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.


A final caveat is that there is the phenomenon of DRIP—Data Rich, Information Poor. This means that an organization may have plenty of data, but producing valuable information from that data is often where organizations lack the expertise. A variation of DRIP may be “Data Rich, Insight Poor.” Using intuition and insights is also a key part of being successful in the field of analytics, and business in general. Developing interesting patterns from large datasets can be valuable, but insights put in proper context must also be applied in order to maximize the value of the analytics results obtained. Certainly, we are advocates of data-based decision-making, but intuition-based decision-making (the “gut feel”) should also be applied to make sense of the results.


Business Analytics and Decision-Making

The hope is that business analytics informs decision-making. There have been various studies in the past that seem to validate this statement. In August 2011, a Bloomberg Businessweek Research Services study sponsored by SAS showed that most firms say business analytics boosts the decision-making process.

Business analytics have been effective in decision-making for three of four enterprises. According to the survey results, companies that gain the most value from business analytics typically have a top-down embrace of analytics by senior leaders, put the right analytic talent in place, improve data management and governance, deploy the right analytic tools, and operationalize the results.1

In Neal Leavitt’s article, “Bringing Big Analytics to the Masses,” in the January 2013 issue of IEEE Computer, he mentions that a 2012 study by IBM and The Economist found that firms that apply analytics outperform their peers that don’t. Tom Davenport’s work on analytics shows that business analytics make for smarter decisions.2 In addition, a sample of 310 companies from different industries from the United States, Europe, Canada, Brazil, and China showed that a statistically significant relationship exists between analytical capabilities and performance.3

To gain further insight in this area, Liebowitz conducted a convenience sample survey of senior managers in 78 organizations in January 2013 to better understand the possible linkage of business analytics to decision-making. About 47% of the organizations surveyed had more than 1,000 employees. The main conclusions are:

• Key Performance Indicators (KPIs) are the analytics used most. (42.3%) for informing decision-making.

Managers typically receive analytics on a monthly basis (36.4%).

A quarterly basis (30.8%) is the frequency that analytics are often 
applied to help in decision-making.

80.5% said that analytics that are received usually provide more 
insights than one’s gut feeling.

A combination of risks are measured by analytics, including 
Information Technology risks, Marketing/Sales risks, and others.

Analytics have affected one’s decision-making on key issues 3–5 
times a year (33.3%).

About 51% said they do not have a dedicated Analytics unit.

Statistical skills (47.3%) and communications skills (41.9%) are often 
lacking in today’s analysts.


The Years Ahead

On attending the National Institute of Standards and Technology (NIST) Conference on Big Data, Cloud Computing, and Analytics in January 2013, it is evident that Big Data and cloud computing are some of the driving factors propelling the need for advanced analytics. For example, in the US Department of Veteran Affairs (VA), their Blue Button system,4 which allows veterans to download their healthcare data from the VA’s electronic health records, could easily expand to thousands of terabytes as genetic data is included. Census, Amazon, Google, Merck, NIST, NASA, NIH/NCI (National Institutes of Health/National Cancer Institute), and many other organizations are deluged with data and are trying to make sense of what is there.

Having analytical and technical skills combined with business and communications skills will be a vital necessity for business analytics to have the impact that it should.

Analytics can play a role in helping to provide useful information from the 3V’s of Big Data5 (volume, velocity, and variety). However, the real challenge is how to extract “big knowledge” from this “Big Data.” Certainly, various intelligent systems and knowledge discovery techniques can be applied to massage the data to uncover possible hidden relationships and patterns that could benefit the organization. However, the integration and synthesis of the Big Data are also a challenge. Developing a Big Data framework and taxonomy for the given organization are important elements in order for analytics to be properly applied to give value to the organization. And here again, to produce “knowledge,” insights and intuition may play a role.

According to the McKinsey Global Institute report on Big Data5 some of the key areas for future work include (1) policies related to privacy, security, intellectual property, and even liability will need to be addressed in a Big Data world; (2) organizations need not only to put the right talent and technology in place but also structure workflows and incentives to optimize the use of Big Data; and (3) access to data is critical—companies will increasingly need to integrate information from multiple data sources, often from third parties, and the incentives have to be in place to enable this. Darrell West’s September 2012 Brookings report, “Big Data for Education: Data Mining, Data Analytics, and Web Dashboard” emphasizes a key point as related to education:6

Schools must understand the value of a data-driven approach to education—having performance systems will contribute to informed decision-making.

One final thought to consider, and mention again, is that tomorrow’s analysts must be able to converse in the “business language” that resonates with their management and senior executives. Having analytical and technical skills combined with business and communications skills will be a vital necessity for business analytics to have the impact that it should.

This article is an excerpt from Business Analytics: An Introduction edited by Jay Liebowitz, ISBN 9781466596092. © 2014 Taylor & Francis Group. Reprinted with permission.

About the Author

Jay Liebowitz, D.Sc. is the Orkand Endowed Chair in Management and Technology in the Graduate School at the University of Maryland University College (UMUC). He previously served as a professor in the Carey Business School at Johns Hopkins University. He was ranked one of the top 10 knowledge management researchers/practitioners out of 11,000 worldwide, and was ranked #2 in KM Strategy worldwide according to the January 2010 Journal of Knowledge Management.





3.Peter Trkman et al, “The Impact of Business Analytics on Supply Chain Performance,” Decision Support Systems Journal, Elsevier, 2010.



6. papers/2012/09/04-


The views expressed in this article are those of the authors and do not necessarily reflect the views or policies of The World Financial Review.