysis methods to this data. The (Outcomes Measurement Systems) OMS questionnaires in surveys are a significant source of collecting numeric data.
• Categorical data: It is data presented in groups. However, an item included in the categorical data cannot belong to more than one group. Example: A person responding to a survey by telling his living style, marital status, smoking habit, or drinking habit comes under the categorical data. A chi-square test is a standard method used to analyze this data.

Learn More : Examples of Qualitative Data in Education

Data analysis in qualitative research

Data analysis and qualitative data research work a little differently from the numerical data as the quality data is made up of words, descriptions, images, objects, and sometimes symbols. Getting insight from such complicated information is a complicated process. Hence it is typically used for exploratory research and data analysis .

Although there are several ways to find patterns in the textual information, a word-based method is the most relied and widely used global technique for research and data analysis. Notably, the data analysis process in qualitative research is manual. Here the researchers usually read the available data and find repetitive or commonly used words. 

For example, while studying data collected from African countries to understand the most pressing issues people face, researchers might find  “food”  and  “hunger” are the most commonly used words and will highlight them for further analysis.

LEARN ABOUT: Level of Analysis

The keyword context is another widely used word-based technique. In this method, the researcher tries to understand the concept by analyzing the context in which the participants use a particular keyword.  

For example , researchers conducting research and data analysis for studying the concept of ‘diabetes’ amongst respondents might analyze the context of when and how the respondent has used or referred to the word ‘diabetes.’

The scrutiny-based technique is also one of the highly recommended  text analysis  methods used to identify a quality data pattern. Compare and contrast is the widely used method under this technique to differentiate how a specific text is similar or different from each other. 

For example: To find out the “importance of resident doctor in a company,” the collected data is divided into people who think it is necessary to hire a resident doctor and those who think it is unnecessary. Compare and contrast is the best method that can be used to analyze the polls having single-answer questions types .

Metaphors can be used to reduce the data pile and find patterns in it so that it becomes easier to connect data with theory.

Variable Partitioning is another technique used to split variables so that researchers can find more coherent descriptions and explanations from the enormous data.

LEARN ABOUT: Qualitative Research Questions and Questionnaires

There are several techniques to analyze the data in qualitative research, but here are some commonly used methods,

• Content Analysis:  It is widely accepted and the most frequently employed technique for data analysis in research methodology. It can be used to analyze the documented information from text, images, and sometimes from the physical items. It depends on the research questions to predict when and where to use this method.
• Narrative Analysis: This method is used to analyze content gathered from various sources such as personal interviews, field observation, and  surveys . The majority of times, stories, or opinions shared by people are focused on finding answers to the research questions.
• Discourse Analysis:  Similar to narrative analysis, discourse analysis is used to analyze the interactions with people. Nevertheless, this particular method considers the social context under which or within which the communication between the researcher and respondent takes place. In addition to that, discourse analysis also focuses on the lifestyle and day-to-day environment while deriving any conclusion.
• Grounded Theory:  When you want to explain why a particular phenomenon happened, then using grounded theory for analyzing quality data is the best resort. Grounded theory is applied to study data about the host of similar cases occurring in different settings. When researchers are using this method, they might alter explanations or produce new ones until they arrive at some conclusion.

LEARN ABOUT: 12 Best Tools for Researchers

Data analysis in quantitative research

The first stage in research and data analysis is to make it for the analysis so that the nominal data can be converted into something meaningful. Data preparation consists of the below phases.

Phase I: Data Validation

Data validation is done to understand if the collected data sample is per the pre-set standards, or it is a biased data sample again divided into four different stages

• Fraud: To ensure an actual human being records each response to the survey or the questionnaire
• Screening: To make sure each participant or respondent is selected or chosen in compliance with the research criteria
• Procedure: To ensure ethical standards were maintained while collecting the data sample
• Completeness: To ensure that the respondent has answered all the questions in an online survey. Else, the interviewer had asked all the questions devised in the questionnaire.

Phase II: Data Editing

More often, an extensive research data sample comes loaded with errors. Respondents sometimes fill in some fields incorrectly or sometimes skip them accidentally. Data editing is a process wherein the researchers have to confirm that the provided data is free of such errors. They need to conduct necessary checks and outlier checks to edit the raw edit and make it ready for analysis.

Phase III: Data Coding

Out of all three, this is the most critical phase of data preparation associated with grouping and assigning values to the survey responses . If a survey is completed with a 1000 sample size, the researcher will create an age bracket to distinguish the respondents based on their age. Thus, it becomes easier to analyze small data buckets rather than deal with the massive data pile.

LEARN ABOUT: Steps in Qualitative Research

After the data is prepared for analysis, researchers are open to using different research and data analysis methods to derive meaningful insights. For sure, statistical analysis plans are the most favored to analyze numerical data. In statistical analysis, distinguishing between categorical data and numerical data is essential, as categorical data involves distinct categories or labels, while numerical data consists of measurable quantities. The method is again classified into two groups. First, ‘Descriptive Statistics’ used to describe data. Second, ‘Inferential statistics’ that helps in comparing the data .

Descriptive statistics

This method is used to describe the basic features of versatile types of data in research. It presents the data in such a meaningful way that pattern in the data starts making sense. Nevertheless, the descriptive analysis does not go beyond making conclusions. The conclusions are again based on the hypothesis researchers have formulated so far. Here are a few major types of descriptive analysis methods.

Measures of Frequency

• Count, Percent, Frequency
• It is used to denote home often a particular event occurs.
• Researchers use it when they want to showcase how often a response is given.

Measures of Central Tendency

• Mean, Median, Mode
• The method is widely used to demonstrate distribution by various points.
• Researchers use this method when they want to showcase the most commonly or averagely indicated response.

Measures of Dispersion or Variation

• Range, Variance, Standard deviation
• Here the field equals high/low points.
• Variance standard deviation = difference between the observed score and mean
• It is used to identify the spread of scores by stating intervals.
• Researchers use this method to showcase data spread out. It helps them identify the depth until which the data is spread out that it directly affects the mean.

Measures of Position

• Percentile ranks, Quartile ranks
• It relies on standardized scores helping researchers to identify the relationship between different scores.
• It is often used when researchers want to compare scores with the average count.

For quantitative research use of descriptive analysis often give absolute numbers, but the in-depth analysis is never sufficient to demonstrate the rationale behind those numbers. Nevertheless, it is necessary to think of the best method for research and data analysis suiting your survey questionnaire and what story researchers want to tell. For example, the mean is the best way to demonstrate the students’ average scores in schools. It is better to rely on the descriptive statistics when the researchers intend to keep the research or outcome limited to the provided  sample  without generalizing it. For example, when you want to compare average voting done in two different cities, differential statistics are enough.

Descriptive analysis is also called a ‘univariate analysis’ since it is commonly used to analyze a single variable.

Inferential statistics

Inferential statistics are used to make predictions about a larger population after research and data analysis of the representing population’s collected sample. For example, you can ask some odd 100 audiences at a movie theater if they like the movie they are watching. Researchers then use inferential statistics on the collected  sample  to reason that about 80-90% of people like the movie. 

Here are two significant areas of inferential statistics.

• Estimating parameters: It takes statistics from the sample research data and demonstrates something about the population parameter.
• Hypothesis test: I t’s about sampling research data to answer the survey research questions. For example, researchers might be interested to understand if the new shade of lipstick recently launched is good or not, or if the multivitamin capsules help children to perform better at games.

These are sophisticated analysis methods used to showcase the relationship between different variables instead of describing a single variable. It is often used when researchers want something beyond absolute numbers to understand the relationship between variables.

Here are some of the commonly used methods for data analysis in research.

• Correlation: When researchers are not conducting experimental research or quasi-experimental research wherein the researchers are interested to understand the relationship between two or more variables, they opt for correlational research methods.
• Cross-tabulation: Also called contingency tables,  cross-tabulation  is used to analyze the relationship between multiple variables.  Suppose provided data has age and gender categories presented in rows and columns. A two-dimensional cross-tabulation helps for seamless data analysis and research by showing the number of males and females in each age category.
• Regression analysis: For understanding the strong relationship between two variables, researchers do not look beyond the primary and commonly used regression analysis method, which is also a type of predictive analysis used. In this method, you have an essential factor called the dependent variable. You also have multiple independent variables in regression analysis. You undertake efforts to find out the impact of independent variables on the dependent variable. The values of both independent and dependent variables are assumed as being ascertained in an error-free random manner.
• Frequency tables: The statistical procedure is used for testing the degree to which two or more vary or differ in an experiment. A considerable degree of variation means research findings were significant. In many contexts, ANOVA testing and variance analysis are similar.
• Analysis of variance: The statistical procedure is used for testing the degree to which two or more vary or differ in an experiment. A considerable degree of variation means research findings were significant. In many contexts, ANOVA testing and variance analysis are similar.
• Researchers must have the necessary research skills to analyze and manipulation the data , Getting trained to demonstrate a high standard of research practice. Ideally, researchers must possess more than a basic understanding of the rationale of selecting one statistical method over the other to obtain better data insights.
• Usually, research and data analytics projects differ by scientific discipline; therefore, getting statistical advice at the beginning of analysis helps design a survey questionnaire, select data collection methods , and choose samples.

LEARN ABOUT: Best Data Collection Tools

• The primary aim of data research and analysis is to derive ultimate insights that are unbiased. Any mistake in or keeping a biased mind to collect data, selecting an analysis method, or choosing  audience  sample il to draw a biased inference.
• Irrelevant to the sophistication used in research data and analysis is enough to rectify the poorly defined objective outcome measurements. It does not matter if the design is at fault or intentions are not clear, but lack of clarity might mislead readers, so avoid the practice.
• The motive behind data analysis in research is to present accurate and reliable data. As far as possible, avoid statistical errors, and find a way to deal with everyday challenges like outliers, missing data, data altering, data mining , or developing graphical representation.

LEARN MORE: Descriptive Research vs Correlational Research The sheer amount of data generated daily is frightening. Especially when data analysis has taken center stage. in 2018. In last year, the total data supply amounted to 2.8 trillion gigabytes. Hence, it is clear that the enterprises willing to survive in the hypercompetitive world must possess an excellent capability to analyze complex research data, derive actionable insights, and adapt to the new market needs.

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QuestionPro is an online survey platform that empowers organizations in data analysis and research and provides them a medium to collect data by creating appealing surveys.

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Home » Data Analysis – Process, Methods and Types

Data Analysis – Process, Methods and Types

Table of Contents

Data Analysis

Definition:

Data analysis refers to the process of inspecting, cleaning, transforming, and modeling data with the goal of discovering useful information, drawing conclusions, and supporting decision-making. It involves applying various statistical and computational techniques to interpret and derive insights from large datasets. The ultimate aim of data analysis is to convert raw data into actionable insights that can inform business decisions, scientific research, and other endeavors.

Data Analysis Process

The following are step-by-step guides to the data analysis process:

Define the Problem

The first step in data analysis is to clearly define the problem or question that needs to be answered. This involves identifying the purpose of the analysis, the data required, and the intended outcome.

Collect the Data

The next step is to collect the relevant data from various sources. This may involve collecting data from surveys, databases, or other sources. It is important to ensure that the data collected is accurate, complete, and relevant to the problem being analyzed.

Clean and Organize the Data

Once the data has been collected, it needs to be cleaned and organized. This involves removing any errors or inconsistencies in the data, filling in missing values, and ensuring that the data is in a format that can be easily analyzed.

Analyze the Data

The next step is to analyze the data using various statistical and analytical techniques. This may involve identifying patterns in the data, conducting statistical tests, or using machine learning algorithms to identify trends and insights.

Interpret the Results

After analyzing the data, the next step is to interpret the results. This involves drawing conclusions based on the analysis and identifying any significant findings or trends.

Communicate the Findings

Once the results have been interpreted, they need to be communicated to stakeholders. This may involve creating reports, visualizations, or presentations to effectively communicate the findings and recommendations.

Take Action

The final step in the data analysis process is to take action based on the findings. This may involve implementing new policies or procedures, making strategic decisions, or taking other actions based on the insights gained from the analysis.

Types of Data Analysis

Types of Data Analysis are as follows:

Descriptive Analysis

This type of analysis involves summarizing and describing the main characteristics of a dataset, such as the mean, median, mode, standard deviation, and range.

Inferential Analysis

This type of analysis involves making inferences about a population based on a sample. Inferential analysis can help determine whether a certain relationship or pattern observed in a sample is likely to be present in the entire population.

Diagnostic Analysis

This type of analysis involves identifying and diagnosing problems or issues within a dataset. Diagnostic analysis can help identify outliers, errors, missing data, or other anomalies in the dataset.

Predictive Analysis

This type of analysis involves using statistical models and algorithms to predict future outcomes or trends based on historical data. Predictive analysis can help businesses and organizations make informed decisions about the future.

Prescriptive Analysis

This type of analysis involves recommending a course of action based on the results of previous analyses. Prescriptive analysis can help organizations make data-driven decisions about how to optimize their operations, products, or services.

Exploratory Analysis

This type of analysis involves exploring the relationships and patterns within a dataset to identify new insights and trends. Exploratory analysis is often used in the early stages of research or data analysis to generate hypotheses and identify areas for further investigation.

Data Analysis Methods

Data Analysis Methods are as follows:

Statistical Analysis

This method involves the use of mathematical models and statistical tools to analyze and interpret data. It includes measures of central tendency, correlation analysis, regression analysis, hypothesis testing, and more.

Machine Learning

This method involves the use of algorithms to identify patterns and relationships in data. It includes supervised and unsupervised learning, classification, clustering, and predictive modeling.

Data Mining

This method involves using statistical and machine learning techniques to extract information and insights from large and complex datasets.

Text Analysis

This method involves using natural language processing (NLP) techniques to analyze and interpret text data. It includes sentiment analysis, topic modeling, and entity recognition.

Network Analysis

This method involves analyzing the relationships and connections between entities in a network, such as social networks or computer networks. It includes social network analysis and graph theory.

Time Series Analysis

This method involves analyzing data collected over time to identify patterns and trends. It includes forecasting, decomposition, and smoothing techniques.

Spatial Analysis

This method involves analyzing geographic data to identify spatial patterns and relationships. It includes spatial statistics, spatial regression, and geospatial data visualization.

Data Visualization

This method involves using graphs, charts, and other visual representations to help communicate the findings of the analysis. It includes scatter plots, bar charts, heat maps, and interactive dashboards.

Qualitative Analysis

This method involves analyzing non-numeric data such as interviews, observations, and open-ended survey responses. It includes thematic analysis, content analysis, and grounded theory.

Multi-criteria Decision Analysis

This method involves analyzing multiple criteria and objectives to support decision-making. It includes techniques such as the analytical hierarchy process, TOPSIS, and ELECTRE.

Data Analysis Tools

There are various data analysis tools available that can help with different aspects of data analysis. Below is a list of some commonly used data analysis tools:

• Microsoft Excel: A widely used spreadsheet program that allows for data organization, analysis, and visualization.
• SQL : A programming language used to manage and manipulate relational databases.
• R : An open-source programming language and software environment for statistical computing and graphics.
• Python : A general-purpose programming language that is widely used in data analysis and machine learning.
• Tableau : A data visualization software that allows for interactive and dynamic visualizations of data.
• SAS : A statistical analysis software used for data management, analysis, and reporting.
• SPSS : A statistical analysis software used for data analysis, reporting, and modeling.
• Matlab : A numerical computing software that is widely used in scientific research and engineering.
• RapidMiner : A data science platform that offers a wide range of data analysis and machine learning tools.

Applications of Data Analysis

Data analysis has numerous applications across various fields. Below are some examples of how data analysis is used in different fields:

• Business : Data analysis is used to gain insights into customer behavior, market trends, and financial performance. This includes customer segmentation, sales forecasting, and market research.
• Healthcare : Data analysis is used to identify patterns and trends in patient data, improve patient outcomes, and optimize healthcare operations. This includes clinical decision support, disease surveillance, and healthcare cost analysis.
• Education : Data analysis is used to measure student performance, evaluate teaching effectiveness, and improve educational programs. This includes assessment analytics, learning analytics, and program evaluation.
• Finance : Data analysis is used to monitor and evaluate financial performance, identify risks, and make investment decisions. This includes risk management, portfolio optimization, and fraud detection.
• Government : Data analysis is used to inform policy-making, improve public services, and enhance public safety. This includes crime analysis, disaster response planning, and social welfare program evaluation.
• Sports : Data analysis is used to gain insights into athlete performance, improve team strategy, and enhance fan engagement. This includes player evaluation, scouting analysis, and game strategy optimization.
• Marketing : Data analysis is used to measure the effectiveness of marketing campaigns, understand customer behavior, and develop targeted marketing strategies. This includes customer segmentation, marketing attribution analysis, and social media analytics.
• Environmental science : Data analysis is used to monitor and evaluate environmental conditions, assess the impact of human activities on the environment, and develop environmental policies. This includes climate modeling, ecological forecasting, and pollution monitoring.

When to Use Data Analysis

Data analysis is useful when you need to extract meaningful insights and information from large and complex datasets. It is a crucial step in the decision-making process, as it helps you understand the underlying patterns and relationships within the data, and identify potential areas for improvement or opportunities for growth.

Here are some specific scenarios where data analysis can be particularly helpful:

• Problem-solving : When you encounter a problem or challenge, data analysis can help you identify the root cause and develop effective solutions.
• Optimization : Data analysis can help you optimize processes, products, or services to increase efficiency, reduce costs, and improve overall performance.
• Prediction: Data analysis can help you make predictions about future trends or outcomes, which can inform strategic planning and decision-making.
• Performance evaluation : Data analysis can help you evaluate the performance of a process, product, or service to identify areas for improvement and potential opportunities for growth.
• Risk assessment : Data analysis can help you assess and mitigate risks, whether it is financial, operational, or related to safety.
• Market research : Data analysis can help you understand customer behavior and preferences, identify market trends, and develop effective marketing strategies.
• Quality control: Data analysis can help you ensure product quality and customer satisfaction by identifying and addressing quality issues.

Purpose of Data Analysis

The primary purposes of data analysis can be summarized as follows:

• To gain insights: Data analysis allows you to identify patterns and trends in data, which can provide valuable insights into the underlying factors that influence a particular phenomenon or process.
• To inform decision-making: Data analysis can help you make informed decisions based on the information that is available. By analyzing data, you can identify potential risks, opportunities, and solutions to problems.
• To improve performance: Data analysis can help you optimize processes, products, or services by identifying areas for improvement and potential opportunities for growth.
• To measure progress: Data analysis can help you measure progress towards a specific goal or objective, allowing you to track performance over time and adjust your strategies accordingly.
• To identify new opportunities: Data analysis can help you identify new opportunities for growth and innovation by identifying patterns and trends that may not have been visible before.

Examples of Data Analysis

Some Examples of Data Analysis are as follows:

• Social Media Monitoring: Companies use data analysis to monitor social media activity in real-time to understand their brand reputation, identify potential customer issues, and track competitors. By analyzing social media data, businesses can make informed decisions on product development, marketing strategies, and customer service.
• Financial Trading: Financial traders use data analysis to make real-time decisions about buying and selling stocks, bonds, and other financial instruments. By analyzing real-time market data, traders can identify trends and patterns that help them make informed investment decisions.
• Traffic Monitoring : Cities use data analysis to monitor traffic patterns and make real-time decisions about traffic management. By analyzing data from traffic cameras, sensors, and other sources, cities can identify congestion hotspots and make changes to improve traffic flow.
• Healthcare Monitoring: Healthcare providers use data analysis to monitor patient health in real-time. By analyzing data from wearable devices, electronic health records, and other sources, healthcare providers can identify potential health issues and provide timely interventions.
• Online Advertising: Online advertisers use data analysis to make real-time decisions about advertising campaigns. By analyzing data on user behavior and ad performance, advertisers can make adjustments to their campaigns to improve their effectiveness.
• Sports Analysis : Sports teams use data analysis to make real-time decisions about strategy and player performance. By analyzing data on player movement, ball position, and other variables, coaches can make informed decisions about substitutions, game strategy, and training regimens.
• Energy Management : Energy companies use data analysis to monitor energy consumption in real-time. By analyzing data on energy usage patterns, companies can identify opportunities to reduce energy consumption and improve efficiency.

Characteristics of Data Analysis

Characteristics of Data Analysis are as follows:

• Objective : Data analysis should be objective and based on empirical evidence, rather than subjective assumptions or opinions.
• Systematic : Data analysis should follow a systematic approach, using established methods and procedures for collecting, cleaning, and analyzing data.
• Accurate : Data analysis should produce accurate results, free from errors and bias. Data should be validated and verified to ensure its quality.
• Relevant : Data analysis should be relevant to the research question or problem being addressed. It should focus on the data that is most useful for answering the research question or solving the problem.
• Comprehensive : Data analysis should be comprehensive and consider all relevant factors that may affect the research question or problem.
• Timely : Data analysis should be conducted in a timely manner, so that the results are available when they are needed.
• Reproducible : Data analysis should be reproducible, meaning that other researchers should be able to replicate the analysis using the same data and methods.
• Communicable : Data analysis should be communicated clearly and effectively to stakeholders and other interested parties. The results should be presented in a way that is understandable and useful for decision-making.

Advantages of Data Analysis

Advantages of Data Analysis are as follows:

• Better decision-making: Data analysis helps in making informed decisions based on facts and evidence, rather than intuition or guesswork.
• Improved efficiency: Data analysis can identify inefficiencies and bottlenecks in business processes, allowing organizations to optimize their operations and reduce costs.
• Increased accuracy: Data analysis helps to reduce errors and bias, providing more accurate and reliable information.
• Better customer service: Data analysis can help organizations understand their customers better, allowing them to provide better customer service and improve customer satisfaction.
• Competitive advantage: Data analysis can provide organizations with insights into their competitors, allowing them to identify areas where they can gain a competitive advantage.
• Identification of trends and patterns : Data analysis can identify trends and patterns in data that may not be immediately apparent, helping organizations to make predictions and plan for the future.
• Improved risk management : Data analysis can help organizations identify potential risks and take proactive steps to mitigate them.
• Innovation: Data analysis can inspire innovation and new ideas by revealing new opportunities or previously unknown correlations in data.

Limitations of Data Analysis

• Data quality: The quality of data can impact the accuracy and reliability of analysis results. If data is incomplete, inconsistent, or outdated, the analysis may not provide meaningful insights.
• Limited scope: Data analysis is limited by the scope of the data available. If data is incomplete or does not capture all relevant factors, the analysis may not provide a complete picture.
• Human error : Data analysis is often conducted by humans, and errors can occur in data collection, cleaning, and analysis.
• Cost : Data analysis can be expensive, requiring specialized tools, software, and expertise.
• Time-consuming : Data analysis can be time-consuming, especially when working with large datasets or conducting complex analyses.
• Overreliance on data: Data analysis should be complemented with human intuition and expertise. Overreliance on data can lead to a lack of creativity and innovation.
• Privacy concerns: Data analysis can raise privacy concerns if personal or sensitive information is used without proper consent or security measures.

About the author

Muhammad Hassan

Researcher, Academic Writer, Web developer

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Business Research: Types, Methods, Examples

• Updated on  
• Jan 29, 2024

Ever wondered what it takes to build a flourishing business ? Aiming to provide maximum sales and profit, business research helps you to gather comprehensive information about your business and accordingly make relevant changes if required. So, in this process of being successful, we gather all types of data to better define our strategies and understand what products or services customers want. And in case, you’re planning to expand your business, research can help you determine your odds of positive results. In this blog, we’ll help you understand the basics of research and analysis .

“Whoever gets closer to the customer, wins.” – Bernadette Jiwa

This Blog Includes:

What is business research, business research example, importance of business research, types & methods, focus groups , case study research , ethnographic research, survey , correlation research , experimental research , advantages and disadvantages of business research, scope of business research, role of business research, business research books, business research report, top 10 tools for business research, business research partners, top 10 business research topics, career prospects , [bonus] best mba colleges in the world.

Business Research can be simply defined as a process of gathering comprehensive data and information on all the areas of business and incorporating this information for sales and profit maximization. If you are wondering what is Business Research, it is a systematic management activity helping companies to determine which product will be most profitable for companies to produce. Also, there are multiple steps in conducting research, with each thoroughly reviewed to ensure that the best decision is made for the company as a whole.

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Let’s say there’s an automobile company that is planning to launch a car that runs on CNG. To promote cleaner fuel, the company will be involved in developing different plans and strategies to identify the demand for the car they intend to launch. Other than this, the company will also look for competitors, and the target audience, keeping in mind the distribution of CNG in India. Hence the research is conducted on various ideas to formulate a sustainable and more efficient design. 

When it comes to the question of why Business Research is important, it has an essential role to play in varied areas of business. Here are some of the reasons describing the importance of Business Research:

• It helps businesses gain better insights into their target customer’s preferences, buying patterns, pain points, as well as demographics.
• Business Research also provides businesses with a detailed overview of their target markets, what’s in trend, as well as market demand.
• By studying consumers’ buying patterns and preferences as well as market trends and demands with the help of business research, businesses can effectively and efficiently curate the best possible plans and strategies accordingly.
• The importance of business research also lies in highlighting the areas where unnecessary costs can be minimized and those areas in a business which need more attention and can bring in more customers and hence boost profits.
• Businesses can constantly innovate as per their customers’ preferences and interests and keep their attention on the brand.
• Business Research also plays the role of a catalyst as it helps businesses thrive in their markets by capturing all the available opportunities and also meeting the needs and preferences of their customers.

Also Read: Business Analyst vs Data Analyst

Business research plays an important role in the business intelligence process. This is usually conducted to determine if a company can succeed in a new region through competitive analyses and a better marketing approach. Due to this, this broad field has been distinguished into two types namely, Qualitative Research and Quantitative Research Method.

Here are the most important types of Business Research :

Qualitative Research Methods 

It involves putting open-ended questions to the audience through different channels of communication to understand why researchers think in a particular manner. Stress is laid on understanding the intent, attitude, and beliefs to figure out the behaviour and response of the customers. Moreover, the goal of Qualitative Business Research is to get in-depth knowledge about the subjects of the research. Moreover, qualitative research enables us to put the perspective of the consumer in front of the researcher so that we can understand and see the alignment of the ideas between the market and the business. 

The data collected in this type of business research is by the following methods:  

• Interviews 
• Case Study 
• Ethnographic Research 
• Website Visitor Profiling 
• Content Analysis 

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Let us take a detailed look at some of the ways-

Interviews and surveys are similar. The only difference lies in the fact that the responder can put a question in an interview whilst it is not possible during a survey. Through interviews, it is easier to understand the detailed perspective of the person concerning the subject of research. A mobile brand researched to understand why certain colours are preferred by male and female customers. The research revealed that since red is assumed to be a feminine colour, it is more preferred by females than males. 

Focus groups are a type of business research that involves only a set of individuals. Each selected individual represents a particular category of the target market. The major difference between interviews and focus groups is the number of people that it involves. To launch a new product for a particular group of society, focus groups prove to be the best way to understand the needs of the local audience. 

For example, Tesla decides to launch their latest car model in India. The company, therefore, will require feedback from the Indian audience only.

Did you know? Amazon, the internet giant changed its payment strategy to enter the Indian market. Since the Indian economy was not entirely ready for online modes of payment, amazon introduced a new payment method and came up with ‘ cash on delivery ’ to gain consumers’ trust.

One of the most effective ways for business research is conducting case studies. With the motive to understand customer satisfaction, challenges that usually the customers face while using the product and hence, providing them with the right solution can be achieved by analysing data secured through data secured by case studies. Case study researchers are conducted in many fields of business that ultimately aid organisations in improving their products or services. 

Ethnographic Research refers to understanding people as a whole. One must be able to grok their consumers or target audience which will help identify patterns, flaws, etc. Ethnography is a branch of anthropology that is the study of what elements or features make us humans. How did people live? What aspect made us so dependent on smartphones and technology? Why would people buy one product over the other? It refers to asking questions about lifestyle, communities, etc., and trying to gain insight into consumer behaviour and buying patterns.

For example, consider a random product. Are people looking for that product? Do they need it? Is it a necessity or a luxury? Which class of people are most likely to buy it? People often cannot comprehend what they are looking for. Gaining different perceptions can help us tailor our products accordingly to the consumers. Who would have thought that the majority of humans will need face masks for survival?

Also Read: How to Become a Research Analyst?

Quantitative Research Methods 

With the employment of mathematical, statistical and computational techniques, quantitative research is carried out to deal with numbers. This systematical empirical investigation starts with the acquisition of the data and then moves on to analyzing it with the help of different tools. The goal is to identify clientele and then meet the targets of the audience. As the method of business research employs a questionnaire to determine the audience’s response, the questions are built around the idea that the audience knows about the product or the services that the firm offers. Some of the key questions answered in quantitative research methods include, who is connected with your network, how they qualify for the ‘product’ or how regularly they visit your website.

The data is collected based on the following research:

• Correlational
• Online 
• Casual Comparative 
• Experimental 

It is the most common method under quantitative research via which a huge amount of data can be collected concerning a product or service. A common set of questions are asked to the people and they are asked to provide their inputs. To understand the nature of the market in-depth, this method is massively used by leading organisations all across the globe. Analysing data recorded through service helps organisations make suitable decisions.

Under this research, usually two entities are put together to examine the impact they create on each other. As suggested by the name it is the best process to understand patterns, relationships and trends. the data grasped through correlation research is generally combined with other tools as one cannot achieve a firm conclusion using this type of business research.  

Experimental research is purely based on proving a particular theory that is pre-assumed. True experimental research companies can understand varied behavioural traits of the customers that further assist them in generating more revenue. Exposing a set of audience to common parameters, their behaviour is recorded and hence analysed. This can be understood as the main basis of the experimental research. 

Also Read: Scope of Operation Research

There are certain pros and cons of business research that you must know about. Here are the advantages and disadvantages of Business Research.

Advantages of Business Research

• Business Research plays the role of a catalyst in identifying potential threats, issues as well as opportunities .
• It provides a detailed analysis of customers and the target audience , thus helping in building better relationships with one’s audience and capturing the areas which we might be missing out on.
• It also anticipates future problems thus the enterprise is able to tackle those uncertainties and prepare for them beforehand.
• It keeps a continuous track of competition in the market and gives businesses the scope to come up with better strategies to tackle their competitors.
• Business Research also conducts a thorough cost analysis thus helping the company efficiently manage resources and allocate them in an optimal manner.
• It keeps you updated with the latest trends and competitor analysis .

Disadvantages of Business Research

• Business Research can be expensive and time-consuming .
• It also has the danger of being assumptive and imprecise at times , because the focus groups might be small or can be highly based on assumptions.
• The market is ever-changing and ever-evolving and capturing the right trends or anticipating them can constitute a complicated process for business research.

Also Read: Types of Research Design

The process of business research can be as comprehensive and as detailed as a business wants it to be. Generally, a company takes up research with a certain aim or hypothesis in order to figure out the issues, opportunities and trends and how they can be leveraged in the best way.

Here is the step-by-step process of Business Research:

• Identifying the Opportunity or Problem – To begin with the research, we first need to know what is the problem or the opportunity we would be leveraging on. It can be a popular trend or a common problem that a business is facing and can potentially become the headstart for the research process. Once you know the problem or the opportunity, go ahead with giving an understandable statement of what it’s about, what the hypothesis of the research will be as well as its objectives.
• Decide and Plan the Research Design – The next step in the business research process to find the right research design which suits the objectives and overall plan of the research. The most popular research designs are Quantitative and Qualitative Research.
• Determining the Research Method – The research design is closely connected to the research method since both qualitative and quantitative research designs have different methods for data collection, analysis, amongst others. So, once you have put a finger on what the right research design will be, go ahead with finding the right research method as per the plan, types of data collection, objective, costs involved, and other determining factors.
• Collect Data – Utilizing the research method and design, the next step in the business research process is to collect data and assimilate it.
• Data Analysis and Evaluation – After assimilating the data required, the data analysis will take place to gather all the observations and findings.
• Communicate Results – The presentation of the business research report is the concluding step of this procedure after which the higher management works upon the best techniques and strategies to leverage the opportunity or tackle the issue.

Also Read: MBA in Business Analytics

The scope of Business Research is multifarious and reaches out to many specialisations and areas. Let’s take a look the scope of business research across various specialisations:

• Marketing Management When it comes to business research, becomes an important part of marketing management that analyses consumer behaviour, target audiences, competition, price policy, promotional plans and much more.
• Financial Management It also plays an essential role in budgeting, financial planning, cost allocation, capital raising, tackling fluctuations with international currency as well as taking finance-related decisions.
• Production Management Production Management also includes business research as it helps in product development, planning out for a newer one, finalizing the right technologies for production, and so on.
• Materials Management Business Research is an important aspect of checking the best materials and carrying out its production, supply chain management , logistics , as well as shortlisting negotiation strategies.

There is an incremental role of business research as its importance is across every aspect of the business. Let’s take a look at the role of business research in an enterprise:

• The most primary role of business research is that it helps across every decision in the business, from product innovation to marketing and promotional planning.
• Business Research also helps in forecasting a business, whether in terms of competition or any other types of problems it will be facing.
• Another key area where this plays a bigger role is ensuring consumer satisfaction as through research, we can carry out research and highlight areas where we can efficiently serve our target audience.
• Business research also helps in implementing cost-effectiveness in a business as it can assist in cutting costs wherever needed and investing more in those areas, where profit is coming from.

Want to understand and learn more about business research? Here are some of the books that will make you a pro in this field. Check out the list of business research books:

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The purpose of a report is to inform the other members, junior and subordinates of the team to provide information on the specific topic. There is a specific format of a business report which makes it look more professional and presentable. There should be a title with the date and nature. The second section includes the introduction, body, and then conclusion. Reports help to identify the issues and helps in resolving them at earlier stages. It can include graphs, surveys, interviews, flow, and piecharts also.

Are you wondering why is there a need to do business research? Business is not stable and it is vital to stay up to date with all the data and developments. It is also important to make business-related decisions, and keep track of competitors, customer feedback, and market changes. The basic objective of business research is to identify the issues and evaluate a plan to resolve them for better managerial functioning.

Now that you are familiar with the objective, importance, and advantages the next important step is to know how to conduct research. There are numerous tools available for free while for some advanced tools there is a membership. Check out the list of top 10 tools:

• Google Keyword Tools
• Google Analytics
• Google Trends

The one thing constant in a business is market changes. A new trend or change comes every time you blink an eye. To keep track of everything externally and internally a research partner comes helpful. There are a few things to keep in mind that will help you in choosing the right business partner. The first thing to keep in mind is that the person should have relevant work experience and expertise in that particular field. An experienced partner can help businesses reach new heights. Look for a partner that can provide well-curated solutions and not the generic ideas that every enterprise follows. Last but not least is that your business research partner should have knowledge of the latest tools and techniques.

Also Read: MBA in Sustainable Development: Courses & Universities

Is your big presentation coming up or your report is due on Monday but you still haven’t finalized your business research topic? Here are some of the trendiest research topics for you:

• How advertisements influence consumer behaviour?
• Does incentive motivation increase employee productivity?
• How to handle crises in the business?
• How to create a work-life balance in the organization?
• What are the things a small business owner has to face?
• How to expand the company globally?
• How is digital marketing helping every business type?
• How to maintain the quality and quantity of products?
• What are the struggles entrepreneurs of a start-up face?
• How to create a budget and maintain company finances?

In order to build a career in Research , you can simply grab a degree in the field of Management , Business or Administration. So, students with an understanding of the core concepts of business and an inclination for research can consider it as a go-to option. Other suitable programs can be Master in Management , MBA Business Analytics , and MBA Data Analytics , to name a few.

To know more, check out Qualitative Research Methods !

It can simply mean researching every area of a business and using the provided information and data to ensure profit maximization.

There are different types of business research such as interviews, surveys, focus groups, correlational research, ethnographic research, case study research, and quantitative research methods, amongst others.

It is essentially important for various aspects of a business such as profit maximization, cost-cutting, financial management , personnel management, consumer behaviour, etc.

The process of research depends upon the type of research design you are opting for. To start with, we first need to determine the aim or objective of the research, then plan out the whole process which includes the types of methods we will be using, then the actual research that takes place followed by the data found that helps in understanding the key observations and how they can be implemented to actualize research hypothesis.

If you’re thinking to start a product line in your existing business or planning a startup, business research is a fundamental process that helps you to navigate the opportunities and obstacles in the marketplace. Knowing your strengths and weaknesses can help you come up with advanced and powerful research techniques that will make it easier to manage. Are you planning to take your higher education abroad? Then, you can quickly book a counselling session with the experts at Leverage Edu and we can help you build the right platform for you to grow in the corporate world.

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National Center for Science and Engineering Statistics

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Business R&D Performance in the United States Tops $600 Billion in 2021

September 28, 2023

Funds spent for business R&D performed in the United States, by type of R&D, source of funds, and size of company: 2018–21

i = more than 50% of the estimate is a combination of imputation and reweighting to account for nonresponse.

a Domestic R&D performance is the cost of R&D paid for and performed by the respondent company and paid for by others outside of the company and performed by the respondent company. b R&D comprises creative and systematic work undertaken in order to increase the stock of knowledge and to devise new applications of available knowledge. This includes (1) activities aimed at acquiring new knowledge or understanding without specific immediate commercial applications or uses (basic research), (2) activities aimed at solving a specific problem or meeting a specific commercial objective (applied research), and (3) systematic work, drawing on research and practical experience and resulting in additional knowledge, which is directed to producing new processes or to improving existing products—goods or services—or processes (development). c Includes foreign subsidiaries of U.S. companies. d Includes companies located inside and outside the United States; U.S. state government agencies and laboratories; U.S. universities, colleges, and academic researchers; and all other organizations located inside and outside the United States. e Includes only companies with 10 or more domestic employees.

Detail may not add to total because of rounding.

National Center for Science and Engineering Statistics and Census Bureau, Business Enterprise Research and Development Survey.

R&D Performance, by Type of R&D, Industry Sector, and Source of Funding

In 2021, of the $602 billion that companies spent on R&D, $40 billion (7%) was spent on basic research, $86 billion (14%) on applied research, and $476 billion (79%) on development ( table 1 ). In 2021, companies in manufacturing industries performed $326 billion (54%) of domestic R&D , defined as R&D performed in the 50 states and Washington, DC ( table 2 ). Most of the funding came from these companies’ own funds (88%). Companies in nonmanufacturing industries performed $276 billion of domestic R&D (46% of total domestic R&D performance), 87% of which was paid for from companies’ own funds.

The U.S. federal government was a large source of external funding for R&D ( also referred to as R&D paid for by others ) across all industries. Of the $75 billion paid for by others, the federal government accounted for $24 billion. Seventy-four percent of federal government funding went to three industry groups: aerospace products and parts (North American Industry Classification System [NAICS] code 3364) ($11 billion), scientific research and development services (NAICS 5417) ($4 billion), and computer and electronic products (NAICS 334) ($3 billion). Companies also received funds from other U.S. companies ($27 billion) and foreign companies—including foreign parent companies of U.S. subsidiaries ($23 billion). Eighteen billion dollars (69%) of all business R&D funded by other U.S. companies was for scientific research and development services (NAICS 5417). The distribution of foreign company R&D funding was spread more broadly across multiple industries ( table 2 ). (See “ Survey Information and Data Availability ” for information on the availability of data tables with full industry detail.)

Funds spent for business R&D performed in the United States, by source of funds, selected industry, and company size: 2021

D = suppressed to avoid disclosure of confidential information; i = more than 50% of the estimate is a combination of imputation and reweighting to account for nonresponse; r = relative standard error is more than 50%.

NAICS = North American Industry Classification System; nec = not elsewhere classified.

a All R&D is the cost of R&D paid for and performed by the respondent company and paid for by others outside of the company and performed by the respondent company. b Includes foreign subsidiaries of U.S. companies ($32.1 billion). c Includes foreign parent companies of U.S. subsidiaries ($20.8 billion) and unaffiliated companies ($2.5 billion). Excludes funds from foreign subsidiaries to U.S. companies paid for through intercompany transactions ($32.1 billion). d Includes U.S. state government agencies and laboratories ($0.3 billion); U.S. universities, colleges, and academic researchers (< $0.01 billion); and all other organizations located inside ($0.7 billion) and outside the United States (< $0.01 billion). e Includes only companies with 10 or more domestic employees.

Detail may not add to total because of rounding. Industry classification was based on the dominant business code for domestic R&D performance, where available. For companies that did not report business codes, the classification used for sampling was assigned. Statistics are representative of companies located in the United States that performed or funded $50,000 or more of R&D.

National Center for Science and Engineering Statistics and Census Bureau, Business Enterprise Research and Development Survey, 2021.

Sales, R&D Intensity, and Employment of Companies That Performed or Funded R&D

U.S. companies that performed or funded R&D reported domestic net sales of $13 trillion in 2021 ( table 3 ). ​ Determining the amount of domestic net sales and operating revenues was left to the reporting company. However, guidance was given to include revenues from foreign operations and subsidiaries and from discontinued operations and to exclude intracompany transfers, returns, allowances, freight charges, and excise, sales, and other revenue-based taxes. For all industries, the R&D intensity (R&D-to-sales ratio) was 4.6%; for manufacturers, 5.0%; and for nonmanufacturers, 4.2%. Manufacturing industries with high levels of R&D intensity in 2021 were pharmaceuticals and medicines (NAICS 3254) (16.1%) and computer and electronic products (NAICS 334) (13.0%). Among the nonmanufacturing industries, industries with high levels of R&D intensity were scientific research and development services (NAICS 5417) (41.2%), software publishers (NAICS 5112) (12.9%), and computer systems design and related services (NAICS 5415) (10.2%).

Businesses that performed or funded R&D employed 23.7 million people in the United States in 2021 ( table 3 ). ​ Employment statistics in this InfoBrief are headcounts unless they are designated as full-time equivalent (FTE) estimates. R&D employees include researchers (defined as R&D scientists and engineers and their managers) and the technicians, technologists, and support staff members who work on R&D or who provide direct support to R&D activities. Approximately 2.1 million (9%) were business R&D employees. ​ The number of persons employed who were assigned full time to R&D plus a prorated number of employees who worked on R&D only part of the time was 1.9 million FTEs, of which 1.3 million FTEs were R&D researchers.

Of the 2.1 million people working on R&D in companies that performed or funded business R&D in 2021, 1.5 million were men and 0.6 million were women; 48% of the men and 45% of the women worked in manufacturing industries ( table 4 ). Researchers—that is, scientists, engineers, and their managers—accounted for 1.4 million of the 2.1 million R&D workers (67%). Of the R&D workers, 130,000 (9%) held PhD degrees. R&D technicians numbered 501,000, and 205,000 were grouped as other supporting staff.

Sales, R&D, R&D intensity, and employment for companies that performed or funded business R&D in the United States, by selected industry and company size: 2021

a Dollar values are for goods sold or services rendered by R&D-performing or R&D-funding companies located in the United States to customers outside of the company, including the U.S. federal government, foreign customers, and the company's foreign subsidiaries. Included are revenues from a company’s foreign operations and subsidiaries and from discontinued operations. If a respondent company is owned by a foreign parent company, sales to the parent company and to affiliates not owned by the respondent company are included. Excluded are intracompany transfers, returns, allowances, freight charges, and excise, sales, and other revenue-based taxes. b All R&D is the cost of R&D paid for and performed by the respondent company and paid for by others outside of the company and performed by the respondent company. c R&D intensity is the cost of domestic R&D paid for by the respondent company and others outside of the company and performed by the company divided by domestic net sales of companies that performed or funded R&D. d Data recorded on 12 March represent employment figures for the year. e Headcounts of researchers, R&D managers, technicians, clerical staff, and others assigned to R&D groups. f Includes only companies with 10 or more domestic employees.

Detail may not add to total because of rounding. Industry classification was based on the dominant business code for domestic R&D performance, where available. For companies that did not report business codes, the classification used for sampling was assigned.

Domestic employment, R&D employment by sex and work activity, R&D researchers by level of education, and full-time equivalent researcher employment for companies that performed or funded business R&D in the United States, by industrial sector: 2021

NAICS = North American Industry Classification System.

a Data recorded on 12 March represent employment figures for the year. b Includes R&D scientists and engineers and their managers. c Includes clerical staff and others assigned to R&D groups. d The number of persons employed who were assigned full time to R&D, plus a prorated number of employees who worked on R&D only part of the time.

Detail may not add to total because of rounding. Industry classification was based on the dominant business code for domestic R&D performance, where available. For companies that did not report business codes, the classification used for sampling was assigned. Excludes data for federally funded research and development centers. Also available in the full set of data tables are statistics on domestic R&D employment, by state; foreign R&D personnel headcounts, by country; and headcounts of leased (i.e., external) R&D personnel, by function.

R&D Performance, by Company Size

Small- and medium-sized companies (10–249 domestic employees) performed 9.8% of the nation’s total business R&D in 2021 ( table 3 ). Frascati Manual ; see Organisation for Economic Co-operation and Development (OECD). 2015. Frascati Manual: Guidelines for Collecting and Reporting Data on Research and Experimental Development. The Measurement of Scientific, Technological, and Innovation Activities . Paris: OECD Publishing. Available at https://www.oecd-ilibrary.org/science-and-technology/frascati-manual-2015_9789264239012-en . Anderson and Kindlon (2019) provide estimates of R&D performance and employment using these new classifications over 2008–15. The authors also compare the trends to those observed in SIRD for the time prior to 2008. The ABS, also cosponsored by NCSES and the Census Bureau, collects R&D data from companies with fewer than 10 employees for 2017 and beyond. See Anderson G, Kindlon A; NCSES. 2019. Indicators of R&D in Small Businesses: Data from the 2009–15 Business R&D and Innovation Survey . InfoBrief NSF 19-316. Alexandria, VA: National Science Foundation. Available at https://www.nsf.gov/statistics/2019/nsf19316/ ." data-bs-content="Company size classifications changed for 2017 and subsequent years in response to the revised Frascati Manual ; see Organisation for Economic Co-operation and Development (OECD). 2015. Frascati Manual: Guidelines for Collecting and Reporting Data on Research and Experimental Development. The Measurement of Scientific, Technological, and Innovation Activities . Paris: OECD Publishing. Available at https://www.oecd-ilibrary.org/science-and-technology/frascati-manual-2015_9789264239012-en . Anderson and Kindlon (2019) provide estimates of R&D performance and employment using these new classifications over 2008–15. The authors also compare the trends to those observed in SIRD for the time prior to 2008. The ABS, also cosponsored by NCSES and the Census Bureau, collects R&D data from companies with fewer than 10 employees for 2017 and beyond. See Anderson G, Kindlon A; NCSES. 2019. Indicators of R&D in Small Businesses: Data from the 2009–15 Business R&D and Innovation Survey . InfoBrief NSF 19-316. Alexandria, VA: National Science Foundation. Available at https://www.nsf.gov/statistics/2019/nsf19316/ ." data-endnote-uuid="bbd761ec-4ed8-45ec-810e-9b53647fe422">​ Company size classifications changed for 2017 and subsequent years in response to the revised Frascati Manual ; see Organisation for Economic Co-operation and Development (OECD). 2015. Frascati Manual: Guidelines for Collecting and Reporting Data on Research and Experimental Development. The Measurement of Scientific, Technological, and Innovation Activities . Paris: OECD Publishing. Available at https://www.oecd-ilibrary.org/science-and-technology/frascati-manual-2015_9789264239012-en . Anderson and Kindlon (2019) provide estimates of R&D performance and employment using these new classifications over 2008–15. The authors also compare the trends to those observed in SIRD for the time prior to 2008. The ABS, also cosponsored by NCSES and the Census Bureau, collects R&D data from companies with fewer than 10 employees for 2017 and beyond. See Anderson G, Kindlon A; NCSES. 2019. Indicators of R&D in Small Businesses: Data from the 2009–15 Business R&D and Innovation Survey . InfoBrief NSF 19-316. Alexandria, VA: National Science Foundation. Available at https://www.nsf.gov/statistics/2019/nsf19316/ . For these companies as a group, the R&D intensity was 8.8%. These companies accounted for 5% of sales and employed 7% of the 23.7 million employees who worked for R&D-performing or R&D-funding companies. They employed 18% of the 2.1 million employees engaged in business R&D in the United States.

Large companies with 250–24,999 domestic employees performed 52% of the nation’s total business R&D in 2021, and their R&D intensity was 4.7%. They accounted for 51% of sales, employed 42% of those who worked for R&D-performing or R&D-funding companies, and employed 51% of R&D employees in the United States.

The largest companies (25,000 or more domestic employees) performed 38% of the nation’s total business R&D in 2021, and their R&D intensity was 4.0%. They accounted for 44% of sales, employed 51% of those who worked for R&D-performing or R&D-funding companies, and employed 31% of business R&D employees in the United States.

R&D Performance, by State

In 2021, of the $602 billion of R&D performed in the United States, businesses in California alone accounted for 35.1% ( table 5 ). Other states with large amounts of business R&D were Washington (8.1% of the national total in 2021), Massachusetts (6.6%), Texas (4.7%), New York (4.4%), and New Jersey (4.2%). Over Half of U.S. Business R&D Performed in 10 Metropolitan Areas in 2015 . InfoBrief NSF 19-322. Alexandria, VA: National Science Foundation. Available at https://www.nsf.gov/statistics/2019/nsf19322/ . Also see Shackelford B, Wolfe R; NCSES. 2020. Businesses Performed 60% of Their U.S. R&D in 10 Metropolitan Areas in 2018 . InfoBrief NSF 21-331. Alexandria, VA: National Science Foundation. Available at https://ncses.nsf.gov/pubs/nsf21331 . Information and statistics on U.S. state trends in R&D, science and engineering education, workforce, patents and publications, and knowledge-intensive industries is also available in the Science and Engineering State Indicators data tool at https://ncses.nsf.gov/indicators/states ." data-bs-content="In addition to statistics for all states and for all states by industry, below-state level statistics are available in the full set of data tables and in other InfoBriefs; see Shackelford B, Wolfe R; NCSES. 2019. Over Half of U.S. Business R&D Performed in 10 Metropolitan Areas in 2015 . InfoBrief NSF 19-322. Alexandria, VA: National Science Foundation. Available at https://www.nsf.gov/statistics/2019/nsf19322/ . Also see Shackelford B, Wolfe R; NCSES. 2020. Businesses Performed 60% of Their U.S. R&D in 10 Metropolitan Areas in 2018 . InfoBrief NSF 21-331. Alexandria, VA: National Science Foundation. Available at https://ncses.nsf.gov/pubs/nsf21331 . Information and statistics on U.S. state trends in R&D, science and engineering education, workforce, patents and publications, and knowledge-intensive industries is also available in the Science and Engineering State Indicators data tool at https://ncses.nsf.gov/indicators/states ." data-endnote-uuid="8051c6cd-6983-4989-9a6c-bbc5713eaaa4">​ In addition to statistics for all states and for all states by industry, below-state level statistics are available in the full set of data tables and in other InfoBriefs; see Shackelford B, Wolfe R; NCSES. 2019. Over Half of U.S. Business R&D Performed in 10 Metropolitan Areas in 2015 . InfoBrief NSF 19-322. Alexandria, VA: National Science Foundation. Available at https://www.nsf.gov/statistics/2019/nsf19322/ . Also see Shackelford B, Wolfe R; NCSES. 2020. Businesses Performed 60% of Their U.S. R&D in 10 Metropolitan Areas in 2018 . InfoBrief NSF 21-331. Alexandria, VA: National Science Foundation. Available at https://ncses.nsf.gov/pubs/nsf21331 . Information and statistics on U.S. state trends in R&D, science and engineering education, workforce, patents and publications, and knowledge-intensive industries is also available in the Science and Engineering State Indicators data tool at https://ncses.nsf.gov/indicators/states .

Funds spent for business R&D performed in the United States, by state and source of funds: 2021

a All R&D is the cost of domestic R&D paid for by the respondent company and others outside of the company and performed by the respondent company. b Includes data reported that were not allocated to a specific state by multi-establishment companies. For single-establishment companies, data reported were allocated to the state in the address used to mail the survey form.

Capital Expenditures

Companies that performed or funded R&D in the United States in 2021 spent $793 billion on capital, that is, assets with expected useful lives of more than 1 year ( table 6 ). Of this amount, $53 billion (7%) was for assets used for domestic R&D operations (i.e., land acquisitions, buildings and land improvement, equipment, capitalized software, and other assets). Companies in manufacturing industries spent $28 billion on capital for domestic R&D, and companies in nonmanufacturing industries spent $24 billion. Industries with high levels of capital expenditures on assets used for domestic R&D in 2021 were pharmaceuticals and medicines (NAICS 3254) ($7.5 billion, or 14% of national capital expenditures on assets used for R&D) and semiconductor and other electronic products (NAICS 3344) ($5 billion, or 9%). Among all types of capital assets, manufacturing industries spent the most on equipment ($15 billion, or 53% of total capital assets used for domestic R&D), and nonmanufacturing industries disbursed the most on capitalized software ($13.7 billion, or 56%).

Capital expenditures in the United States, total and used for domestic R&D, by type of expenditure, industry, and company size: 2021

* = amount < $500,000; i = more than 50% of the estimate is a combination of imputation and reweighting to account for nonresponse; r = relative standard error is more than 50%.

a Domestic R&D is the R&D paid for by the respondent company and others outside of the company and performed by the company. b Capital expenditures are payments by a business for assets that usually have a useful life of more than 1 year. The value of assets acquired or improved through capital expenditures is recorded on a company’s balance sheet. BERD Survey statistics exclude the cost of assets acquired through mergers and acquisitions. c Capital expenditures for long-lived assets used in a company’s R&D operations are not included in its R&D expense, but any depreciation recorded for those assets is included in its R&D expense. For 2021, depreciation associated with domestic R&D paid for and performed by the company was $18.4 billion and with domestic R&D performed by the company and paid for by others was $2.7 billion. d Includes the cost of purchased or improved buildings and other facilities that are fixed to the land. e Includes the cost of other capital expenditures, including purchased patents and other intangible assets, and expenditures not distributed among the categories shown. f Includes only companies with 10 or more domestic employees.

Detail may not add to total because of rounding. Industry classification was based on dominant business code for domestic R&D performance, where available. For companies that did not report business codes, the classification used for sampling was assigned. An estimate range may be displayed in place of a single estimate to avoid disclosing operations of individual companies.

National Center for Science and Engineering Statistics and U.S. Census Bureau, Business Enterprise Research and Development Survey, 2021.

Survey Information and Data Availability

The sample for the BERD Survey was selected to represent all for-profit, nonfarm companies that were publicly or privately held, had 10 or more employees in the United States, and performed or funded R&D either domestically or abroad. The estimates in this InfoBrief are based on responses from a sample of the population and may differ from actual values because of sampling variability or other factors. As a result, apparent differences between the estimates for two or more groups may not be statistically significant. All comparative statements in this InfoBrief have undergone statistical testing and are significant at the 90% confidence level unless otherwise noted. The variances of estimates in this report were calculated using design-based formulas. Also, because the statistics from the survey are based on a sample, they are subject to both sampling and nonsampling errors. (See the 2021 “Technical Notes” at https://ncses.nsf.gov/surveys/business-enterprise-research-development/ .) ​ The Census Bureau reviewed the information in this InfoBrief for unauthorized disclosure of confidential information and approved the disclosure avoidance practices applied (Project No. P-7504682, Disclosure Review Board (DRB) approval number: CBDRB-FY23-0161).

Beginning in survey year 2018, companies that performed or funded less than $50,000 of R&D were excluded from tabulation.

In this InfoBrief, money amounts are expressed in current U.S. dollars and are not adjusted for inflation. A company is defined as a business organization located in the United States, either U.S. owned or a U.S. affiliate of a foreign parent company, of one or more establishments under common ownership or control.

For 2020, a total of 47,500 companies were sampled to represent the population of 1,140,000 companies; for 2021, a total of 47,500 companies were sampled, representing 1,137,000 companies. The actual numbers of reporting units in the sample that remained within the scope of the survey between sample selection and tabulation were 44,500 for 2020 and 44,000 for 2021. These lower counts represent the number of reporting units that were determined to be within the scope of the survey after all data collected were processed. Reasons for the reduced counts include mergers, acquisitions, and instances where companies had fewer than 10 employees in the United States or had gone out of business in the interim. Of these in-scope reporting units, 67% were considered to have met the criteria for a complete response to the 2020 survey; 69% fulfilled the 2021 complete response criteria. Coverage of the previous year’s known positive R&D stratum for 2020 was 92%; the coverage rate for 2021 was also 92%. Industry classification was based on the dominant business activity for domestic R&D performance, where available. For reporting units that did not report business activity codes for R&D, the classification used for sampling was assigned.

The estimation methodology for state estimates in the BERD Survey takes the form of a hybrid estimator, combining the unweighted reported amount, by state, with a weighted amount apportioned (or raked) across states with relevant industrial activity. The hybrid estimator smooths the estimate over states with R&D activity, by industry, and accounts for real observed change within a state. Table 5 shows the adjusted state estimates after this estimation methodology was applied.

The full set of data tables from the 2021 survey will be available at the BERD Survey page . Individual data tables and tables with relative standard errors and imputation rates from the 2021 survey are available from the author in advance of the full release. To minimize reporting burden, survey items are rotated on and off the survey on an odd- and even-numbered year schedule. Statistics on patents, intellectual property, and technology transfer activities were rotated off the survey for 2021. Items rotated on the survey for 2021 include questions on R&D performed by others by type of performer, federal R&D by government agency, and R&D by application area.

The BERD Survey contains confidential data that are protected under Title 13 and Title 26 of the U.S. Code. Restricted microdata can be accessed at the secure Federal Statistical Research Data Centers (FSRDCs) administered by the Census Bureau. FSRDCs are partnerships between federal statistical agencies and leading research institutions. FSRDCs provide secure environments supporting qualified researchers using restricted-access data while protecting respondent confidentiality. Researchers interested in using the microdata can submit a proposal to the Census Bureau, which evaluates proposals based on their benefit to the Census Bureau, scientific merit, feasibility, and risk of disclosure. To learn more about the FSRDCs and how to apply, please visit https://www.census.gov/about/adrm/fsrdc.html .

Suggested Citation

Britt R; National Center for Science and Engineering Statistics (NCSES). 2023. Business R&D Performance in the United States Tops $600 Billion in 2021 . NSF 23-350. Alexandria, VA: National Science Foundation. Available at http://ncses.nsf.gov/pubs/nsf23350 .

1 NSF has cosponsored an annual business R&D survey since 1953. The Survey of Industrial Research and Development (SIRD) collected data for 1953–2007, and its successor, the Business R&D and Innovation Survey (BRDIS), collected data for 2008–16. Beginning with 2017, the collection of innovation data was moved to the Annual Business Survey (ABS), another survey cosponsored with the Census Bureau, and BRDIS became the Business Research and Development Survey (BRDS). Beginning with 2019, the business R&D data collection reported here was renamed the Business Enterprise Research and Development (BERD) Survey for international comparability.

2 Determining the amount of domestic net sales and operating revenues was left to the reporting company. However, guidance was given to include revenues from foreign operations and subsidiaries and from discontinued operations and to exclude intracompany transfers, returns, allowances, freight charges, and excise, sales, and other revenue-based taxes.

3 Employment statistics in this InfoBrief are headcounts unless they are designated as full-time equivalent (FTE) estimates. R&D employees include researchers (defined as R&D scientists and engineers and their managers) and the technicians, technologists, and support staff members who work on R&D or who provide direct support to R&D activities.

4 The number of persons employed who were assigned full time to R&D plus a prorated number of employees who worked on R&D only part of the time was 1.9 million FTEs, of which 1.3 million FTEs were R&D researchers.

5 Company size classifications changed for 2017 and subsequent years in response to the revised Frascati Manual ; see Organisation for Economic Co-operation and Development (OECD). 2015. Frascati Manual: Guidelines for Collecting and Reporting Data on Research and Experimental Development. The Measurement of Scientific, Technological, and Innovation Activities . Paris: OECD Publishing. Available at https://www.oecd-ilibrary.org/science-and-technology/frascati-manual-2015_9789264239012-en . Anderson and Kindlon (2019) provide estimates of R&D performance and employment using these new classifications over 2008–15. The authors also compare the trends to those observed in SIRD for the time prior to 2008. The ABS, also cosponsored by NCSES and the Census Bureau, collects R&D data from companies with fewer than 10 employees for 2017 and beyond. See Anderson G, Kindlon A; NCSES. 2019. Indicators of R&D in Small Businesses: Data from the 2009–15 Business R&D and Innovation Survey . InfoBrief NSF 19-316. Alexandria, VA: National Science Foundation. Available at https://www.nsf.gov/statistics/2019/nsf19316/ .

6 In addition to statistics for all states and for all states by industry, below-state level statistics are available in the full set of data tables and in other InfoBriefs; see Shackelford B, Wolfe R; NCSES. 2019. Over Half of U.S. Business R&D Performed in 10 Metropolitan Areas in 2015 . InfoBrief NSF 19-322. Alexandria, VA: National Science Foundation. Available at https://www.nsf.gov/statistics/2019/nsf19322/ . Also see Shackelford B, Wolfe R; NCSES. 2020. Businesses Performed 60% of Their U.S. R&D in 10 Metropolitan Areas in 2018 . InfoBrief NSF 21-331. Alexandria, VA: National Science Foundation. Available at https://ncses.nsf.gov/pubs/nsf21331 . Information and statistics on U.S. state trends in R&D, science and engineering education, workforce, patents and publications, and knowledge-intensive industries is also available in the Science and Engineering State Indicators data tool at https://ncses.nsf.gov/indicators/states .

7 The Census Bureau reviewed the information in this InfoBrief for unauthorized disclosure of confidential information and approved the disclosure avoidance practices applied (Project No. P-7504682, Disclosure Review Board (DRB) approval number: CBDRB-FY23-0161).

Report Author

Ronda Britt Survey Manager NCSES Tel: (703) 292-7765 E-mail: [email protected]

National Center for Science and Engineering Statistics Directorate for Social, Behavioral and Economic Sciences National Science Foundation 2415 Eisenhower Avenue, Suite W14200 Alexandria, VA 22314 Tel: (703) 292-8780 FIRS: (800) 877-8339 TDD: (800) 281-8749 E-mail: [email protected]

Source Data & Analysis

Data Tables (NSF 23-351)

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Table of Contents

What is data collection, why do we need data collection, what are the different data collection methods, data collection tools, the importance of ensuring accurate and appropriate data collection, issues related to maintaining the integrity of data collection, what are common challenges in data collection, what are the key steps in the data collection process, data collection considerations and best practices, choose the right data science program, are you interested in a career in data science, what is data collection: methods, types, tools.

The process of gathering and analyzing accurate data from various sources to find answers to research problems, trends and probabilities, etc., to evaluate possible outcomes is Known as Data Collection. Knowledge is power, information is knowledge, and data is information in digitized form, at least as defined in IT. Hence, data is power. But before you can leverage that data into a successful strategy for your organization or business, you need to gather it. That’s your first step.

So, to help you get the process started, we shine a spotlight on data collection. What exactly is it? Believe it or not, it’s more than just doing a Google search! Furthermore, what are the different types of data collection? And what kinds of data collection tools and data collection techniques exist?

If you want to get up to speed about what is data collection process, you’ve come to the right place. 

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Data collection is the process of collecting and evaluating information or data from multiple sources to find answers to research problems, answer questions, evaluate outcomes, and forecast trends and probabilities. It is an essential phase in all types of research, analysis, and decision-making, including that done in the social sciences, business, and healthcare.

Accurate data collection is necessary to make informed business decisions, ensure quality assurance, and keep research integrity.

During data collection, the researchers must identify the data types, the sources of data, and what methods are being used. We will soon see that there are many different data collection methods . There is heavy reliance on data collection in research, commercial, and government fields.

Before an analyst begins collecting data, they must answer three questions first:

• What’s the goal or purpose of this research?
• What kinds of data are they planning on gathering?
• What methods and procedures will be used to collect, store, and process the information?

Additionally, we can break up data into qualitative and quantitative types. Qualitative data covers descriptions such as color, size, quality, and appearance. Quantitative data, unsurprisingly, deals with numbers, such as statistics, poll numbers, percentages, etc.

Before a judge makes a ruling in a court case or a general creates a plan of attack, they must have as many relevant facts as possible. The best courses of action come from informed decisions, and information and data are synonymous.

The concept of data collection isn’t a new one, as we’ll see later, but the world has changed. There is far more data available today, and it exists in forms that were unheard of a century ago. The data collection process has had to change and grow with the times, keeping pace with technology.

Whether you’re in the world of academia, trying to conduct research, or part of the commercial sector, thinking of how to promote a new product, you need data collection to help you make better choices.

Now that you know what is data collection and why we need it, let's take a look at the different methods of data collection. While the phrase “data collection” may sound all high-tech and digital, it doesn’t necessarily entail things like computers, big data , and the internet. Data collection could mean a telephone survey, a mail-in comment card, or even some guy with a clipboard asking passersby some questions. But let’s see if we can sort the different data collection methods into a semblance of organized categories.

Primary and secondary methods of data collection are two approaches used to gather information for research or analysis purposes. Let's explore each data collection method in detail:

1. Primary Data Collection:

Primary data collection involves the collection of original data directly from the source or through direct interaction with the respondents. This method allows researchers to obtain firsthand information specifically tailored to their research objectives. There are various techniques for primary data collection, including:

a. Surveys and Questionnaires: Researchers design structured questionnaires or surveys to collect data from individuals or groups. These can be conducted through face-to-face interviews, telephone calls, mail, or online platforms.

b. Interviews: Interviews involve direct interaction between the researcher and the respondent. They can be conducted in person, over the phone, or through video conferencing. Interviews can be structured (with predefined questions), semi-structured (allowing flexibility), or unstructured (more conversational).

c. Observations: Researchers observe and record behaviors, actions, or events in their natural setting. This method is useful for gathering data on human behavior, interactions, or phenomena without direct intervention.

d. Experiments: Experimental studies involve the manipulation of variables to observe their impact on the outcome. Researchers control the conditions and collect data to draw conclusions about cause-and-effect relationships.

e. Focus Groups: Focus groups bring together a small group of individuals who discuss specific topics in a moderated setting. This method helps in understanding opinions, perceptions, and experiences shared by the participants.

2. Secondary Data Collection:

Secondary data collection involves using existing data collected by someone else for a purpose different from the original intent. Researchers analyze and interpret this data to extract relevant information. Secondary data can be obtained from various sources, including:

a. Published Sources: Researchers refer to books, academic journals, magazines, newspapers, government reports, and other published materials that contain relevant data.

b. Online Databases: Numerous online databases provide access to a wide range of secondary data, such as research articles, statistical information, economic data, and social surveys.

c. Government and Institutional Records: Government agencies, research institutions, and organizations often maintain databases or records that can be used for research purposes.

d. Publicly Available Data: Data shared by individuals, organizations, or communities on public platforms, websites, or social media can be accessed and utilized for research.

e. Past Research Studies: Previous research studies and their findings can serve as valuable secondary data sources. Researchers can review and analyze the data to gain insights or build upon existing knowledge.

Now that we’ve explained the various techniques, let’s narrow our focus even further by looking at some specific tools. For example, we mentioned interviews as a technique, but we can further break that down into different interview types (or “tools”).

Word Association

The researcher gives the respondent a set of words and asks them what comes to mind when they hear each word.

Sentence Completion

Researchers use sentence completion to understand what kind of ideas the respondent has. This tool involves giving an incomplete sentence and seeing how the interviewee finishes it.

Role-Playing

Respondents are presented with an imaginary situation and asked how they would act or react if it was real.

In-Person Surveys

The researcher asks questions in person.

Online/Web Surveys

These surveys are easy to accomplish, but some users may be unwilling to answer truthfully, if at all.

Mobile Surveys

These surveys take advantage of the increasing proliferation of mobile technology. Mobile collection surveys rely on mobile devices like tablets or smartphones to conduct surveys via SMS or mobile apps.

Phone Surveys

No researcher can call thousands of people at once, so they need a third party to handle the chore. However, many people have call screening and won’t answer.

Observation

Sometimes, the simplest method is the best. Researchers who make direct observations collect data quickly and easily, with little intrusion or third-party bias. Naturally, it’s only effective in small-scale situations.

Accurate data collecting is crucial to preserving the integrity of research, regardless of the subject of study or preferred method for defining data (quantitative, qualitative). Errors are less likely to occur when the right data gathering tools are used (whether they are brand-new ones, updated versions of them, or already available).

Among the effects of data collection done incorrectly, include the following -

• Erroneous conclusions that squander resources
• Decisions that compromise public policy
• Incapacity to correctly respond to research inquiries
• Bringing harm to participants who are humans or animals
• Deceiving other researchers into pursuing futile research avenues
• The study's inability to be replicated and validated

When these study findings are used to support recommendations for public policy, there is the potential to result in disproportionate harm, even if the degree of influence from flawed data collecting may vary by discipline and the type of investigation.

Let us now look at the various issues that we might face while maintaining the integrity of data collection.

In order to assist the errors detection process in the data gathering process, whether they were done purposefully (deliberate falsifications) or not, maintaining data integrity is the main justification (systematic or random errors).

Quality assurance and quality control are two strategies that help protect data integrity and guarantee the scientific validity of study results.

Each strategy is used at various stages of the research timeline:

• Quality control - tasks that are performed both after and during data collecting
• Quality assurance - events that happen before data gathering starts

Let us explore each of them in more detail now.

Quality Assurance

As data collecting comes before quality assurance, its primary goal is "prevention" (i.e., forestalling problems with data collection). The best way to protect the accuracy of data collection is through prevention. The uniformity of protocol created in the thorough and exhaustive procedures manual for data collecting serves as the best example of this proactive step. 

The likelihood of failing to spot issues and mistakes early in the research attempt increases when guides are written poorly. There are several ways to show these shortcomings:

• Failure to determine the precise subjects and methods for retraining or training staff employees in data collecting
• List of goods to be collected, in part
• There isn't a system in place to track modifications to processes that may occur as the investigation continues.
• Instead of detailed, step-by-step instructions on how to deliver tests, there is a vague description of the data gathering tools that will be employed.
• Uncertainty regarding the date, procedure, and identity of the person or people in charge of examining the data
• Incomprehensible guidelines for using, adjusting, and calibrating the data collection equipment.

Now, let us look at how to ensure Quality Control.

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Quality Control

Despite the fact that quality control actions (detection/monitoring and intervention) take place both after and during data collection, the specifics should be meticulously detailed in the procedures manual. Establishing monitoring systems requires a specific communication structure, which is a prerequisite. Following the discovery of data collection problems, there should be no ambiguity regarding the information flow between the primary investigators and staff personnel. A poorly designed communication system promotes slack oversight and reduces opportunities for error detection.

Direct staff observation conference calls, during site visits, or frequent or routine assessments of data reports to spot discrepancies, excessive numbers, or invalid codes can all be used as forms of detection or monitoring. Site visits might not be appropriate for all disciplines. Still, without routine auditing of records, whether qualitative or quantitative, it will be challenging for investigators to confirm that data gathering is taking place in accordance with the manual's defined methods. Additionally, quality control determines the appropriate solutions, or "actions," to fix flawed data gathering procedures and reduce recurrences.

Problems with data collection, for instance, that call for immediate action include:

• Fraud or misbehavior
• Systematic mistakes, procedure violations 
• Individual data items with errors
• Issues with certain staff members or a site's performance 

Researchers are trained to include one or more secondary measures that can be used to verify the quality of information being obtained from the human subject in the social and behavioral sciences where primary data collection entails using human subjects. 

For instance, a researcher conducting a survey would be interested in learning more about the prevalence of risky behaviors among young adults as well as the social factors that influence these risky behaviors' propensity for and frequency. Let us now explore the common challenges with regard to data collection.

There are some prevalent challenges faced while collecting data, let us explore a few of them to understand them better and avoid them.

Data Quality Issues

The main threat to the broad and successful application of machine learning is poor data quality. Data quality must be your top priority if you want to make technologies like machine learning work for you. Let's talk about some of the most prevalent data quality problems in this blog article and how to fix them.

Inconsistent Data

When working with various data sources, it's conceivable that the same information will have discrepancies between sources. The differences could be in formats, units, or occasionally spellings. The introduction of inconsistent data might also occur during firm mergers or relocations. Inconsistencies in data have a tendency to accumulate and reduce the value of data if they are not continually resolved. Organizations that have heavily focused on data consistency do so because they only want reliable data to support their analytics.

Data Downtime

Data is the driving force behind the decisions and operations of data-driven businesses. However, there may be brief periods when their data is unreliable or not prepared. Customer complaints and subpar analytical outcomes are only two ways that this data unavailability can have a significant impact on businesses. A data engineer spends about 80% of their time updating, maintaining, and guaranteeing the integrity of the data pipeline. In order to ask the next business question, there is a high marginal cost due to the lengthy operational lead time from data capture to insight.

Schema modifications and migration problems are just two examples of the causes of data downtime. Data pipelines can be difficult due to their size and complexity. Data downtime must be continuously monitored, and it must be reduced through automation.

Ambiguous Data

Even with thorough oversight, some errors can still occur in massive databases or data lakes. For data streaming at a fast speed, the issue becomes more overwhelming. Spelling mistakes can go unnoticed, formatting difficulties can occur, and column heads might be deceptive. This unclear data might cause a number of problems for reporting and analytics.

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Duplicate Data

Streaming data, local databases, and cloud data lakes are just a few of the sources of data that modern enterprises must contend with. They might also have application and system silos. These sources are likely to duplicate and overlap each other quite a bit. For instance, duplicate contact information has a substantial impact on customer experience. If certain prospects are ignored while others are engaged repeatedly, marketing campaigns suffer. The likelihood of biased analytical outcomes increases when duplicate data are present. It can also result in ML models with biased training data.

Too Much Data

While we emphasize data-driven analytics and its advantages, a data quality problem with excessive data exists. There is a risk of getting lost in an abundance of data when searching for information pertinent to your analytical efforts. Data scientists, data analysts, and business users devote 80% of their work to finding and organizing the appropriate data. With an increase in data volume, other problems with data quality become more serious, particularly when dealing with streaming data and big files or databases.

Inaccurate Data

For highly regulated businesses like healthcare, data accuracy is crucial. Given the current experience, it is more important than ever to increase the data quality for COVID-19 and later pandemics. Inaccurate information does not provide you with a true picture of the situation and cannot be used to plan the best course of action. Personalized customer experiences and marketing strategies underperform if your customer data is inaccurate.

Data inaccuracies can be attributed to a number of things, including data degradation, human mistake, and data drift. Worldwide data decay occurs at a rate of about 3% per month, which is quite concerning. Data integrity can be compromised while being transferred between different systems, and data quality might deteriorate with time.

Hidden Data

The majority of businesses only utilize a portion of their data, with the remainder sometimes being lost in data silos or discarded in data graveyards. For instance, the customer service team might not receive client data from sales, missing an opportunity to build more precise and comprehensive customer profiles. Missing out on possibilities to develop novel products, enhance services, and streamline procedures is caused by hidden data.

Finding Relevant Data

Finding relevant data is not so easy. There are several factors that we need to consider while trying to find relevant data, which include -

• Relevant Domain
• Relevant demographics
• Relevant Time period and so many more factors that we need to consider while trying to find relevant data.

Data that is not relevant to our study in any of the factors render it obsolete and we cannot effectively proceed with its analysis. This could lead to incomplete research or analysis, re-collecting data again and again, or shutting down the study.

Deciding the Data to Collect

Determining what data to collect is one of the most important factors while collecting data and should be one of the first factors while collecting data. We must choose the subjects the data will cover, the sources we will be used to gather it, and the quantity of information we will require. Our responses to these queries will depend on our aims, or what we expect to achieve utilizing your data. As an illustration, we may choose to gather information on the categories of articles that website visitors between the ages of 20 and 50 most frequently access. We can also decide to compile data on the typical age of all the clients who made a purchase from your business over the previous month.

Not addressing this could lead to double work and collection of irrelevant data or ruining your study as a whole.

Dealing With Big Data

Big data refers to exceedingly massive data sets with more intricate and diversified structures. These traits typically result in increased challenges while storing, analyzing, and using additional methods of extracting results. Big data refers especially to data sets that are quite enormous or intricate that conventional data processing tools are insufficient. The overwhelming amount of data, both unstructured and structured, that a business faces on a daily basis. 

The amount of data produced by healthcare applications, the internet, social networking sites social, sensor networks, and many other businesses are rapidly growing as a result of recent technological advancements. Big data refers to the vast volume of data created from numerous sources in a variety of formats at extremely fast rates. Dealing with this kind of data is one of the many challenges of Data Collection and is a crucial step toward collecting effective data. 

Low Response and Other Research Issues

Poor design and low response rates were shown to be two issues with data collecting, particularly in health surveys that used questionnaires. This might lead to an insufficient or inadequate supply of data for the study. Creating an incentivized data collection program might be beneficial in this case to get more responses.

Now, let us look at the key steps in the data collection process.

In the Data Collection Process, there are 5 key steps. They are explained briefly below -

1. Decide What Data You Want to Gather

The first thing that we need to do is decide what information we want to gather. We must choose the subjects the data will cover, the sources we will use to gather it, and the quantity of information that we would require. For instance, we may choose to gather information on the categories of products that an average e-commerce website visitor between the ages of 30 and 45 most frequently searches for. 

2. Establish a Deadline for Data Collection

The process of creating a strategy for data collection can now begin. We should set a deadline for our data collection at the outset of our planning phase. Some forms of data we might want to continuously collect. We might want to build up a technique for tracking transactional data and website visitor statistics over the long term, for instance. However, we will track the data throughout a certain time frame if we are tracking it for a particular campaign. In these situations, we will have a schedule for when we will begin and finish gathering data. 

3. Select a Data Collection Approach

We will select the data collection technique that will serve as the foundation of our data gathering plan at this stage. We must take into account the type of information that we wish to gather, the time period during which we will receive it, and the other factors we decide on to choose the best gathering strategy.

4. Gather Information

Once our plan is complete, we can put our data collection plan into action and begin gathering data. In our DMP, we can store and arrange our data. We need to be careful to follow our plan and keep an eye on how it's doing. Especially if we are collecting data regularly, setting up a timetable for when we will be checking in on how our data gathering is going may be helpful. As circumstances alter and we learn new details, we might need to amend our plan.

5. Examine the Information and Apply Your Findings

It's time to examine our data and arrange our findings after we have gathered all of our information. The analysis stage is essential because it transforms unprocessed data into insightful knowledge that can be applied to better our marketing plans, goods, and business judgments. The analytics tools included in our DMP can be used to assist with this phase. We can put the discoveries to use to enhance our business once we have discovered the patterns and insights in our data.

Let us now look at some data collection considerations and best practices that one might follow.

We must carefully plan before spending time and money traveling to the field to gather data. While saving time and resources, effective data collection strategies can help us collect richer, more accurate, and richer data.

Below, we will be discussing some of the best practices that we can follow for the best results -

1. Take Into Account the Price of Each Extra Data Point

Once we have decided on the data we want to gather, we need to make sure to take the expense of doing so into account. Our surveyors and respondents will incur additional costs for each additional data point or survey question.

2. Plan How to Gather Each Data Piece

There is a dearth of freely accessible data. Sometimes the data is there, but we may not have access to it. For instance, unless we have a compelling cause, we cannot openly view another person's medical information. It could be challenging to measure several types of information.

Consider how time-consuming and difficult it will be to gather each piece of information while deciding what data to acquire.

3. Think About Your Choices for Data Collecting Using Mobile Devices

Mobile-based data collecting can be divided into three categories -

• IVRS (interactive voice response technology) -  Will call the respondents and ask them questions that have already been recorded. 
• SMS data collection - Will send a text message to the respondent, who can then respond to questions by text on their phone. 
• Field surveyors - Can directly enter data into an interactive questionnaire while speaking to each respondent, thanks to smartphone apps.

We need to make sure to select the appropriate tool for our survey and responders because each one has its own disadvantages and advantages.

4. Carefully Consider the Data You Need to Gather

It's all too easy to get information about anything and everything, but it's crucial to only gather the information that we require. 

It is helpful to consider these 3 questions:

• What details will be helpful?
• What details are available?
• What specific details do you require?

5. Remember to Consider Identifiers

Identifiers, or details describing the context and source of a survey response, are just as crucial as the information about the subject or program that we are actually researching.

In general, adding more identifiers will enable us to pinpoint our program's successes and failures with greater accuracy, but moderation is the key.

6. Data Collecting Through Mobile Devices is the Way to Go

Although collecting data on paper is still common, modern technology relies heavily on mobile devices. They enable us to gather many various types of data at relatively lower prices and are accurate as well as quick. There aren't many reasons not to pick mobile-based data collecting with the boom of low-cost Android devices that are available nowadays.

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1. What is data collection with example?

Data collection is the process of collecting and analyzing information on relevant variables in a predetermined, methodical way so that one can respond to specific research questions, test hypotheses, and assess results. Data collection can be either qualitative or quantitative. Example: A company collects customer feedback through online surveys and social media monitoring to improve their products and services.

2. What are the primary data collection methods?

As is well known, gathering primary data is costly and time intensive. The main techniques for gathering data are observation, interviews, questionnaires, schedules, and surveys.

3. What are data collection tools?

The term "data collecting tools" refers to the tools/devices used to gather data, such as a paper questionnaire or a system for computer-assisted interviews. Tools used to gather data include case studies, checklists, interviews, occasionally observation, surveys, and questionnaires.

4. What’s the difference between quantitative and qualitative methods?

While qualitative research focuses on words and meanings, quantitative research deals with figures and statistics. You can systematically measure variables and test hypotheses using quantitative methods. You can delve deeper into ideas and experiences using qualitative methodologies.

5. What are quantitative data collection methods?

While there are numerous other ways to get quantitative information, the methods indicated above—probability sampling, interviews, questionnaire observation, and document review—are the most typical and frequently employed, whether collecting information offline or online.

6. What is mixed methods research?

User research that includes both qualitative and quantitative techniques is known as mixed methods research. For deeper user insights, mixed methods research combines insightful user data with useful statistics.

7. What are the benefits of collecting data?

Collecting data offers several benefits, including:

• Knowledge and Insight
• Evidence-Based Decision Making
• Problem Identification and Solution
• Validation and Evaluation
• Identifying Trends and Predictions
• Support for Research and Development
• Policy Development
• Quality Improvement
• Personalization and Targeting
• Knowledge Sharing and Collaboration

8. What’s the difference between reliability and validity?

Reliability is about consistency and stability, while validity is about accuracy and appropriateness. Reliability focuses on the consistency of results, while validity focuses on whether the results are actually measuring what they are intended to measure. Both reliability and validity are crucial considerations in research to ensure the trustworthiness and meaningfulness of the collected data and measurements.

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Program Name Data Scientist Master's Program Post Graduate Program In Data Science Post Graduate Program In Data Science Geo All Geos All Geos Not Applicable in US University Simplilearn Purdue Caltech Course Duration 11 Months 11 Months 11 Months Coding Experience Required Basic Basic No Skills You Will Learn 10+ skills including data structure, data manipulation, NumPy, Scikit-Learn, Tableau and more 8+ skills including Exploratory Data Analysis, Descriptive Statistics, Inferential Statistics, and more 8+ skills including Supervised & Unsupervised Learning Deep Learning Data Visualization, and more Additional Benefits Applied Learning via Capstone and 25+ Data Science Projects Purdue Alumni Association Membership Free IIMJobs Pro-Membership of 6 months Resume Building Assistance Upto 14 CEU Credits Caltech CTME Circle Membership Cost $$ $$$$ $$$$ Explore Program Explore Program Explore Program

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Studies on the Value of Data

The U.S. Bureau of Economic Analysis has undertaken a series of studies that present methods for quantifying the value of simple data that can be differentiated from the complex data created by highly skilled workers that was studied in Calderón and Rassier 2022 . Preliminary studies in this series focus on tax data, individual credit data, and driving data. Additional examples include medical records, educational transcripts, business financial records, customer data, equipment maintenance histories, social media profiles, tourist maps, and many more. If new case studies under this topic are released, they will be added to the listing below.

• Capitalizing Data: Case Studies of Driving Records and Vehicle Insurance Claims | April 2024
• Private Funding of “Free” Data: A Theoretical Framework | April 2024
• Capitalizing Data: Case Studies of Tax Forms and Individual Credit Reports | June 2023

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Disruption is creating opportunities and challenges for global banks. While the risk and regulatory protection agenda remains a major focus, banks must also address financial performance and heightened customer and investor expectations, as they reshape and optimize operational and business models to deliver sustainable returns. Innovation and business-led transformation will be critical for future growth. To remain competitive and relevant, every bank must embrace disruption and strategically build a better ecosystem — not a bigger bank.

Our worldwide team of industry-focused assurance, tax, transaction and consulting professionals integrates sector knowledge and technical experience. We work with clients to navigate digital innovation, new business models and ecosystem partnerships, helping banks become the nimble, responsive organizations that customers demand.

Five priorities for harnessing the power of GenAI in banking</p> "> Five priorities for harnessing the power of GenAI in banking

What to expect from global financial services in 2024 — Americas and EMEIA

In this webcast for Americas and EMEIA audiences, the EY Global Regulatory Network will discuss the direction of travel for regulators across key areas and how to prepare for what's coming.

Our latest thinking on Banking & Capital Markets

Impacts of Central Clearing of US Treasuries and Repo

In this webcast, panelists will discuss key themes and high-level requirements of the US Treasury and repo central clearing rules.

Can core platform modernization position a bank for future success?  

Case study: how one regional bank used core platform modernization to build a strong foundation for future profitability.

The case for a modern transaction banking platform

The evolution of corporate treasury management needs presents an opportunity for corporate banks. Learn from an industry approach.

How to transition from a tactical to strategic adoption of ISO 20022

With ISO 20022 adoption lagging amid competing global deadlines, a successful migration may hinge on changing from a tactical to a strategic mindset.

How Gen Z’s preference for digital is changing the payments landscape

EY survey shows Gen Z embraces simple, seamless payment methods. Learn more.

How can financial institutions modernize their fair-lending practices?

FIs that disregard fair banking are lagging behind FIs that enhance compliance procedures, lending models and data analytics to become more compliant. Read more.

Digital identity opportunities in financial services

Exploring the policy and regulatory trends shaping digital identity and opportunities for financial services companies in a changing payment landscape.

Explore our Banking & Capital Markets case studies

Using AI to augment pricing intelligence for banks

How an AI-powered digital tool, Smart Advisor (SA), helped one bank deliver better client service while maximizing value creation.

How a global FinTech captured growth in the SME segment

A global Fintech captured growth in an opportunistic SME segment with a differentiated, holistic strategy. Learn more in this case study.

Using AI to improve a bank’s agent effectiveness

Leveraging the power of AI and machine learning, one bank mined sales agents’ calls for performance-boosting insights. Learn more in this case study.

After cloud migration, investment bank sees potential for big dividends

A leading investment bank sought to move vital assets to the clouds by building an experienced, cross-functional team. Find out how.

How digital transformation is redesigning trade finance

Banks that adopt an agile, design-based approach to digital transformation can boost the success of their trade finance functions.

How to transform product development to outperform the competition

EY Nexus is a cloud-based platform offering access to the most advanced technologies to launch new products, businesses and services.

How EY can help

Capital Markets Services

Know how our Capital Markets consulting team can help your business grow, manage costs and meet regulatory requirements.

Consumer banking and wealth services

EY consumer banking and wealth technology solutions are designed to drive operational excellence and profitable growth. Learn more.

Corporate, Commercial and SME Banking services

Our Corporate, Commercial and SME (CCSB) Banking services team can help your business navigate through rising market expectation. Learn more.

Cost transformation

EY cost transformation teams help banks to optimize profits and fund transformation. Find out more.

Consumer lending services

Our consumer lending team can help navigate the complexities of unique lending propositions. Find out how.

EY Nexus for Banking

A transformative solution that accelerates innovation, unlocks value in your ecosystem, and powers frictionless business. Learn more.

Finance transformation

We help clients transform finance functions to be a strategic business partner for the business via value creation and controllership activities.

EY Financial Crime solutions

Our skilled teams, operational efficiencies enabled by innovative technology and flexible global delivery service centers can help you manage financial crime risk in a cost-effective, sustainable way.

Financial services risk management

Discover how EY can help the banking & capital markets, insurance, wealth & asset management and private equity sectors tackle the challenges of risk management.

IBOR transition services

EY helps global institutions prepare for the imminent transition away from Interbank Offered Rates (IBORs) to Alternate Reference Rates (ARRs). We also play a leading role in supporting regulators, trade associations and others to increase awareness and education.

Open banking services

Our open banking professionals can help your business maintain a trusted and secure open banking ecosystem while managing its risks. Learn more.

Payment services

Our payments professionals can help your business enhance innovation, drive growth and improve performance. Find out more.

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Discover how EY's Third Party Risk Management team can enable your business to make better decisions about the third parties they choose to work with.

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The Banking & Capital Markets team

Enjoys traveling with family, and coaching his daughters’ basketball and soccer teams. Enjoys running and playing basketball and golf.

Lee Ann Lednik

People-focused leader committed to building trust and transparency amid increasing complexity. Passionate working mom of three. Aspiring photographer. Avid sports fan.

David Kadio-Morokro

Passionate about technology, innovation, and leading EY people to solve clients’ most challenging problems.

Heidi Boyle

Passionate about helping people thrive in the workplace and creating a sense of belonging for all. Writer. Musician. Cooking enthusiast.

Seasoned financial services professional. Resides in Massachusetts with her husband and three children.

Kellen Maia de Sá

Collaborator and problem-solver with the desire to do the right thing. Leads efforts to help financial services clients with the disruption and impact of COVID-19.

Terry Cardew

Builds trust by helping banks solve business issues and stay competitive. Devoted husband. Father of six. Avid skier. NY Giants and Yankees fan. Supporter of The Fresh Air Fund and Lynne’s Kids.

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The following outline provides a high-level overview of the FTC’s proposed final rule :

• Specifically, the final rule provides that it is an unfair method of competition—and therefore a violation of Section 5 of the FTC Act—for employers to enter into noncompetes with workers after the effective date.
• Fewer than 1% of workers are estimated to be senior executives under the final rule.
• Specifically, the final rule defines the term “senior executive” to refer to workers earning more than $151,164 annually who are in a “policy-making position.”
• Reduced health care costs: $74-$194 billion in reduced spending on physician services over the next decade.
• New business formation: 2.7% increase in the rate of new firm formation, resulting in over 8,500 additional new businesses created each year.
• This reflects an estimated increase of about 3,000 to 5,000 new patents in the first year noncompetes are banned, rising to about 30,000-53,000 in the tenth year.
• This represents an estimated increase of 11-19% annually over a ten-year period.
• The average worker’s earnings will rise an estimated extra $524 per year. 

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Single women own more homes than single men in the U.S., but that edge is narrowing

Recent news stories have highlighted the fact that single women in the United States own more homes than single men do . Over the long term, however, that gap is narrowing, according to recently released U.S. Census Bureau data.

Pew Research Center undertook this analysis to better understand the gender gap in homeownership among single Americans and put it in broader historical context.

The counts of single households and single homeowners are based on the Current Population Survey/Housing Vacancy Survey . The Census Bureau publishes these figures in historical table 15a .

The Census Bureau defines a “single household” as one headed by an unmarried person, regardless of who else is living in the household.

The gender pay gap among employed single heads of household is based on an analysis of the 2019 Current Population Survey monthly outgoing rotation group files ( IPUMS ). The calculation is based on the median hourly earnings of part-time and full-time workers. The 88% figure is not for all workers but for workers who are single household heads.

The median income and wealth figures use the 2019 Survey of Consumer Finances (SCF) collected by the Federal Reserve Board. The SCF is a triennial survey and 2019 is the most recent survey year available.

In 2022, single women owned 58% of the nearly 35.2 million homes owned by unmarried Americans , while single men owned 42%.

In 2000, by comparison, single women owned 64% of the almost 25 million homes owned by unmarried Americans. Single men owned 36%.

So what explains the homeownership edge that single women have over single men? And why has the pattern shifted in recent years?

The homeownership edge that single women have held over single men is due more to their numbers than their economic power. This is especially true among older Americans, who are more likely than younger people to own a home. About 70% of single household heads ages 65 and older own their home, compared with 44% of single household heads ages 35 to 44.

Among households headed by an unmarried person age 65 or older, about 6 million more are headed by women than men . Looked at another way, a third of all single women household heads were at least 65 years old in 2022, while only 22% of single men household heads were in that age group. This may be because women in the U.S. tend to live longer than men . (Single Americans in this analysis include those who are widowed, who tend to be in older age groups .)

In most age groups, households headed by single women have lower homeownership rates than those headed by single men – a finding that aligns with economic considerations. Among all employed single household heads, women earned about 88% of what men earned in 2019. The median income of households headed by single women ($49,400) was considerably lower than that of households headed by single men ($61,700). Households headed by single women also have slightly less wealth, or net worth , than those headed by single men.

A basic reason the gender gap in homeownership among single Americans has narrowed in recent years is that single women no longer so heavily outnumber single men among older household heads. Today, women only account for about two-thirds of single household heads ages 65 and older, down from three-quarters in 2000. Again, this may reflect changes in life expectancy; women tend to live longer than men, but the gap has narrowed over time .

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Richard Fry is a senior researcher focusing on economics and education at Pew Research Center

For Women’s History Month, a look at gender gains – and gaps – in the U.S.

Women have gained ground in the nation’s highest-paying occupations, but still lag behind men, how americans see the state of gender and leadership in business, diversity, equity and inclusion in the workplace, in a growing share of u.s. marriages, husbands and wives earn about the same, most popular.

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CERN Accelerating science

CERN's edge AI data analysis techniques used to detect marine plastic pollution

CERN’s expertise in data management is leveraged to combat marine plastic litter through the new EU project, Edge SpAIce

22 April, 2024

By Marzena Lapka

Edge SpAIce, a new EU project involving CERN, EnduroSat and NTU Athens, coordinated by AGENIUM Space.

Earth Observation (EO) and particle physics research have more in common than you might think. In both environments, whether capturing fleeting particle collisions or detecting transient traces of ocean plastics, rapid and accurate data analysis is paramount.

On this Earth Day, as we reflect on our responsibility to reduce plastics for the benefit of our society and all life on our planet, we are excited to present a new EU project, Edge SpAIce . It applies CERN’s cutting-edge AI technology to monitor the Earth’s ecosystems from space in order to detect and track plastic pollution in our oceans.

“In particle physics, the trigger system plays a critical role by swiftly determining which data from the particle detector should be retained, given that only a small fraction of the 40 million collision snapshots taken each second can be recorded. As the data influx at the Large Hadron Collider (LHC) has grown significantly over the years, physicists and computer scientists are continually innovating to upgrade this process - and this is where AI technology comes in,” says Sioni Summers, a CERN physicist working on the CMS experiment at the LHC, who is supervising this work.

Edge SpAIce is a collaborative endeavour involving CERN, EnduroSat (BG) and NTU Athens (GR) and coordinated by AGENIUM Space . Its aim is to develop a specially designed on-board system for satellites that will make it possible to acquire and process high-resolution pictures using a DNN (Deep Neural Network). The system will use the “edge AI” approach, in which data is processed in near real-time directly on the satellite, mirroring the efficient filtering of LHC data in particle detectors at CERN. This means that it is not necessary to transmit all of the captured data back to Earth but only the relevant information - in this case, the presence of marine plastic litter. The system will also be deployed on FPGA hardware developed in Europe, which will improve competitiveness. This could open the door for a whole new market for EO services and applications.

As modern life increasingly relies on technology, the solution that the project offers adeptly addresses the growing demand for data processing and the rapid expansion of EO satellites. By eliminating the need for heavy processing in Earth-based data centres, it not only reduces the carbon footprint but also helps to relieve the burden on these facilities. The innovative approach holds potential for broader applications in domains such as agriculture, urban planning, disaster relief and climate change. Additionally, this technology will provide environmental scientists and policymakers with invaluable data for targeted clean-up operations. It will advance our understanding of plastic pollution patterns, thereby enhancing our capacity to address environmental challenges effectively.

“AGENIUM Space is thrilled to have found synergies with CERN in developing innovative solutions for our planet’s future,” said Dr Andis Dembovskis, a business development executive with AGENIUM Space.

The Edge SpAIce project exemplifies how creative thinking by partners across diverse fields can lead to a collaborative knowledge transfer project that tackles major societal challenges. To discover how other CERN knowledge transfer and innovation projects are making a positive impact on the environment, please visit: https://kt.cern/environment

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