A short while ago I wrote a post suggesting that human analysts would not be disappearing anytime soon. As important as hiring good analytical people, however, is taking advantage of all of the current possibilities for improving their productivity in analytical work. Machine learning tools and platforms are the most promising approach to creating analytical models at the pace required by big data.
Each year, IIA gathers its faculty and leadership to look at the evidence and personal experience and provide a perspective on what the future holds in the world of analytics. Last year, we predicted that data visualization tools would become more prevalent, suggested that personalized customer analytics would transcend product driven analytics, and posed that the lines between data scientists and other analytics professionals would blur.
Many companies are attracted to small “centers of excellence” (CoEs) that put a small number of people in a central coordination role, but leave the great majority of quants to fend for themselves in highly decentralized environments. This is appealing if you want to apply a gloss of coordination to a largely uncoordinated activity, but I don’t think it suggests a strong commitment to a well-organized analytical capability.
What do big companies do with Big Data? Jill Dyché from SAS Institute Inc. and I have just finished a study of over 20 companies on this topic (you can download the full report here). Much of what has been said about Big Data until now has come from online firms like Google Inc., eBay Inc., LinkedIn Inc., and Facebook Inc., and startups in data-intensive industries. These companies were built around Big Data from the beginning. No integration with existing architectures or processes was necessary. Big Data could stand alone, Big Data analytics could be the only focus of analytics, and Big Data technology architectures could be the only architecture.
Analytics are not a new idea. The tools have been used in business since the mid-1950s. To be sure, there has been an explosion of interest in the topic, but for the first half-century of activity, the way analytics were pursued in most organizations didn’t change that much. Let’s call the initial era Analytics 1.0.
Last week I spoke for IIA at Predictive Analytics World. Whereas I often speak to executives who aren’t yet persuaded of the virtues of analytics, at this gathering—which also included attendees of the Marketing Optimization Summit and Text Analytics World—that wasn’t the problem. I was preaching to the converted, who already undertake a wide variety [...]
Last week in the IIA we did a webcast for our Retail Analytics Research Council on “next best offers.” I’ve been doing some research on this issue with John Lucker and Leandro DalleMule from Deloitte. I don’t want to give away the punch line of the webcast—or the article we hope to write for Harvard [...]