A machine learning project must be both impactful and feasible to warrant an investment. While the market factors determining a project’s impact are out of a company’s control, organizations can complete a lot more impactful projects by improving feasibility.
In many cases, trying to improve the model’s code is a red herring. Instead, organizations should focus on the quality and size of their data. Focusing on data quality will enable organizations to deploy and iterate on successful models more rapidly. The faster they can move, the greater their chance of outstripping their competition.