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  • When predictive analytics models produce false outcomes

    Predictions are very difficult, especially when they're about the future. Whether attributed in part or whole to Nobel physicist Niels Bohr or Hall of Fame catcher Yogi Berra, at no time was that prescient observation more applicable than in predicting the outcome of the 2016 presidential election.

    The cover story of Business Information's February issue examines some of the hard lessons that, in retrospect, need to be applied when it comes to building and running predictive analytics models, be it in the political realm or the business world. Those lessons include working with high-quality data, measuring the right information, and making sure predictive models reflect reality and not wishful thinking.

    In another feature, we look at a critical first step in ensuring that predictive analytics models yield valid outcomes. The proper preparation of data -- a process made more complex with the proliferation of big data and the expansion of predictive analytics applications to leverage it -- requires new tools and techniques.

    Also in this issue, learn how the self-proclaimed "CRM and analytics czar" of pro basketball's Charlotte Hornets convinced team ownership to invest in Phizzle FanTracker, which helped combine millions of fan records into accurate, up-to-date, individual fan profiles.

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  • Machine learning projects face data prep, model building hurdles

    IT and analytics managers discuss the biggest challenges of machine learning applications, with data preparation and development of algorithm-driven analytical models sharing top billing. Continue Reading

  • New data preparation tools open up more info to analysts

    Consultant David Loshin says self-service data preparation software helps give data analysts broader access to business information than they typically can get in a data warehouse. Continue Reading

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