Monday, March 2, 2015

Board Deck Week

I sit here Monday morning with about ten data requests littering my inbox.  All due by Wednesday.  While this is not an unmanageable task (in fact if I wanted to, and skipped a meeting, I could probably have these all done by noon without any team member help), but it is quite a task to undertake and verify accuracy of results.

The requests are data for various decks and charts to be  presented at this weeks Board of Directors meeting.  I'm seeing three general categories of requests.

  1. Ad hoc query requests. Example: what do customer growth trends look like over time?
  2. Ad hoc modeling requests. Generally phrased as "what if". .. for instance what if the regulatory environment in all states start looking like Washington State?  Actually created something I termed a stochastic customer entropy model to solve this problem.
  3. Model performance requests. Example: what kind of lift have the new underwriting models created?

I actually have quite a bit of experience with these kinds of requests.  At my old job the team had "Board Deck Week" where we would spend almost all of our time pulling ad hoc queries, cutting data new ways, and creating new models for business analysis.  Though this kind of work isn't necessarily the type of thing that Data Scientists like spending their time on, it speaks to the value that data science teams bring to businesses.

For the simpler requests, it's a recognition that the data science team can quickly pull data from databases, and analyze them in ways that can be Board-ready within a matter of hours.  It's just a verification of the known skills of data scientists.

For some of the more complex requests, it's a vote of confidence in the value-added nature of data science teams.  In some cases the executive team has a question they've never been able to address, and see models as a legitimate way to get there.

In other cases, if the executives are interested in actual model results, the importance of data science speaks for itself.  What this means, is that our models are deployed in areas of the business so important that their results need to be reported the Board.  While this can create a lot of anxiety on the team, a payoff exists as well, as the team is obviously viewed as important to the business, and if models perform poorly, the business is likely to give us a chance to improve them rather than throw them away.

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