What’s your Instagram data worth? Most people don’t know. We sense data is valuable, but struggle to define how and why. For business leaders, this isn’t a theoretical issue. Companies that harness data effectively outperform their peers. But value doesn’t come from data alone. It comes from how you use it.
In our book Datanomics, co-authored with Simon Chignard, we introduced a simple framework to clarify this challenge. It distinguishes three forms of data value: commodity, lever, and asset. Each represents a distinct opportunity — and a different kind of return on your data investments.
1. Data as a Commodity
Value = Revenue potential
Some data can be sold. Think geolocation data, credit histories, or consumer preferences. Data brokers build multi-billion-dollar businesses collecting and reselling this information. OpenAI’s $250 million deal with NewsCorp to license content for training its AI models is a recent high-profile example.
If your company collects unique, high-quality data, it may have market value. But don’t expect high margins — this is a volume game, with increasing regulatory scrutiny and diminishing exclusivity.
Key question: Is any of our data commercially valuable to others, and under what terms?
2. Data as a Lever
Value = Efficiency and growth
More powerful is using data to improve your core business. Predictive maintenance in manufacturing, personalized recommendations in retail, dynamic pricing in logistics, these are all examples where data is a multiplier on existing capabilities.
TotalEnergies, for instance, saves tens of millions annually using sensor data to anticipate equipment failures. Amazon’s recommendation engine reportedly drives nearly half its online sales. These are not new businesses, just better ones.
Key question: Where can data help us do what we already do, faster, cheaper, or better?
3. Data as an Asset
Value = Strategic differentiation
The most strategic use of data treats it as a long-term resource. Not traded. Not just applied. But embedded in your competitive advantage.
Tesla’s real-time driving data, continuously training its autonomous driving algorithms, is a classic case. So is Delta Airlines, which backed a $6.5 billion debt deal using its frequent flyer data as collateral. These aren’t just operational plays, they’re moves that shape industry positioning.
Key question: What proprietary data could strengthen our strategic control or valuation?
A Final Word: Value = Intent × Execution
These three forms of value aren’t mutually exclusive. The same data can be sold, used, and invested, but the return depends on clarity of intent and execution.
Many companies treat data like oil: valuable in itself. But oil doesn’t create value, engines do.
Executives don’t need to be data scientists. But they do need to be value architects. Ask not just “what data do we have?” but “what role should this data play in our business model?”
Photo de Raul Petri sur Unsplash.
Generative AI was used for editing this post.
