04/25/2025

The Missing Data Link

Five practical lessons to scale your data products

Imagine you were a railway executive with a contract to transport valuable cargo across the country. You wouldn't have a different engine pulling each individual car of cargo. It would be much more efficient and cost-effective to hitch as many cargo cars as possible to the same engine. In fact, you would want a standard set of trains and connectors that would allow you to pull different kinds of cargo anywhere.

This analogy is particularly germane to the world of data products. Scale and value come from treating a data product like an engine that can support a large number of high-value use cases (or cars). Unfortunately, when it comes to data products, companies are operating much more along the single engine–single car model. The result is fragmenting data programs that fail to scale or generate the value that many had expected.

In some ways, this is a glass-half-full problem. When we wrote about data products in 2022, we detailed the advantages of managing data like a product. A data product delivers a high-quality, ready-to-use set of data that people across an organization can easily access and reuse for a variety of business opportunities (see sidebar, “What is a data product?”). Since then, organizations across sectors have started to adopt data products as core elements of their data and business strategies. The wave of enthusiasm surrounding gen AI has driven a wider appreciation in the boardroom of the importance of data and the need to better harness it.

Please select this link to read the complete article from McKinsey Digital.