bringing predictive insights to the people via the wizard-driven, self-service design and modeling environment we expose as part of Predixion Insight. Insight enables the equivalent of a what-you-see-is-whatyou-get (WYSIWYG) design experience for predictive analytics. If you’re a highly technical user, you can bypass its wizards and jump right in. However, even the most gifted of technicians will appreciate the conveniences we’ve built into Insight – e.g., detailed statistical breakdowns on stuff like variable distributions and correlation relationships, along with other, even more esoteric stats.
analytics.” This is a term that Predixion first coined and which others have since used. It speaks to the problem of enabling on-site, in-the-field, in situ employees to make real-time decisions on the basis of predictive analytic insights. I’m talking about people who don’t know what an algorithm is – nor should they. Think of the nurse in the ICU, the sales person in the waiting room, the technician on the tarmac – the people who ultimately interpret and act on predictive insights.
Because it’s a WYSIWYG-like design tool, Insight can be used productively by non-technical people, too. Subject matter experts and other smart, savvy users might not know much about predictive modeling but they do know an awful lot about their business domains. Insight’s wizards and self-service capabilities can guide the user through the process of creating a predictive model, automate the selection of algorithms and variables, and even automatically create and score a model.
The “classical model” of predictive analytics isn’t just unwieldy, it’s pernicious: it gives disproportionate emphasis to a small (if critical) part of the predictive analytic process – namely, planning, preparation, and development – at the expense of the whole. As I’ve tried to point out, it all but ignores the myriad ways in which insights are used and acted upon by people in the real world: by nurses, doctors, and veterinarians; by retail sales clerks, delivery truck drivers, and plant managers; by social workers, rescue and aid workers, and community organizers; by engineers, scientific researchers, and academics: by individuals in occupations or avocations of every kind.
We also innovated around the end-user consumption paradigm that we call the “last mile of predictive
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