What Does a Data Analyst Do?
A degree may open the door to a variety of opportunities and diverse career paths. The degree programs offered at AIU will not necessarily lead to the featured careers. This collection of articles is intended to help inform and guide you through the process of determining which level of degree and types of certifications align with your desired career path.
Data, defined as "factual information (such as measurements or statistics) used as a basis for reasoning, discussion or calculation,"1 take many forms. It can be numeric (e.g., sales figures), textual (e.g., customer address information) or multimedia (e.g., audio or video).
Though an organization may possess different types of data, it may not be very useful to the organization in its raw form. Raw data needs to be organized, processed and interpreted in order to be useful—and this is where data analysts come in.
In this article, we will dive into the details of what data analysts do and the different types of skills that employers typically look for in those who wish to pursue a data analytics career path. But first, let's spend a little bit of time exploring what data analytics is.
What Is Data Analytics?
According to CompTIA, "[d]ata analytics involves using data, techniques and tools that identify patterns and trends, which in turn generate actionable insights that support informed decision-making. The primary objective of data analytics is to address specific questions or challenges that are relevant to an organization to drive better business outcomes."2 In other words, data analytics asks what is the meaning of data. It focuses on examining, analyzing and interpreting data in order to identify trends and insights. These trends and insights may help an organization understand:
- what is currently happening (descriptive data analytics),
- why something happened (diagnostic data analytics),
- what is likely to happen in the future (predictive data analytics) and
- what the organization should do next (prescriptive data analytics).2
Examples of the Four Main Types of Data Analysis
There are four categories or types of data analytics: (1) descriptive, (2) diagnostic, (3) predictive and (4) prescriptive.
- Descriptive analytics describes what happened during a certain timeframe/is happening now. For example, if you are conducting data analysis for a organization that sells a product, descriptive analytics can tell you the number of customers/users you have, year-over-year sales growth or the amount of revenue per customer. Likewise, if you are conducting data analysis for a health care provider, descriptive analytics can give you a snapshot of the number of uninsured patients, how many patients are using preventive health services or the most common illnesses in your patient population.
- Diagnostic analytics describes why the thing you described is happening. In the retail example, if the number of sales dramatically increased during the month of July but the revenue per customer was down in that month, diagnostic analytics might reveal that this was due to your product being temporarily sold at a discount in certain major markets. In the health care example, if both the number of uninsured patients and the number of patients using preventive health services increased in Q3 while the overall percentage of patients utilizing preventive health services declined in the same quarter, a diagnostic analysis might reveal that even though more insured patients are using these services, the gains are offset by the higher number of uninsured patients who are not.
- Predictive analytics describes what might happen next, taking into account what is or has happened (descriptive analytics) and why it is happening (diagnostic analytics). In the retail example, if your product goes on sale every July, and every July for the past several years your overall sales have increased while your revenue per person has decreased, it is logical to predict that the same pattern will repeat itself next year. In the health care example, if the data for the past five years shows that the number of patients presenting at your office with the flu rises in the days following the Thanksgiving holiday, it is logical to assume that this same trend will continue.
- Prescriptive analytics describes what you should do next, taking into account all three of the previous bullets. Is there a way to increase overall sales without experiencing a decrease in revenue every July? Perhaps you could reduce the size of the July discount without negatively impacting the number of customers who purchase your product. Is there a way to decrease the number of patients who show up to the office sick with the flu in December? Perhaps sending patients an SMS reminders to get their flu shot in September could help.
What Does a Data Analyst Actually Do?
The short definition of a data analyst is someone who uses data to report on and find solutions to business problems. More specifically, data analysts identify patterns or trends in the data, generate forecasts and tell persuasive stories through data visualizations (e.g., charts and graphs). They then use all of this information to develop actionable insights that management and other stakeholders can use to make better informed business decisions. Career paths involving data analysis include operations research analysts6, computer and information research scientists7, and management analysts8.
Data Analyst Skills
The data analyst skill set includes a number of specialized technical skills. To help them make sense of massive amounts of data, data analysts must be able to utilize different types of data analysis tools, including:
- statistical software,
- programming languages,
- data visualization tools,
- machine learning libraries,
- spreadsheet software and
- database management systems (DBMS).3
But it's not enough for a data analysts to run software or crunch numbers—they need to be able to communicate their findings to management or other stakeholders. Data visualization tools like charts or graphs can be very useful for data storytelling, but data analysts also need to be able to communicate their findings and recommendations clearly in both written (reports) and verbal (presentation) forms. They also need to have interpersonal skills in order to work on teams and to persuade others to accept their recommendations. Furthermore, data analysts need to be problem-solvers. When presented with a business problem to solve, they need to be able to define it, think critically about it, determine the best process(es) for analyzing it, make sense of the results and prescribe innovative solutions based on those results.
Those who are interested in studying data analytics and working to build relevant skills may find that different institutions may offer different types of data analytics programs. While some students may choose to pursue a bachelor's or master's degree in data analytics, others may instead opt for a degree program in another field that offers the ability to concentrate in data analytics, such as American InterContinental University's Master of Science in Information Technology (MSIT) Degree with a Concentration in Data Analytics.
Data Analyst vs. Data Scientist
While both data analysts and data scientists generally have the same objective—to pull actionable insights from data and use them to inform an organization’s business decisions—and may perform some of the same tasks (e.g., data cleaning, data visualization and developing actionable insights), they are not simply two different terms for one role.4 Data science and data analytics are not the same.
A primary difference between a data analyst and a data scientist are the techniques they use to extract those insights.4 For example, while data analysts use existing systems and processes to extract insights, data scientists build and test new algorithms and analytical models to support machine learning programs, and then they use machine learning to classify data or make predictions based on the data.5
With that said, there are various potential IT career paths that someone who is interested in data analytics might consider, and each comes with its own set of educational and skill requirements.
Differences between Data Analyst and Data Scientist Skill Sets
According to CompTIA, data analyst skills include data mining, data warehousing, data visualization tools and Microsoft Excel, while data scientist skills include statistical analysis, machine learning, model deployment and Hadoop (an open source framework that efficiently stores and process large datasets). Meanwhile, data storytelling, analytical thinking, critical thinking, interpersonal skills and knowledge of programming languages are some skills these occupations share in common.4
Qualifications for Pursuing a Data Analyst Career Path*
According to the Bureau of Labor Statistics (BLS), operations research analysts, a type of data analyst, typically need at least a bachelor's degree to enter the occupation, although a master's degree may be required or preferred. Fields of degree may include operations research, business, mathematics, engineering, or computer and information technology. Important qualities for operations research analysts include many of those already described: analytical skills, communication skills, critical-thinking skills, interpersonal skills, math skills and problem-solving skills.6
Is a Master's Degree in Data Analytics Worth It for You?
Whether pursuing a master's degree in data analytics or a related field is worth it for you is something that only you can decide. There is no one-size-fits-all answer, and you will likely have to do research to make an informed decision.
Demand for data analysts might be one factor worth investigating. According to the BLS, employment of operations research analysts is projected to grow 23 percent from 2023 to 2033, faster than the average for all occupations. This demand is being driven in part by technological advances that have made it faster and easier for organizations to get data. Improvements in analytical software have also helped to make operations research more affordable and widely applicable.6
But demand isn't the only factor to consider or research—your goals, the degree program's cost, whether the degree program is fully online or campus-based and whether you can continue to work while you study are just a few additional issues that you should think about before making any big moves.
AIU's MS in Information Technology—Concentration in Data Analytics
AIU's Master of Science in Information Technology (MSIT) with a concentration in Data Analytics online degree program is designed to help students prepare to pursue IT management positions. The curriculum focuses on the knowledge, practical skills and abilities necessary for becoming an IT manager. Students in this fully online program will study scientific methods for IT architecture design, applied uses of IT, communication techniques, big-picture evaluation techniques and process management. Courses in the Data Analytics concentration may include:
- Advanced Data Analytics
- Ethical, Social and Legal Issues in Analytics
- Executive Strategies and Actionable Analytics
Considering a master's in data analytics? Come explore American InterContinental University's MS in Information Technology—Concentration in Data Analytics degree program or apply today.
* The list of career paths related to this program is based on a subset from the Bureau of Labor Statistics CIP to SOC Crosswalk. Some career paths listed above may require further education or job experience.
1“Data,” Merriam-Webster.com Dictionary, https://www.merriam-webster.com/dictionary/data (last visited Feb. 21, 2025).
2 “What Is Data Analytics: The Ultimate Guide,” CompTIA, https://www.comptia.org/content/guides/what-is-data-analytics (last visited Feb. 21, 2025).
3 “What Tools Do Data Analysts Use?,” International Association of Business Analytics Certification (IABAC) (Feb. 5, 2024), https://iabac.org/blog/what-tools-do-data-analysts-use.
4 “Data Analytics vs. Data Science,” CompTIA, https://www.comptia.org/content/guides/data-analytics-vs-data-science (last visited Feb. 21, 2025).
5 Bureau of Labor Statistics, U.S. Department of Labor, Occupational Outlook Handbook, “Data Scientists,” https://www.bls.gov/ooh/math/data-scientists.htm (last visited Feb. 21, 2025). This data represents national figures and is not based on school-specific information. Conditions in your area may vary.
6 Bureau of Labor Statistics, U.S. Department of Labor, Occupational Outlook Handbook, “Operations Research Analysts,” https://www.bls.gov/ooh/math/operations-research-analysts.htm (last visited Feb. 21, 2025). This data represents national figures and is not based on school-specific information. Conditions in your area may vary.
7 Bureau of Labor Statistics, U.S. Department of Labor, Occupational Outlook Handbook, “Computer and Information Research Scientists,” https://www.bls.gov/ooh/computer-and-information-technology/computer-and-information-research-scientists.htm (last visited Feb. 21, 2025). This data represents national figures and is not based on school-specific information. Conditions in your area may vary.
8 Bureau of Labor Statistics, U.S. Department of Labor, Occupational Outlook Handbook, “Management Analysts,” https://www.bls.gov/ooh/business-and-financial/management-analysts.htm (last visited Feb. 21, 2025). This data represents national figures and is not based on school-specific information. Conditions in your area may vary.
American InterContinental University cannot guarantee employment, salary, or career advancement. Not all programs are available to residents of all states. REQ2106873 2/2025