Читать книгу Minding the Machines - Jeremy Adamson - Страница 14

The business will rarely, if ever, have the analytics knowledge and vernacular required to clearly articulate its needs and to formulate a problem statement that naturally lends itself to an analytical solution. Whereas Kaggle competitions, hackathons, boot camps, and university assignments present problems with a well-formed data set and a clear desired outcome, business problems are fuzzy, poorly defined, and often posited without a known objective. As practitioners, it is our responsibility to find the underlying issue and present the most situationally appropriate and practical solution.

Advanced analytics and AI practitioners can often have the expectation that their stakeholder group will provide a solution for them. Just as a doctor cannot expect a patient to diagnose their own health issues and for the doctor’s approval an analytics team cannot expect a business unit to suggest an approach, provide a well-formed data set and an objective function, and request a model. What the business unit requests is very often not even what the analytics project lead hears.

Early in the project intake process, an analytics lead will meet with a business lead to discuss an opportunity. The business leader (actuarial, in this example) may say that they want a model that predicts the probability that a policyholder will lapse. The outcome that the leader is hoping for is a way to reduce their lapse rate, but what the analyst hears is, “Ignoring all other considerations, how can I best predict the probability of an individual lapsing?” If the practitioner executes on this misapprehension, the deliverable will have little use for the business; a prediction model of this sort has no operational value. This model would only work on a macro scale, and even if it could be disaggregated, the business would be making expensive concessions in the face of perceived threats.

Empathizing with the underlying needs of the business, understanding what success looks like for the project, and leveraging the domain knowledge of the project sponsor would have highlighted that the value in the analysis was further upstream. The factors driving lapse behavior were where the value to the business was and where an operationalizable change in process was possible.

As with the doctor analogy, it is through deep questioning, structured thinking, and the expert application of professional experience that the ideal path forward is uncovered. That path requires collaboration and the union of deep domain knowledge with analytical expertise.