of data scientists found that the majority saw 20% or fewer of their models go into production deployment.— 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 and analytics products .
This idea isn’t necessarily new. Data products involving analytics have been in use for at least a decade at digital-native firms. But as legacy companies start to adopt them, many are struggling with implementing the idea — both internally and for customers. For one, they typically sell tangible products, and may struggle with data products as a result. And while many large companies are naming chief data officers, product management disciplines aren’t generally inherent in CDO roles.
There are already models for what this role can look like. Some of the same skills needed for data products are found in software product management, including coordinating across functions and units, managing diverse teams, exercising influence without formal authority, and understanding customer needs. And since data products also involve software, both software and data product managers need to understand software development.
Data product management responsibilities don’t end with deployment. Data product managers are responsible for ensuring and measuring the ongoing use and value of the data product on a quarterly basis, whether the product has internal or external customers.The only consensus among devotees of data product management is that the product managers need to have both a business orientation and a familiarity with data and analytics, and that they need to be able to lead a diverse team.
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