Why Your Company Needs Data-product Managers
Creating a new role with a different set of skills
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.
In response, many companies have adopted the concept of data products — an attempt to create reusable datasets that can be analyzed in different ways by different users over time to solve a particular business problem. While some incorporate AI and analytics, others don’t, and so some organizations use two terms: data products (which are datasets suitable for reuse) and analytics products (which incorporate analytics or AI methods to analyze the data). While our definition of data products includes both data and analytics/AI, all that really matters is that an organization is clear on its terminology; a product orientation is useful for both data and analytics/AI.
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