Businesses generate more data today than at any point in history, from point-of-sale transactions to website clicks to supply chain sensors. Collecting that data is the easy part. Turning it into decisions that raise revenue, cut costs or serve customers better is the harder step, and demand for that skill set keeps climbing.
Through coursework that combines business strategy with hands-on analytics tools such as SQL, Tableau and RStudio, graduates of Pittsburg State University’s online Master of Business Administration (MBA) in Data Science and Information Systems program build the analytical fluency employers are hiring for.
Graduates develop skills to translate spreadsheets and dashboards into strategy that executives can act on. The program is accredited by the Association to Advance Collegiate Schools of Business (AACSB), a distinction held by fewer than 6% of business schools worldwide.
According to the U.S. Bureau of Labor Statistics (BLS), employment of data scientists is projected to grow 35% from 2025 to 2035, faster than the average across all occupations. Companies in nearly every industry are hiring analytically literate managers to close the gap between what their systems can measure and what their leaders can act on. The following sections walk through how businesses use data analytics across major industries, the tools MBA graduates need to know and how classroom training translates into workplace impact.
How Companies Use Data Analytics to Drive Business Decisions
Data analytics generally falls into three categories: descriptive, predictive and prescriptive analytics. Most business problems draw on some combination of the three.
- Descriptive analytics explains what already happened, using dashboards and reports to summarize sales, web traffic or operating costs.
- Predictive analytics goes further, using statistical models to forecast what is likely to happen next.
- Prescriptive analytics recommends the specific action a decision-maker should take.
Access to data does not guarantee good decisions on its own. Harvard Business Review has cautioned that business leaders often either accept an analysis at face value or dismiss it altogether, and both reactions can lead a company astray if the underlying data was never tested for validity in the first place.
That ability to analyze and test the validity of data is an in-demand skill MBA-trained analysts bring to an organization, since they know how to question a model’s assumptions before it drives budget or staffing decisions. The professional association INFORMS describes analytics as “the application of scientific & mathematical methods to the study & analysis of problems involving complex systems.”
That discipline sits at the center of how modern companies operate. The industries below show what happens when that skill is applied well.
Real-World Data Analytics Applications Across Industries
The industries below rely on data analytics in different ways, but the underlying pattern repeats. Each starts with a business question, applies the right type of analytics to answer it and assigns a team member, often an MBA-trained analyst, to translate the finding into action. The following five examples show how retail, healthcare, finance, marketing and supply chain teams put analytics to work every day.
Retail
Retail companies use predictive analytics to forecast demand, decide what to stock and personalize the shopping experience for individual customers. According to McKinsey & Company, retailers that have refined how they target promotions to specific customer segments, rather than offering the same discount to everyone, have seen sales lifts of 1 to 2% and margin gains of up to 3% from more precise, data-backed targeting. A graduate working in retail analytics might build the customer segmentation model that identifies which shoppers respond to loyalty offers and which are more likely to switch to a competitor.
That same graduate would also monitor how those models perform over time, since customer behavior shifts and a segmentation strategy that worked last year can quietly stop working without anyone noticing. Retail is often the first place students encounter descriptive, predictive and prescriptive analytics working together in a single project, which makes it a common training ground for new analysts.
Healthcare
Hospitals and health systems use data analytics to predict which patients face the highest risk of complications and to allocate scarce resources such as beds, staff and equipment. Deloitte reports that health care executives are prioritizing predictive models that flag high-risk patients early, since catching a problem before a hospital readmission is better for the patient and considerably cheaper for the system. An MBA graduate in a health system’s analytics office might build the resource allocation model that helps administrators decide how many nurses to schedule on a given unit next month.
Descriptive analytics still plays a role here too, since accurate historical reporting on patient volume and staffing is what makes any predictive model trustworthy in the first place. Business-trained analysts in healthcare settings often serve as the bridge between clinical staff and technical data teams, a role that requires fluency in both worlds.
Finance
Banks and financial services firms rely heavily on analytics for risk modeling and fraud detection, two areas where a wrong call is expensive in very different ways. PwC has found that banks are redesigning their risk functions around real-time analytics and scenario modeling so risk teams can catch problems as they emerge rather than after the fact. Fraud detection works in a similar way, using pattern recognition models that flag transactions that deviate from a customer’s normal behavior for manual review before money moves.
A finance-focused MBA graduate might work on either side of this equation, refining the models that catch fraud or building the reporting that helps a risk committee understand its exposure in plain business terms. Both roles require the same underlying skill, reading a statistical output and explaining what it means for the business in front of people who did not build the model themselves.
Marketing
Marketing teams use analytics to understand which campaigns drove a sale and which customers are worth investing in over the long term, a discipline generally known as attribution and segmentation. McKinsey research on personalized marketing found that companies using more granular customer segmentation, instead of treating all shoppers the same, saw measurable gains in both conversion and campaign efficiency. A marketing analyst with MBA training typically owns the segmentation model itself, then works with creative and media teams to make sure the right message reaches the right group.
Getting attribution right also protects the marketing budget, since a campaign that looks successful on the surface can quietly overspend on customers who would have bought anyway. This is one of the clearest examples of data-driven decision making paying for itself, since the savings show up in next quarter’s marketing spend.
Supply Chain
Supply chain teams use data analytics to optimize everything from delivery routes to warehouse layouts, often relying on simulation models that test a change before it happens on the ground. Gartner has identified decision intelligence, which combines decision modeling, AI and analytics to support and improve business choices, as one of the top supply chain technology trends companies are adopting. An MBA graduate in supply chain analytics might use this kind of tool to model how a new distribution center would affect delivery times before the company commits capital to building it.
Because supply chain decisions are so capital-intensive, the ability to test a scenario with data before acting on it is one of the most valuable skills a business analyst can offer. Prescriptive analytics is especially common in this space, since the whole point of a supply chain model is recommending the specific route, order quantity or staffing level a manager should choose.
What MBA Graduates Need to Know About Data Analytics Tools
Knowing which type of analytics fits a business question is only half the job. Graduates also need working fluency in the tools that turn a business question into an analysis, and most employers expect that fluency. Students in Pitt State’s online program gain hands-on experience using the tools that show up across almost every business analytics role.
- SQL, the standard language for pulling and shaping data stored in company databases
- Tableau and Microsoft Power BI, which turn raw data into dashboards executives can read at a glance
- RStudio, used for statistical modeling and more advanced predictive work
- Advanced Excel analytics, still the most common tool for day-to-day business reporting
Gartner has ranked both Power BI and Tableau as leaders in business intelligence software for years running, citing their visualization strength and growing use of built-in AI features. Pitt State’s AACSB-accredited online MBA in Data Science and Information Systems program builds hands-on proficiency with SQL, Tableau and RStudio into required coursework, and graduates build on that foundation with the Power BI and Excel skills most employers expect as well, helping graduates develop comfort moving between a database query and an executive-ready dashboard.
Become an In-Demand Analytics Leader With an Online MBA From Pitt State
Pitt State’s MBA in Data Science and Information Systems is built around applied projects rather than theory alone, so students practice the skills covered above on business problems before they graduate. Coursework moves between statistics, database management and business strategy, which mirrors how analytics works inside a company, where a single project might require pulling data, building a model and then presenting the recommendation to a room full of non-technical executives. Graduates leave the program having already done that work at least once, which shortens the learning curve once they start a new job.
Artificial intelligence is reshaping how the analytics function operates, automating much of the descriptive reporting that used to take an analyst hours to produce and freeing up time for higher-value predictive and prescriptive work. Business leaders who understand both the technology and the underlying business problem are the ones best positioned to guide that shift responsibly, rather than leaving AI adoption entirely to the technology team. That combination of business judgment and data fluency is increasingly what employers ask for by name when they post analytics roles at every level.
Learn more about Pittsburg State University’s online MBA in Data Science and Information Systems program.
About Pittsburg State University’s Online MBA in Data Science and Information Systems Program
Pittsburg State University’s MBA in Data Science and Information Systems program is a concentration built for working professionals who want to pair core business management training with hands-on analytics skills, including SQL, Tableau and RStudio. The 30-credit-hour program is delivered fully online with multiple start dates each year, and no GMAT or GRE is required. Students who want to move quickly can complete the degree in as few as 12 months, taking a shared MBA core plus dedicated data science and information systems coursework that prepares them to apply both business strategy and technical analytics skills in roles across nearly any industry.
Pittsburg State University is a public university in Pittsburg, Kansas, and its Kelce College of Business holds accreditation from AACSB International, a distinction held by a small share of business schools worldwide. Founded in 1903, the university has grown from a small teacher training school into a comprehensive institution serving thousands of students, with a long-standing reputation for practical, career-focused programs in business, education and technology.
