Do Your Data Scientists Know the ‘Why’ Behind Their Work?

Data science, broadly defined, has been around for a long time. But the failure rates of big data projects in general and AI projects in particular remain disturbingly high. And despite the hype (e.g., “data is the new oil”), companies have yet to cite the contributions of data science to their bottom lines. What is going on?

Recently, Ron Kenett, the distinguished Israel-based data scientist, and I compared notes on our own successes and failures — and those of our colleagues — in helping companies with data science. It was immediately clear that the biggest successes stemmed not simply from technical excellence but from softer factors such as a deep understanding of business problems; building the trust of decision makers; explaining results in simple, powerful ways; and working patiently to address dozens of concerns among those impacted.

Author: Thomas C. Redman

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