Every time a streaming service recommends a show, a retailer adjusts its prices or a hospital improves patient care outcomes, a data analyst likely had a hand in it. These professionals sit at the center of data-driven decision-making, translating raw numbers into insights that guide strategy.
If you are considering a career in data, understanding what a data analyst does is the right place to start. This guide breaks down the role, the core skills employers expect and what you can realistically earn as you build experience, the same foundation covered in Southern Illinois University Edwardsville’s online Bachelor of Science in Business Administration (BSBA) with a Data Analytics specialization.
What Does a Data Analyst Do?
A data analyst collects, organizes and examines data to identify trends, answer business questions and support decision-making. They work with structured data sets, such as sales records, website metrics, survey results and financial reports, as well as unstructured data like images, videos, and social media content. After analysis, they present findings in charts, dashboards and written summaries that non-technical stakeholders can act on.
The analyst’s role is descriptive and diagnostic: what happened, why it happened and what it might mean for future strategy. Analysts spend most of their time moving between these two questions, using data to explain outcomes rather than simply reporting them.
Key Responsibilities of a Data Analyst
While specific duties vary by employer and industry, most data analyst roles include these core responsibilities. The list below covers the tasks that make up a typical data analyst’s day-to-day work.
- Data collection and extraction: Analysts pull data from internal databases, third-party systems or web analytics platforms using tools like SQL. They identify the data needed to answer a specific business question and make sure it is complete and accessible.
- Data cleaning and preparation: Raw data is rarely ready to analyze. Analysts use industry-leading tools and programming to check for errors, fill in missing values, remove duplicates and standardize formats before beginning their analysis.
- Statistical analysis: Analysts apply statistical methods, such as averages, trend lines, correlations and regression analysis, to find patterns in data. They test whether a pattern is meaningful or might be a product of random variation.
- Data visualization: Analysts build charts, graphs and interactive dashboards to provide deeper insights into data relationships and that make findings clear to managers, executives and cross-functional teams. Tools like Tableau and Power BI are standard for this work.
- Reporting and presentation: Data analysts produce regular reports and present findings in meetings. Strong communication is essential; the ability to explain a complex data set in plain terms separates good analysts from great ones.
- Collaboration with stakeholders: Analysts work closely with marketing, finance, operations and product teams to understand what questions need answering and what decisions the analysis will support.
Data Analyst Skills
Employers look for a mix of technical and interpersonal skills in data analyst candidates. Many of these competencies, including SQL, Python and data visualization, are covered directly in SIUE’s online BSBA with a Data Analytics specialization.
Technical Skills
SQL is the foundation. Analysts use it to query relational databases, filter records, join tables and aggregate data at scale. Most entry-level job descriptions list SQL as a required skill.
Python or R enables analysts to work with larger data sets, automate repetitive tasks and run statistical models beyond what spreadsheets can handle. Python is more widely used in industry; R remains common in research-heavy environments.
Excel remains a daily tool at most companies. Pivot tables, VLOOKUP and conditional formatting are standard analyst competencies.
Tableau and Power BI are the dominant data visualization platforms. Analysts use them to build dashboards that update automatically as new data comes in.
Database management, meaning understanding how data is stored, queried and structured, gives analysts context for where the data they work with comes from and how reliable it is. This background helps analysts catch inconsistencies before they affect a final report.
Soft Skills
- Communication: Analysts translate technical findings for non-technical audiences. Written reports, verbal presentations and data storytelling are all part of the job.
- Critical thinking: Good analysts question their data. They look for confounding variables, challenge assumptions and flag when findings seem inconsistent with other information.
- Attention to detail: A small error in a data cleaning step can skew every analysis downstream. Precision matters throughout the entire workflow.
- Curiosity: The best analysts ask follow-up questions. Why did that metric change? What would happen if we tested a different approach? Intellectual curiosity drives better insights.
Beyond the technical and soft skills above, data analysts also rely on a specific set of tools to get the job done. Familiarity with the following platforms is often what separates a competitive candidate from one who still needs on-the-job training.
Tools and Technologies Data Analysts Use
Beyond SQL, Python, Excel and Tableau/Power BI, a few additional platforms round out the toolkit:
- Google Analytics for web traffic and user behavior data
- Jupyter Notebooks for documented, shareable analysis workflows
According to O*NET (the Occupational Information Network)’s occupational profile for data scientists, a closely related analytics role, core requirements include business intelligence and data visualization software, database tools and statistical software, the same categories of skills developed through degree programs in applied data analytics and business analytics. That range of tools is part of why the role translates so well across industries.
Industries Where Data Analysts Work
While the core toolkit stays largely the same, the day-to-day focus of the role shifts depending on the industry. Here’s a look at how data analysts contribute across some of the sectors that rely on them most:
- Finance and banking: Analysts track market trends, assess credit risk, detect fraud and evaluate investment performance.
- Healthcare: Analysts examine patient outcomes, clinical trial data and operational metrics to improve care quality and reduce costs. Healthcare is one of the industries driving national demand for data-related roles.
- Marketing and e-commerce: Analysts measure campaign ROI, study customer behavior and optimize digital marketing channels.
- Manufacturing and logistics: Supply chain analysts monitor inventory, shipping performance and production efficiency to identify cost-saving opportunities.
- Government and nonprofits: Public sector analysts use data to evaluate program effectiveness, allocate budgets and track demographic trends.
The World Economic Forum’s Future of Jobs Report 2025 identifies big data specialists as one of the fastest-growing job categories worldwide and ranks AI and big data skills as the single fastest-growing skill area employers plan to prioritize through 2030. With demand for these skills rising across so many industries, it’s worth looking at what that growth actually translates to in pay.
Data Analyst Salary
Compensation for data analysts varies by role, experience and industry. The U.S. Bureau of Labor Statistics tracks pay for data scientists, the closest official occupational category, and reports a median annual wage of $112,590 in May 2024, with the lowest-paid 10% earning less than $63,650 and the highest-paid 10% earning more than $194,410. Employment in this field is projected to grow 34% from 2024 to 2034, making it one of the fastest-growing occupational groups.
According to PayScale, data analysts with less than one year of experience average $63,574 per year, rising to about $69,589 for those with one to four years of experience, with base salaries typically ranging from $51,000 to $95,000 depending on location, industry and skill set. Senior data analysts average $94,958 per year, with base pay typically between $72,000 and $125,000. High-paying industries for data-related roles include computer systems design, management and scientific consulting, and scientific research and development, each reporting median wages above $110,000 for data scientists in 2024, according to the same BLS data cited above.
How to Become a Data Analyst
Most data analyst positions require a bachelor’s degree. Relevant fields include data analytics, business analytics, statistics, computer science or information systems. Some analysts enter the field through mathematics or economics degrees with substantial coursework in statistics and programming.
Association to Advance Collegiate Schools of Business (AACSB) accreditation, held by only about 6% of business schools worldwide, signals that a program has met rigorous, peer-reviewed standards in business education. AACSB-accredited programs typically integrate data analytics into business and management curricula, preparing graduates for roles across functions, not just in dedicated analytics departments.
SIUE’s online BSBA with a Data Analytics specialization combines business fundamentals with technical training in data tools and analytical methods. The program is designed for working adults who want to launch or advance an analytics career without pausing their professional life.
Building Your Path Into Data Analytics
Data analysis has become one of the most versatile and in-demand skill sets in business today, spanning nearly every industry. Whether you are drawn to the technical side of the work, such as SQL and Python, or the communication side, such as translating findings for decision-makers, the role offers multiple paths for growth.
A bachelor’s degree with a data analytics focus gives you the technical foundation and business context employers look for in this field, along with a credential that signals you can do the work from day one. SIUE’s program builds that foundation through the same mix of business fundamentals and hands-on training in SQL, Python and data visualization covered throughout this guide, letting you develop those skills without stepping away from your career.
Explore SIUE’s online BSBA with a Data Analytics specialization to see how the program can prepare you for this career.
Frequently Asked Questions
Here are answers to a few common questions about the data analyst role. These are the questions people considering this career path ask most often.
What is the difference between a data analyst and a data scientist?
Data analysts typically focus on interpreting existing data to answer specific business questions, while data scientists build predictive models and apply more advanced statistical and machine learning techniques. The two roles overlap significantly, and many data analysts move into data science positions as they gain more technical experience.
What skills do I need to become a data analyst?
Most employers look for proficiency in SQL and Excel at minimum, with Python, Tableau or Power BI increasingly expected for full-time roles. Strong communication skills matter just as much as technical ability, since analysts regularly present findings to non-technical audiences.
Is data analytics a good career path?
Data analytics offers strong job growth, competitive pay and opportunities across nearly every industry. The field also provides a natural path toward more senior roles in data science, analytics management or business intelligence.
About Southern Illinois University Edwardsville
Southern Illinois University Edwardsville (SIUE) is a public university located in the Metro-East region near St. Louis. Founded through a community-driven campaign in the 1950s, SIUE opened its Edwardsville campus in 1965 and today offers undergraduate and graduate programs across the arts and sciences, business, education, engineering and nursing.
SIUE’s School of Business holds accreditation from AACSB International and is one of only 2% of business schools worldwide to hold dual accreditation in both business and accounting, a distinction it has maintained since 1975. The university extends that same academic standard to working professionals through flexible, career-focused online programs.
For students interested in data analytics, SIUE combines a business-focused curriculum with hands-on technical training in tools like SQL, Python and data visualization software, giving graduates both the quantitative skills and the business context employers expect. Its online format allows working adults to build these credentials without stepping away from their careers.
