SPOT LIGHT INSIGHT
Release the Data Analyst! Kurt Pflughoeft, Ph.D. Sentry Endowed Chair of Computational Analytics; Director of the Center for Data Analytics
Data analytics is all the buzz at many corporations subsuming areas such as business analytics and business intelligence. The goals of data analytics are broad, ranging from knowledge discovery to automated decision-making. The impact of this field has led to many benefits such as increased cross- and up-selling as well as identifying new business opportunities. Now, if for some reason, you are a bit skeptical about this field, you may ask, “Isn’t data analytics just a rebranding of past quantitative approaches like statistics, operations research or computer science?” The answer is: “Not quite.” Although these fields provide many contributions to data analytics, there have been great advances with regards to algorithms, software and hardware that have led to synergies which are leveraged by data analysts. Data analysts have a “can-do” attitude to arrive at solutions even if an unconventional approach may be needed. In fact, a few short years ago, the American Statistical Association, was worried that applied statistics was being eclipsed by fields like data analytics. The association made the following recommendations to its members: 1. Gain a deep understanding of the product/service that you are supporting. 2. Be more focused on predictions rather than merely inferring relationships. 3. Be willing to tackle large and messy problems. 4. Create more partnerships between the statistics and data analytic communities. These recommendations were patterned upon the successes of data analytics. For example, large and messy problems may be characterized by unsatisfied assumptions or by big, dirty and unstructured data. Many statisticians had not wanted to deal with such issues.
Likewise, managers don’t want to hear about excuses why a problem is too difficult to address; they want insights to guide their decision making. Solutions need not be optimal but “good-enough” solutions can be quite helpful. While working at a market research (MR) firm, I experienced some of the upheavals caused by data analytics. Most of our newer competitors were no longer other MR firms but rather data analytics firms. This observation was also evident from the confusion surrounding the revered Honomichl list of MR firms. Is a data analytics firm that tackles many MR problems an MR firm? Probably. An early example of the use of data analytics for an unconventional solution was the determination of audience share for TV programs. MR firms used many different techniques ranging from TV diaries to monitoring devices. The “audience share world” was reset when a professor simply analyzed tweets to come up with the same results. Tweets are now a standard way to determine, in part, audience share. A more recent example is the prediction of customer attrition by MR firms that measure satisfaction. Their method to determine potential defections relied on “Hot Alerts.” A “Hot Alert” identifies customers who may have given the client a low score on a key question. Although “Hot Alerts” have some benefits, they may be too late for intervention and they only address surveyed customers. Data analysts brought much more horsepower to identify potential defectors early on. For example, a TV broadcast company issued a request for proposal (RFP) to help with this task. The data analysts requested many more variables including call center contacts/notes, web site navigation, services bundles, customer viewing habits, customer age and location to name a few.
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Center for Business and Economic Insight
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