Data-driven analytics is rapidly becoming a central theme in business conversations. To prepare for these new opportunities, business schools must step up to guide business leaders, new and old, in making the most effective use of these new analytic tools. Two factors underlie this new environment.
First, data is increasing at an explosive rate. Some industry estimates suggest that 90 percent of the current data has been created in the past two years. This explosion spans virtually every area of business.
Second, there has been a corresponding advancement in analytics methods. These new models turn out to be much better at extracting information from data–especially big data–than conventional statistical methods. Similar advancements have occurred in the areas of data visualization and data storage.
YOU CAN MANAGE WHAT YOU CAN MEASURE
A recent analytics inflection point was the introduction of ChatGPT from Open AI. While ChatGPT still makes a surprising number of factual mistakes, it has nevertheless established the value of creating useful text and freeing human bandwidth for more valuable applications. Within the first week of its release, more than 1 million users were exploring ChatGPT’s capabilities and many were thinking about AI-based business models. Buzzfeed (a media company) saw its market valuation roughly double after it announced a partnership with Open AI.
An old adage is that you can’t manage what you can’t measure … but you can manage what you can measure. Data analytics offers managers the ability to quickly analyze this new data and to uncover patterns and trends that were previously undetected. The ground rules for starting and managing a business are changing.
Business schools must adapt quickly or risk not preparing students for the world of the future. The need for organizations to make decisions and manage operations is not going away. However, what is needed in this new world is a framework for making data-based decisions that efficiently utilize the new information and the computational advances.
This framework will have three key components: data acquisition, data interpretation, and data-based actions.
Acquisition: Good decisions will require extensive data collection efforts. If the world is changing fast, recent data will be more informative. Managers must use their knowledge of industry structure and developments to determine what type of data should be collected. Data that replicates existing data is generally less valuable, while new data that captures inflection points is highly valuable.
Interpretation: Managers need a good understanding of their data and a reasonable understanding of analytic models. They don’t have to be data scientists, but they need to be able to interpret and explain the results of the models. They need to provide visual explanations and describe the implications for their business strategy.
Actions: Models can make predictions, but managers need to make decisions. Managers will use analytic models that take the predictions as input and generate recommendations as output. Managers will need to be able to interpret, explain, and monitor the decisions that result from these models.
IN THE B-SCHOOL CURRICULUM
How could this acquisition/interpretation/action process be incorporated into a business school curriculum? Here are four suggestions.
First, have the functional areas, such as accounting and marketing, use case studies that highlight specific analytical methods. The objective would be to understand the value of the analytics for making business decisions. Second, have the instructors discuss areas that are ripe for AI disintermediation. AI creates processes that are cheaper/faster/better and thereby remove traditional intermediaries. These discussions would get students thinking about AI business models—where can AI be applied and how to integrate AI into new and existing business models. Third, offer courses that enable students to be intelligent users of analytical models. This may require a partnership with computer science faculty. Finally, create an environment where faculty can easily develop their analytics knowledge. This would include standardizing on AI/ML platforms and providing analytical instructional assistance to faculty.
In summary, the world is becoming analytics-intensive and business schools need to quickly adapt and update their curricula. Schools that move slowly will produce graduates with reduced professional opportunities.
Robert Neal is a professor of finance in the Kelley School of Business. David Clement is an AI developer based in Vancouver.
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