The VP of Analytics can’t do it all

February 1, 2014 — Leave a comment

Don’t expect the new VP of Advanced Analytics to do it all.  When processes and decision-making have to change to exploit insights from data, the line managers, with executive sponsorship, must carry the weight.  Without that, you have models that work on paper but aren’t used in the operations.  It’s tempting to blame the analytics leader but that is misplaced.  Operations managers actually resist doing things in a new way, despite the math telling them otherwise. And the VP of Analytics likely has no power to change that, short of appealing to the executive staff–not a popular move for the analytics lead that also must evangelize for new projects and problems to solve.

Also, commanding a small specialized team is not normally seen as a position of clout, despite the title and sponsorship.  Managers of large operations (financial and headcount) naturally have more influence in most organizations, even if they have a peer title to the advanced analytics leader.

In defense of the operations level manager, they are rightfully reluctant to be accountable for mathematics that is probablistic and hard to understand.  Likely they are also normally protective over the operational influence that analytics can have within their business units.  This creates tension that makes analytics ROI go sideways.

What’s the answer?  For analytics to truly be exploited, operations management must step up….understand the science more, be ready to believe in it and lead their operations to adopting it.  That means they are hired for it, trained for it and managed for it.  How an organization mobilizes for that is under the leadership of a Chief Analytics Officer and a full program management approach to advanced analytics.  Deploying advanced analytics must be seen as the path to promotion for career operations managers.

Secondly, the advanced analytics effort must include Test and Learn experiments for every model pilot that help prove the in-use value of models beyond validation on historic data.  This is a natural extension of the model development work

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