The problem and promise of artificial intelligence (AI) is people. This has always been true, whatever our hopes (and fears) of robotic overlords taking over. In AI, and data science more generally, the trick is to blend the best of humans and machines. For some time, the AI industry’s cheerleaders have tended to stress the machine side of the equation. But as Spring Health data scientist Elena Dyachkova intimates, data (and the machines behind it) is only as useful as the people interpreting it are smart.
Let’s unpack that.
Dyachkova was replying to a comment made by Sarah Catanzaro, a general partner with Amplify Partners and former head of data at Mattermark. Discussing the utility of imperfect data/analysis in decision-making, Catanzaro says, “I think the data community often misses the value of reports and analysis that [are] flawed but directionally correct.” She then goes on to argue, “Many decisions don’t require high-precision insights; we shouldn’t shy from the quick and dirty in many contexts.”
