Focusing on Value Creation

Data teams need to stop looking for a problem to solve and solve a real business problem. The days of just looking for a business problem to solve or just storing data in the hopes the data team will eventually create value are gone (this shouldn’t have been possible in the first place). If teams aren’t creating any business value, management needs to go back to the business to see how existing and emerging problems can be solved with data.

We need to be focusing on creating models that optimize or improve efficiency. These models should save the company money or improve a process within it. Some examples would be models for pricing optimization, inventory management, ad spend, or customer acquisition. Some organizations have been delaying the deployment of a model to production until there is a higher improvement in optimization over an existing model or even the lack of a machine learning model. Pre-COVID-19 era, this sort of delay was feasible until the model improvements met the data science team’s approval. In this COVID-19 era, even marginal improvements could be the difference between a company staying afloat versus layoffs. The models could be the driver for some improvements that weren’t critical before but are vital now.

While deploying models is all well and good, the organization is still constrained by the quality of its data. If it hasn’t already, the data engineering team needs to be creating value with high-quality data products and correct infrastructure so the data scientists can create meaningful models.