Business Analytics and Decision-Making: The Years Ahead

By Jay Liebowitz

It is readily apparent that business analytics is an emerging and fast-growing field. Universities and colleges are developing programs in this area, and companies are developing relationships with universities (such as IBM and Ohio State University forming the IBM Client Centre for Advanced Analytics as part of the College of Business at Ohio State) in order to meet the future needs for analytics talent. Combined with the onslaught of “Big Data,” the field of analytics is a promising area for most sectors, such as healthcare, finance, emergency management, marketing, cybersecurity, and others.

Certainly, we can educate individuals for a “data scientist” or “business analyst” role, but there are several caveats that we need to be aware of. First, the sheer number of people needed to fill this vacuum is quite a demanding load and challenge. If the McKinsey report is accurate in predicting the need for up to 200,000 new analysts and re-trained managers in the United States alone, we have to think of new ways to provide this supply of talent, as universities and colleges can’t do it alone. Companies, professional societies, and foundations may need to be proactive in offering training and education courses in these areas to refine the talent of those in related fields. This is certainly being done, as evidenced by the KDNuggets Website (www. Perhaps MOOCs (Massive Open Online Courses) in the analytics area may be a remedy for this challenge. Other creative ways may be to look at the STEM (Science, Technology, Engineering, and Math) areas in high schools and introduce analytics during secondary education, so high school students will be more aware of the field for possible majoring or minoring in college.

A second caveat is that analytics is a number-intensive field that brings in applied mathematics, statistics, machine learning, and other computer-intensive techniques. However, a good analyst needs to have a set of other skills in order to portray and communicate the underlying meaning of the analytics results to managers and senior executives. Couching the terms in a way that the business leader can understand is ultimately an important part of being successful in analytics. If the meaning behind the analytics results can’t be conveyed in a comprehensible way, then sub-optimization will occur. Certainly, the use of performance and executive dashboards for visualizing the KPIs (Key Performance Indicators) of an organization will help in this regard, but the data or business analyst must be well versed in the context of the organization for management to best value the results.

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