Analytics Governance

TL;DR for operators The useful question is not whether AI will “replace data scientists”. That framing is wonderfully dramatic and operationally lazy. Timpone and Yang’s paper, AI, Humans, and Data Science: Optimizing Roles Across Workflows and the Workforce, gives a better mechanism: allocate human and AI work by asking what kind of quality each workflow stage needs.1 Early planning needs creative breadth and problem definition. Execution needs accurate, valid, and ethically defensible data and modelling. Activation needs contextual interpretation, stakeholder judgement, and responsible action. ...

TL;DR for operators Most business causal analysis begins with an uncomfortable little fiction: that someone knows the causal graph. The marketing team wants to know whether a campaign caused retention. The risk team wants to know whether a policy change reduced defaults. The operations team wants to know whether a staffing rule improved service levels. Everyone has observational data. Nobody has a clean experimental intervention. Somewhere, usually in a deck with too many arrows, a causal diagram appears. ...