AI is creating a strange problem for businesses. It is making it easier than ever to generate data, while making it harder to find professionals who can actually make sense of it.
As AI gets embedded into more products, workflows and decision-making systems, the amount of data businesses generate is only expected to grow. The US Bureau of Labor Statistics expects employment of data scientists to increase by 33.5% between 2024 and 2034, making it the fastest-growing mathematical science occupation and the fourth-fastest-growing occupation overall.
The implication is bigger than the data science job market. As businesses become more data-driven, professionals across functions will increasingly need to understand how to work with data, interpret it and use it to make decisions.
That is where advanced business analytics skills could become a career differentiator.
The World Economic Forum's Future of Jobs Report 2025 puts analytical thinking at the top of the list of core skills employers consider essential. At the same time, AI and big data are among the fastest-growing skills in demand.
There is a reason the two are converging.
AI can generate an answer, identify a pattern or make a prediction. But someone still needs to decide whether the underlying data is reliable, whether the pattern matters to the business and what should happen next.
That requires more than knowing how to use an AI tool.
It requires professionals who can move between data and business decisions.
The New Analytics Skill Stack
Advanced business analytics is therefore becoming less about knowing one particular tool and more about developing a combination of statistical, technical and business skills.
Data visualisation is one part of it. Professionals need to be able to turn large amounts of information into something that decision-makers can actually understand.
Statistical analysis is another. It helps identify relationships, patterns and trends in data rather than relying on intuition alone.
Then there is data mining, which involves finding useful patterns in large and complex datasets. Machine learning takes this further by allowing professionals to build models that can generate predictions and insights.
Optimisation addresses another question: if there are several possible outcomes, what is the best decision the business can make?
These capabilities also map to three increasingly important layers of analytics.
Descriptive analytics explains what happened. Predictive analytics looks at what is likely to happen. Prescriptive analytics helps determine what could be done about it.
The progression is important.
A business does not gain much value from knowing that sales fell last quarter. It gains more value from understanding why they fell, predicting where they are likely to go next and identifying what it can do to change the outcome.
This is also why the skills gap remains a major problem.
The WEF's 2025 report found that 63% of employers consider skill gaps a major barrier to business transformation, while 85% expect to prioritise workforce upskilling.
For professionals, that creates an unusual opportunity. The most valuable people may not necessarily be those who are purely technical or purely business-oriented, but those who can operate at the intersection of both.
Where The University Of Cincinnati Fits In
The University of Cincinnati's online Master of Science in Business Analytics is built around this intersection.
The programme, offered through the Carl H. Lindner College of Business, focuses on descriptive, predictive and prescriptive analytics, with the aim of developing both analytical capabilities and the ability to apply them to business problems.
The curriculum covers areas such as data visualisation, statistical analysis, data mining, machine learning and optimisation, giving professionals a broader analytical toolkit rather than training them around a single technology.
The emphasis is also on applying those skills.
The programme's capstone provides students with an opportunity to work on a real-world problem. Depending on the student's background, that can involve building on a research project, a course project, an internship or work completed in a professional role.
For working professionals, that connection between coursework and an actual business problem can be particularly useful. Instead of analytics remaining something that exists only inside a classroom, it can become part of the work they are already doing.
Designed For Professionals Who Cannot Press Pause
Going back to school while working full-time is a challenge in itself.
The UC programme is 100% online and asynchronous, allowing students to structure their coursework around their existing professional and personal commitments. It is designed for part-time students and can be completed in one to two years.
There is also an online bootcamp before classes begin, designed to help students get comfortable with the technical and nontechnical foundations needed for the programme.
The programme does not require a GMAT and offers courses across spring, summer and fall semesters.
Students can also customise their electives according to where they want their careers to go. Lindner offers stackable graduate certificates in areas including AI, finance and marketing, allowing students to combine analytics with another business specialisation.
That flexibility reflects a broader reality about analytics careers.
Not everyone needs to become a data scientist.
A finance professional who can build predictive models has a different toolkit from one who only reads financial reports. A marketing professional who can mine customer data and optimise campaigns can approach the job differently. A product manager who understands statistical analysis and machine learning can ask different questions of their data.
The advantage is not simply knowing more technology.
It is knowing how to use technology and data to make better decisions.
The University of Cincinnati's online MS in Business Analytics is a STEM programme offered through the AACSB-accredited Lindner College of Business. It was also ranked No. 3 in the US in Fortune's most recent ranking of the best online business analytics master's programmes.
As AI continues to generate more data, businesses will need more than people who can build AI systems.
They will need professionals who can understand what those systems are telling them.
And increasingly, the career advantage may belong to those who can take that information and answer the most important question of all: What should the business do next?
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