Driving Data Science Productivity Without Compromising Quality

How will data science teams maintain quality standards in the face of advancing automation? Attend the IBM DataFirst Launch Event on Sep 27 in NYC and learn how to drive greater productivity from your data science teams without compromising the quality of the mission-critical business assets they produce. Productivity in data science isn’t a matter of output in any quantitative sense. It’s more an issue of the quality of what data scientists produce. In a data-science context, quality refers to the validity and relevance of the insights that statistical models are able to distill from the data. As I stated recently, the more data you have, the more stories that data scientists can tell with it, though many of those narratives may be entirely (albeit inadvertently) fictitious. Given the paramount importance…

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