What exactly do we mean by data-driven work?
Data-driven work means that people make decisions based on the same facts, the same insights, and the same reality, not each on their own, but collectively. This requires not only access to data, but also a shared understanding of what that data means. And that’s exactly where things often go wrong.
When the same numbers mean something different
In many organizations, there are multiple versions of the same truth. Not because someone is deliberately using incorrect figures, but because terms are interpreted differently.
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What is a “customer”?
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When do we call something an “incident”?
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Does a case count once it’s been created, closed, or approved?
Each department often has its own logical answer to these questions. Within the context of their work, that makes sense. But as soon as data is used organization-wide, misunderstandings arise. Reports contradict one another. Dashboards spark debate. And trust in the data declines, not because the data is incorrect, but because its meaning is unclear.
Metadata sounds technical, but it isn’t
The word “metadata” often puts people off. It sounds like something for specialists. In practice, it’s actually quite simple: agreements about meaning.
Among other things, metadata describes:
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exactly what a piece of data is
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where it comes from
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how it is used
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and who is responsible for it
Without that context, data remains a collection of numbers and fields. With context, it becomes information you can act on.
Why this is so often missing
The lack of clear definitions is rarely a conscious choice. It arises because organizations grow, change, and add systems. Agreements that were once implicit are never explicitly documented. And as long as data is used primarily locally, this goes unnoticed. It’s only when data is used broadly within the organization, or in partnerships with other organizations, that the problem becomes apparent. Then it turns out that everyone is working with the same numbers, yet seeing something different.
How this affects trust in data
As soon as definitions are called into question, trust disappears. Discussions are no longer about the content, but about the numbers themselves. Decisions are postponed. And data loses its role as an objective basis for decisions. Data-driven work then stalls not because of technology, but because of interpretation.
How organizations can make progress here
Organizations that are making strides in this area don’t try to define everything at once. They start with what matters, the concepts that are essential for decision-making.
They ask questions such as:
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What data are we actually talking about here?
- What exactly do we mean by this concept?
- And are we agreeing on this together, or just within a single department?
Those conversations can sometimes be uncomfortable, but they’re necessary. Because without a shared language, there’s no shared understanding.
Start small
At Enable U, we help organizations make data not only accessible but also understandable. We assist in clarifying definitions and establishing context, without turning this into a cumbersome or theoretical process. Not by writing thick manuals, but by capturing metadata right where the data is used. In alignment with processes, integrations, and governance. This way, data becomes not only available but also usable.
The same principle applies here: start small to demonstrate value. Begin with a single report, a single dashboard, or a single decision-making process. Make definitions explicit, document them, and use them consistently. That may seem like a minor detail. But without shared definitions, insight remains an illusion.
Data-driven work doesn’t start with more data.
It starts with meaningful data.
This blog post is a contribution from Enable-U, The Data & Integration Enablers. Enable-U helps organizations securely and reliably connect systems, applications, and data so they can operate in a data-driven way. For more information, visit https://enable-u.nl/en/ or stop by the Enable-U booth at Data Expo.
