Intelligence at scale: Data monetization in the age of gen AI
Even before the breakthrough of AI, it was a bad idea to sell your data instead of monetizing the products derived from data and analytics, aka data products. With AI in general and generative AI in particular, data production is simplified and accelerated:
“By harnessing gen AI, companies can leapfrog from selling data to building robust data products that deliver actionable intelligence and unlock previously untapped value.”
However, the technology should not come first, but rather the strategy:
“The most successful data monetization strategies start with a sharp understanding of a company’s proprietary advantage. Whether this advantage is privileged access to high-quality data, deep customer knowledge, or domain-specific infrastructure, data product strategies should be grounded in what makes the organization uniquely positioned to win.”
At Datentreiber, we use the Data & AI Business Design Method, which consists of three crucial phases:
1) Business Understanding: To answer the question of how data products can be used to create a unique value proposition, it is essential to thoroughly understand not only your own business model, but also that of your customers. In other words, what makes your business model unique, and how do data products create value for your customers’ business models? Helpful tools include the Business Model Canvas and Value Chain Canvas.
2) User Understanding: In this case, the users are your customers. There are different roles to consider: end users, economic buyers, and decision makers. Here, we use the Stakeholder Analysis and Analytics & AI Use Case canvases.
3) Data Understanding: What data sets do we own that are exclusive to us? This creates a defensible unique selling point and prevents imitators if the data products are successful. We use the Data Monetization and Data Landscape canvases.
As McKinsey pointedly notes in its article:
“Companies that design a clear road map to identify where and how their data can drive the most impact are also well positioned to succeed. The most successful data monetization efforts treat it not as a side project but as a vehicle for business building. […] This involves understanding the unique value of an organization’s data, prioritizing high-potential use cases, and aligning efforts with broader business objectives. Without this clarity, even the most promising data initiatives risk becoming misaligned with strategic goals.”
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