3 Ways Business Analytics Are Changing to Meet New Demands


An illustration shows a woman's head in profile in a thinking posture, with icons representing business analytics and data points superimposed. The sun over a city skyline is in the background.

It seems like only yesterday when a well-crafted dashboard defined the practice of business intelligence. Assembling useful information in formats executives could use to support effective decision-making was the goal. The good news is that the goal of business analytics hasn’t changed, but the goalposts have moved.

As McKinsey notes in The Data-Driven Enterprise of 2025, “rapidly accelerating technology advances, the recognized value of data, and increasing data literacy are changing what it means to be data driven.” The McKinsey post goes on to say that by 2025, “smart workflows and seamless interactions among humans and machines will likely be as standard as the corporate balance sheet, and most employees will use data to optimize nearly every aspect of their work.”

Jobi Abraham, the newest faculty member in Elmhurst University’s Data Science and Analytics program—who brings a depth of experience in the field of health care analytics—recently taught a fascinating course exploring some ways this future might be realized.

Like most things in data science, progress involves both technical advances and organizational adaptation. Let’s explore some specifics.

Business Analytics Must Meet Higher Expectations

Whether you are a consumer searching for the right product to buy, a vendor looking to anticipate your customers’ preferences, or a health care provider seeking ways to optimize the quality of patient care, you want and need data that are relevant, fresh, and accessible in real-time—and in formats you can use.

The emergence of insight engines, AI-based search engines that deploy natural language processing technologies to add context to searches, is one illustration of the technology market’s response to these demands.

As Gartner notes in their 2021 Magic Quadrant for Insight Engines, the core capabilities and characteristics of insight engines are:

  • Ability to Include Key Data Sources
  • Support for Data Enrichment
  • Delivery of Results to Various Touchpoints
  • Evaluation and Tuning of Relevance
  • Security Features
  • Query Input Flexibility

This is no longer a set of capabilities aimed exclusively or even primarily at IT professionals.

More Complexity Requires New Data Architectures

In The Data-Driven Enterprise of 2025, McKinsey predicts that by 2025, “vast networks of connected devices will gather and transmit data and insights, often in real time. How data is generated, processed, analyzed, and visualized for end-users will be dramatically transformed.”

One key to progress toward the transformation of the business analytics landscape is the move to cloud-based architectures and environments. In his class, Jobi led our students on an exploration of ways one major cloud provider, Microsoft Azure, can be used to acquire, store, prepare, and use structured and unstructured health care data to feed a cognitive analytics engine, all within a seamless pipeline. The project resulted in an enhanced understanding of the social determinants of health in the U.S.

While familiar tools such as SQL remain in the mix, simple databases increasingly give way to more robust data warehouses, data lakes, and other data architectures suited for efficiently storing, accessing, processing, and validating large amounts of data of many varieties.

The primary technical challenge in this realm lies with data engineers; however, everyone involved in a modern analytics effort benefits from being aware of how these modern data environments work. Indeed, some of our MBA students taking Jobi’s course as part of their area of specialization especially enjoyed gaining these new perspectives.

Advanced Analytics will Drive Strategy

Chris Mulligan, co-author of Strategy Beyond the Hockey Stick, joined colleagues in sharing insights on the kinds of strategic outcomes advanced business analytics can enable in another McKinsey post, The Strategy-Analytics Revolution. The outcomes the authors cited include:

  • Reducing Bias in Decisions
  • Unearthing New Growth Opportunities
  • Identifying Early-Stage Trends
  • Anticipating Complex Market Dynamics

As the authors note, “Each of these applications can sharpen business leaders’ views of the competitive arena and how they can position themselves to win. But that requires putting advanced analytics front and center in the strategy process.”

As we consistently preach throughout our program’s eight years of operation, the fundamental focus of any data science effort does not change: Create value for your client. The methods for doing so, however, are evolving into a whole new discipline. I hope this is as exciting for you as it is for us!

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About the Author

Jim KulichJim Kulich is a professor in the Department of Computer Science and Information Systems at Elmhurst University. Jim directs Elmhurst’s master’s program in data science and analytics and teaches courses to graduate students who come to the program from a wide range of professional backgrounds.

Posted July 5, 2022

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