Powering Ongoing Performance
Utilities have not always had wide-reaching success with BI implementations. Still, when it comes to predictive analytics, the industry is moving aggressively into the space by 2014, particularly with tools for monitoring usage and outages, according to industry watcher BRIDGE Energy Group. For instance, CenterPoint Energy can now outline analyze automated data from its metering systems to move toward information sharing across new departments and with users.
Steadying Volatile Markets
Recently at the IE Predictive Analytics Summit, data scientist officers at Sears Holding Corporation, the parent company of the retailer, presented the makeup of the more than 100 performance metrics they tap into for predictive financial models. As analyst/blogger Steve Miller wrote about the presentation: The DS group then creates segments using techniques like k-mean clustering, and examines KPI trends against features volatility and momentum that are so important in financial services. With support vector machines and neural nets as their essential classification engines, the authors note off-the-charts efficiency improvements from the efforts.
Finding the Right Person for the Job
Dean Abbot, president of Abbot Analytics, detailed his experience consulting the U.S. Special Forces on data models to help assess new candidates. The models there could be transferred to a predictive framework for any employer looking for the best fit for an open position. Here was Abbots approach to the Special Forces gig and others: The question is, How much does each factor matter? You can find acceptable trade-offs (read: trainable) with a model that gives pertinent weight to questions more vital to intelligence and learning (or whatever is most vital for the position you're looking to fill). And a good target variable is the people who have stayed and succeeded (though that can take a while and not always give a huge pool of results), and a small, quick pool to avoid too much noise.