![]() The process or methodology of CRISP-DM is described in these six major stepsįocuses on understanding the project objectives and requirements from a business perspective, and then converting this knowledge into a data mining problem definition and a preliminary plan. SAS Institute that’s been around longer than anyone can remember had its own version called SEMMA (Sample, Explore, Modify, Model, Assess) but within just a year or two many more practitioners were basing their approach on CRISP-DM. ![]() ![]() I’m honored to say that I was one of the original contributors to that SIG.ĬRISP-DM was not actually the first. Two of leading tools providers of the day, SPSS and Teradata, along with three early adopter user corporations, Daimler, NCR, and OHRA convened a special interest group (SIG) in 1996 (also probably one of the earliest collaborative efforts over the newly available worldwide web) and over the course of less than a year managed to codify what is still today the CRISP-DM, Cross Industry Standard Process for Data Mining. Efforts like this always start out by wondering aloud whether there even was a common approach given that the problems looked so dissimilar. Seldom were there more than one or two ‘data scientists’ in the same room and we were much more likely to be called ‘predictive modelers’ since that type of modeling was state-of-the-art in its day.Īs the 90’s progressed there was a natural flow that drew us toward standardizing the lessons we’d learned into a common methodology. In the early 1990s as data mining was evolving from toddler to adolescent we spent a lot of time getting the data ready for the fairly limited tools and limited computing power of the day. ![]() This includes not only traditional data analytic projects but also our most advanced recommenders, text, image, and language processing, deep learning, and AI projects. Summary: To ensure quality in your data science group, make sure you’re enforcing a standard methodology. ![]()
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