The promise of “we’ll fix it later”
Many systems are built around an unspoken promise: just get the data in, we will make sense of it later. It sounds pragmatic and flexible, especially under time pressure. In reality, it postpones design decisions and pushes complexity downstream.
- Validation turns into reconciliation.
- Structure turns into interpretation.
- Data trust turns into something you expect to emerge over time, rather than something the system actively enforces.
In practice, it rarely does. When data is ambiguous at the source, every downstream consumer is forced to interpret it. Dashboards, integrations, and AI models all make assumptions about what the data represents. Sometimes those assumptions align, but often they don’t.
When they drift apart, nothing fails visibly. Instead, inconsistencies accumulate quietly, and data quality erodes without triggering any clear signal that something is wrong.
The feature we deliberately chose not to build
Early on, many customers asked for the same thing: a contact database, a vendor database, a counterparty register inside the CLM. On paper, it made sense. Contracts revolve around external parties, and storing them inside the Contract Lifecycle Management system felt natural. But the more closely we examined it, the more uncomfortable the idea became.
Those records already existed elsewhere. In CRM systems, ERP systems, and finance or procurement platforms. They already had an owner. They were already part of established master data structures. Adding yet another place to store the same information would not improve data quality. It would multiply complexity. Customers would suddenly be responsible for keeping multiple sources of truth in sync. Update a vendor in one system, remember to update it in another. One missed entry or a failed webhook, and data quality starts to decay quietly.
That is not simplification. It is operational debt disguised as functionality.
Why duplication quietly destroys data quality
There is a comforting belief that duplication is manageable as long as people are careful. In practice, it isn’t. The moment the same entity can be edited in more than one system, you introduce questions with no good answers:
- Which system is the authoritative source of truth?
- What happens when values don’t match?
- Who owns reconciliation?
- How quickly does change propagate across systems?
- How do we deal with human error?
Most organizations never answer these questions explicitly. They rely on habit, workarounds, and tribal knowledge. That is how data quality erodes. Not through dramatic system failures, but through small, invisible inconsistencies that compound over time.
Free text, flexibility, and the cost of ambiguity
Free text is often defended in the name of flexibility. But flexibility without structure is simply ambiguity with better UX. When critical contract data is entered manually, the same entity is written slightly differently. Updates are applied unevenly. Context disappears the moment a document is signed. Nothing crashes. Nothing looks obviously wrong.
But trust slowly disappears, and once trust is gone, every report, integration, and AI insight built on that data becomes questionable.
Enforcing data quality at the point of creation
Eventually, one conclusion becomes unavoidable. If data matters, the system has to enforce correctness before the data exists. That means fewer places where data can be entered, fewer opportunities for interpretation, and stronger reliance on existing sources of truth such as CRM and ERP systems. In practice, this means contracts inside the CLM do not create their own versions of counterparties or master data. They reference what already exists and inherit structure, constraints, and ownership from the systems that already own that information.
Instead of letting related fields drift independently, they are bound together by design. Instead of fixing data inconsistencies later, they are prevented at the source. This approach can feel restrictive at first. It removes a certain kind of freedom. That is intentional. When contracts are involved, correctness matters more than flexibility.
What designing for data quality at the source enables
Designing for data quality at the source enables things that are otherwise fragile or impossible.
- Integrations become more reliable because systems refer to the same entities in the same way.
- Analytics becomes trustworthy because metrics are not built on contradictory inputs.
- AI becomes useful because it reasons over structured, consistent contract data instead of guessing from noise.
Just as importantly, organizations stop wasting time reconciling records that should never have diverged in the first place. This is not abstract future-proofing. It is about eliminating an entire class of data quality problems before they appear.
The unavoidable conclusion
If your approach to data relies on duplicating records, manual synchronization, or cleaning things up later, then data quality is already compromised. Not because users are careless, but because the system allows ambiguity at the source. The only reliable way to achieve high data quality is to design for it upstream. To be opinionated about structure. To be deliberate about references. To accept constraints in exchange for trust.
Everything else is damage control.
