At the recent Gartner Data & Analytics Summit India, there was an interesting session on 'Dangerous Data'. Gareth Herschel, VP Analyst, Research Engagement Services, Gartner helped us to double-click on this subject on the sidelines of this summit. It was both fun and thought-provoking to juggle issues like possession of tricky data, plausible deniability, third-party data and data localization while also fiddling with possible answers like privacy cloak, Blockchain, Zero-proof models and data ethics.
How would you explain 'dangerous data' to an ignorant CEO if you had to - in two or three lines?
Your organization is collecting data as a necessary part of its normal operations that has potentially negative consequences. Even worse, there is often no automatically "correct" way of responding to these consequences as both taking an action, and failing to take that action could be considered inappropriate depending upon the priorities you attach to different outcomes.
Any recent examples or possibilities that you can cite to explain 'dangerous data'?
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