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. 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. 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.

That enthusiasm, however, has produced mixed results. Confusion about how data products deliver value, governance practices that favor the individual use case over larger ROI benefits, and institutional incentives that reward building data products over scaling them all have a role in choking value. With companies increasingly relying on data—from harnessing gen AI to developing digital twins—to innovate and expand the business, ineffective or nonexistent data product practices are becoming a top strategic issue.

According to the authors’ experience working with dozens of companies in the past few years has shown that building valuable data products is much less of a technical challenge than a strategic and operational one. That experience can be boiled down to five key lessons:

1. It’s about more value, not better data. The goal of developing data products isn’t to generate better data; it’s to generate value. No data product program should begin until leadership has a firm grasp of the value that each use case can generate and prioritized the biggest opportunities.
2. Understand the economics of data products. A data product’s effectiveness is based on the “flywheel effect” of accelerating value capture and reducing costs with each additional business case that it enables.
3. Build data products that can power the flywheel effect. Harnessing the flywheel effect of ever-lowering costs and -rising value requires building a capability that maximizes reuse and reduces rework.
4. Find people who can run data products like a business. Put in place empowered data product owners (DPOs) and senior data leaders who understand what matters to the business, from articulating the value in business terms to building support.
5. Integrate gen AI into the data product program. Gen AI is already proving that it can help develop better data products faster (as much as three times faster) and cheaper than other methods.

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