You built that famous data lake meant to solve everything: centralize data, break down silos, make information accessible. On paper, it's all there. And yet teams keep searching, decisions take time, and dependencies remain hard to understand.
A data lake, on its own, doesn't create value. As Gartner points out, the value isn't in the volume but in the ability to use data in a precise business context. We collected; did we actually understand?
The myth: more data, more value
A data lake lets you store. But storing doesn't mean understanding, connecting, exploiting, or deciding. The idea was appealing: bring all the data together in one place to break down silos. In reality, we often just moved the problem.
You end up with an immense reservoir, but no map to extract meaning from it. The lake exists; the system for understanding it is missing.
The real problem: data with no structure of meaning
An isolated piece of data has little use. Its value comes from what it represents, how it's defined, the context it sits in, and above all the links it has with other elements of your ecosystem. In many data lakes, data coexists without communicating with each other in a usable way.
McKinsey warns: without structuring and integration into business processes, data initiatives struggle to show a return on investment. The result is palpable frustration, with business experts having to rebuild the puzzle and reinterpret meaning with every new request.
Data-first or knowledge-first: a paradigm shift
The traditional approach is data-first: collect massively, store, and try to structure afterward. In complex industrial environments, this method quickly hits its limits. Deloitte even talks about data swamps, those data marshes where information gets bogged down and unusable.
It's time to flip the logic. The knowledge-first approach starts from your most valuable intangible asset, business knowledge: the concepts, rules, relationships and dependencies that govern your activity are modeled first, and the data then connects to that framework of meaning.
Why knowledge-first changes everything
With this approach, data is no longer isolated points on a map: it becomes attributes connected to concrete business objects, a production line, an asset, a quality process. It gains meaning immediately.
You stop just asking "where's the data?" and can ask the real questions.
- +What does this value mean for my process?
- +What other equipment or alert is it linked to?
- +If this parameter changes, what's the impact on my production or maintenance?
The architecture that makes the shift happen
The platform acts as a nervous system for your industrial data.
The result is tangible: data is no longer just accessible, it becomes understandable and directly useful for action. IDC estimates that employees can spend up to 30% of their time searching for and cross-referencing information; that time gets reinvested into analysis and decisions.
Because at the end of the day, data alone informs. Knowledge lets you act.
Sources
- Gartner, 5 Best Practices for Data and Analytics Governance.
- McKinsey, The data-driven enterprise of 2025.
- Deloitte, Data management techniques, approaches and tools: the risk of the data swamp.