The Economics of Data Products

Chief Data Officers (CDO) and Chief Data Analytics Officers (CDAO) are under intense pressure to find ways to "monetize" their growing volumes of data. While some organizations seek "monetization" by trying to sell their data, as I discussed in the "4 Types of Data Monetization", more and more organizations are realizing that the most impactful, profitable, and scalable way to monetize their data is by uncovering and applying the customer, product, and operational predictive insights buried in their data to their business to drive quantifiable financial outcomes. This is a natural maturation for data and analytics organizations that corresponds to the Insights Monetization phase of the Data & Analytics Business Model Maturity (Figure 1). As discussed in "It's Insights Monetization, Not Data Monetization", there are two ways that organizations can monetize their data in the "Insights Monetization" phase: I have written and talked extensively about the internal application of insights to derive and drive value (see my "The Art of Thinking Like a Data Scientist"), and I want to use this blog to further explore the external application of insights via data products. Data Products are a category of domain-infused, AI-powered apps designed to help non-technical users manage data-intensive operations to achieve specific business outcomes.