There’s a familiar problem with companies’ efforts to build AI and analytics applications: They hire or engage with data scientists to build models, but the models are rarely deployed into production. A recent survey of data scientists found that the majority saw 20% or fewer of their models go into production deployment.
Why Your Company Needs Data-Product Managers
As companies have struggled to make use of datasets and AI, many have started to create data products — reusable datasets that can be analyzed in different ways by different users over time to solve a particular business problem. Data products can be a powerful tool, especially for large, legacy companies, but often require companies to create a new role that’s distinct from chief digital officer and product manager: the data product manager. Data product managers, like product managers of other types, don’t have all the technical or analytical expertise to create the model or engineer the data for it. They are unlikely to be gifted at redesigning business processes or retraining workers either. What they do need to have is the ability to manage a cross-functional product development and deployment process, and a team of people with diverse skills to perform the needed tasks.