Data analysis is the practice of working with data to glean useful information, which can then be used to make informed decisions.
Sherlock Holmes once said (in a story by Arthur Conan Doyle), “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.”
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 healthcare 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 .
As the data companies have available to them continues to grow in both amount and complexity, so does the need for an effective and efficient process by which to harness the value of that data. The analysis method 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 begin to 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
Data can be used to answer questions and support decisions in several different ways. It can help to group these types of analysis into four categories commonly used in the field. 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, 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” — that 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.
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 .
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.
Data analysis makes use of a range of 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.
Data from Salary.com indicates that the average salary for a data analyst in the United States in 2021 is $77,493 . 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 statistics can help set you up for success.
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).
1. World Economic Forum. "The Future of Jobs Report 2020, https://www.weforum.org/reports/the-future-of-jobs-report-2020." Accessed March 26, 2021.
2. McKinsey & Company. "Five facts: How customer analytics boosts corporate performance, https://www.mckinsey.com/business-functions/marketing-and-sales/our-insights/five-facts-how-customer-analytics-boosts-corporate-performance." Accessed March 26, 2021.
3. Salary.com. "Data Analyst Salary in the United States, https://www.salary.com/research/salary/listing/data-analyst-salary." Accessed March 26, 2021.
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.