Most data scientists naturally gravitate towards the fun parts of data science — developing a technically advanced, sophisticated machine learning model. However, a good chunk of data science managers invest too much time in a model’s technical design, and don’t invest enough time in developing deeper understanding of the business problem the model is intended to solve. As a result, technically successful projects are deployed into production, only to fall short of delivering the anticipated business value and taking it’s permanent home in what I like to call the `data science graveyard`. As a data science manager of a large team, I’ve buried more projects in that graveyard than I care to admit. However, these experiences have taught me valuable lessons about ensuring data science projects generate real business value. In this article, I’ll share the four important lessons that data science managers can use to ensure that data science projects generate clear and meaningful business impact.
Most, if not all, companies share strategic goals and objectives on some regular cadence. An example goal might look like…