What Is Data Analysis? (With Examples)
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Data analysis is the practice of working with data to glean useful information, which can then be used to make informed decisions.
"It is a capital mistake to theorize before one has data. Insensibly one begins to twist facts to suit theories, instead of theories to suit facts," Sherlock Holme's proclaims in Sir Arthur Conan Doyle's A Scandal in Bohemia.
This idea lies at the root of data analysis. When we can extract meaning from data, it empowers us to make better decisions. And we’re living in a time when we have more data than ever at our fingertips.
Companies are wisening up to the benefits of leveraging data. Data analysis can help a bank to personalize customer interactions, a health care system to predict future health needs, or an entertainment company to create the next big streaming hit.
The World Economic Forum Future of Jobs Report 2020 listed data analysts and scientists as the top emerging job, followed immediately by AI and machine learning specialists, and big data specialists .In this article, you'll learn more about the data analysis process, different types of data analysis, and recommended courses to help you get started in this exciting field.
Read more: How to Become a Data Analyst (with or Without a Degree)
Data analysis process
As the data available to companies continues to grow both in amount and complexity, so too does the need for an effective and efficient process by which to harness the value of that data. The data analysis process typically moves through several iterative phases. Let’s take a closer look at each.
Identify the business question you’d like to answer. What problem is the company trying to solve? What do you need to measure, and how will you measure it?
Collect the raw data sets you’ll need to help you answer the identified question. Data collection might come from internal sources, like a company’s client relationship management (CRM) software, or from secondary sources, like government records or social media application programming interfaces (APIs).
Clean the data to prepare it for analysis. This often involves purging duplicate and anomalous data, reconciling inconsistencies, standardizing data structure and format, and dealing with white spaces and other syntax errors.
Analyze the data. By manipulating the data using various data analysis techniques and tools, you can begin to find trends, correlations, outliers, and variations that tell a story. During this stage, you might use data mining to discover patterns within databases or data visualization software to help transform data into an easy-to-understand graphical format.
Interpret the results of your analysis to see how well the data answered your original question. What recommendations can you make based on the data? What are the limitations to your conclusions?
Watch this video to hear what data analysis how Kevin, Director of Data Analytics at Google, defines data analysis.
Learn more: What Does a Data Analyst Do? A Career Guide
Types of data analysis (with examples)
Data can be used to answer questions and support decisions in many different ways. To identify the best way to analyze your date, it can help to familiarize yourself with the four types of data analysis commonly used in the field.
In this section, we’ll take a look at each of these data analysis methods, along with an example of how each might be applied in the real world.
Descriptive analysis tells us what happened. This type of analysis helps describe or summarize quantitative data by presenting statistics. For example, descriptive statistical analysis could show the distribution of sales across a group of employees and the average sales figure per employee.
Descriptive analysis answers the question, “what happened?”
If the descriptive analysis determines the “what,” diagnostic analysis determines the “why.” Let’s say a descriptive analysis shows an unusual influx of patients in a hospital. Drilling into the data further might reveal that many of these patients shared symptoms of a particular virus. This diagnostic analysis can help you determine that an infectious agent—the “why”—led to the influx of patients.
Diagnostic analysis answers the question, “why did it happen?”
So far, we’ve looked at types of analysis that examine and draw conclusions about the past. Predictive analytics uses data to form projections about the future. Using predictive analysis, you might notice that a given product has had its best sales during the months of September and October each year, leading you to predict a similar high point during the upcoming year.
Predictive analysis answers the question, “what might happen in the future?”
Prescriptive analysis takes all the insights gathered from the first three types of analysis and uses them to form recommendations for how a company should act. Using our previous example, this type of analysis might suggest a market plan to build on the success of the high sales months and harness new growth opportunities in the slower months.
Prescriptive analysis answers the question, “what should we do about it?”
This last type is where the concept of data-driven decision-making comes into play.
Read more: Advanced Analytics: Definition, Benefits, and Use Cases
What is data-driven decision-making (DDDM)?
Data-driven decision-making, sometimes abbreviated to DDDM), can be defined as the process of making strategic business decisions based on facts, data, and metrics instead of intuition, emotion, or observation.
This might sound obvious, but in practice, not all organizations are as data-driven as they could be. According to global management consulting firm McKinsey Global Institute, data-driven companies are better at acquiring new customers, maintaining customer loyalty, and achieving above-average profitability .
Get started with Coursera
If you’re interested in a career in the high-growth field of data analytics, you can begin building job-ready skills with the Google Data Analytics Professional Certificate. Prepare yourself for an entry-level job as you learn from Google employees — no experience or degree required. Once you finish, you can apply directly with more than 130 US employers (including Google) and advance to courses such as Google's Advanced Data Analytics Professional Certificate to continue deepening your skill set.
Frequently asked questions (FAQ)
Just about any business or organization can use data analytics to help inform their decisions and boost their performance. Some of the most successful companies across a range of industries — from Amazon and Netflix to Starbucks and General Electric — integrate data into their business plans to improve their overall business performance.
Data analysis makes use of a range of analysis tools and technologies. Some of the top skills for data analysts include SQL, data visualization, statistical programming languages (like R and Python), machine learning, and spreadsheets.
Read: 7 In-Demand Data Analyst Skills to Get Hired in 2022
Data from Glassdoor indicates that the average salary for a data analyst in the United States is $70,166 as of May 2023 . How much you make will depend on factors like your qualifications, experience, and location.
Data analytics tends to be less math-intensive than data science. While you probably won’t need to master any advanced mathematics, a foundation in basic math and statistical analysis can help set you up for success.
Learn more: Data Analyst vs. Data Scientist: What’s the Difference?
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This content has been made available for informational purposes only. Learners are advised to conduct additional research to ensure that courses and other credentials pursued meet their personal, professional, and financial goals.
A simple example of data analysis can be seen whenever we make a decision in our daily lives by evaluating what has happened in the past or what will happen if we make that decision. Basically, this is the process of analyzing the past or future and making a decision based on that analysis.What is a simple example of data analysis? ›
A simple example of data analysis can be seen whenever we make a decision in our daily lives by evaluating what has happened in the past or what will happen if we make that decision. Basically, this is the process of analyzing the past or future and making a decision based on that analysis.What is good example of data analysis? ›
Qualitative data analysis example: A fitness studio owner sends out an open-ended survey asking customers what types of exercises they enjoy the most. The owner then performs qualitative content analysis to identify the most frequently suggested exercises and incorporates these into future workout classes.What are the 3 types of data analysis? ›
There are three types of analytics that businesses use to drive their decision making; descriptive analytics, which tell us what has already happened; predictive analytics, which show us what could happen, and finally, prescriptive analytics, which inform us what should happen in the future.What are the 4 types of data analysis? ›
- Predictive data analytics. Predictive analytics may be the most commonly used category of data analytics. ...
- Prescriptive data analytics. ...
- Diagnostic data analytics. ...
- Descriptive data analytics.
➤ Data analytics enables organizations to uncover patterns and extract valuable insights from raw data. It helps companies understand their customers better, produce relevant content, strategize ad campaigns, develop meaningful products, and ultimately boost business performance.What is data analysis real life example? ›
Several top logistic companies like DHL and FedEx are using data analysis to examine collected data and improve their overall efficiency. Using data analytics applications, the companies were able to find the best shipping routes, delivery time, as well as the most cost-efficient transport means.What is the most basic data analysis? ›
It is at the foundation of all data insight. It is the simplest and most common use of data in business today. Descriptive analysis answers the “what happened” by summarizing past data, usually in the form of dashboards.
Microsoft Excel is the most common tool used for manipulating spreadsheets and building analyses. With decades of development behind it, Excel can support almost any standard analytics workflow and is extendable through its native programming language, Visual Basic.What are the 5 steps of data analysis? ›
It's a five-step framework to analyze data. The five steps are: 1) Identify business questions, 2) Collect and store data, 3) Clean and prepare data, 4) Analyze data, and 5) Visualize and communicate data.
Simply select a cell in a data range > select the Analyze Data button on the Home tab. Analyze Data in Excel will analyze your data, and return interesting visuals about it in a task pane.What are the three C's of data analysis? ›
We've divided them into three related categories: completeness, correctness, and clarity.What is data analysis skills? ›
Probability and statistics are important data analyst skills. This knowledge will guide your analysis and exploration and help you decipher the data. Additionally, understanding statistics will also help you ensure your analysis is valid, and it will help you avoid common fallacies and logical errors.How to do data analysis? ›
- Identify the data you want to analyze.
- Collect the data.
- Clean the data in preparation for analysis.
- Analyze the data.
- Interpret the results of the analysis.
Simply put, data analyst roles and responsibilities involve collecting and analyzing data to identify patterns, trends, insights, and solutions. This requires applying knowledge gained from fields such as math, statistics, economics, and computer science.What are the 6 phases of data analysis? ›
According to Google, there are six data analysis phases or steps: ask, prepare, process, analyze, share, and act.What are 3 data examples? ›
Data can come in the form of text, observations, figures, images, numbers, graphs, or symbols. For example, data might include individual prices, weights, addresses, ages, names, temperatures, dates, or distances. Data is a raw form of knowledge and, on its own, doesn't carry any significance or purpose.What is the easiest data analysis method? ›
Content analysis is possibly the most common and straightforward QDA method. At the simplest level, content analysis is used to evaluate patterns within a piece of content (for example, words, phrases or images) or across multiple pieces of content or sources of communication.What is data analysis in one word? ›
Data analytics (DA) is the process of examining data sets to find trends and draw conclusions about the information they contain.