What “Correct” Actually Means in a CRM
Most conversations about CRM data quality eventually arrive at the same conclusion – the data isn’t correct.
Pipelines feel unreliable. Reports require explanation. Numbers are “mostly right,” but rarely trusted without context. Teams sense the problem clearly, even if they struggle to name it.
What’s rarely examined is what correct actually means.
CRMs enforce structure. They require fields, validate formats, and guide records through stages. What they do not do is define meaning. That part is left to people — and usually left implicit. As long as records look complete and processes move forward, the system appears to work.
Problems surface later, when decisions depend on data whose meaning was never clearly agreed upon.
This is not a tooling issue. It’s a design issue. “Correct” is not a feature of a CRM. It’s a decision that has to be made explicitly. Without that decision, systems quietly drift away from reality while still appearing functional.
Validation Rules Don’t Define Correctness
Most CRMs rely heavily on validation rules to enforce quality. Fields are required. Formats are checked. Certain actions are blocked unless conditions are met.
This creates the impression that correctness is being enforced.
In reality, validation rules only confirm that something has been entered — not that it is true, usable, or reliable. They ensure presence, not meaning.
A familiar example is contact data. An email address can be syntactically valid and still useless. A website can exist but be outdated, abandoned, or unrelated to the actual decision-maker. A phone number can follow the correct format and never be answered.
From the CRM’s perspective, these values are correct. They pass validation. From an operational perspective, they may be worse than missing — because they create false confidence.
Correctness, in this context, is not about format. It’s about fitness for purpose. An email address is only correct if it can realistically be used for communication. A website is only correct if it reflects the entity you’re actually dealing with. A phone number is only correct if someone is expected to answer it.
CRMs rarely encode this distinction. They enforce structure, not usability. The moment teams rely on format as a proxy for truth, correctness becomes performative rather than functional.
Correctness Always Depends on Context
Another common mistake is treating correctness as an absolute property of data. Either a value is correct, or it isn’t.
In practice, correctness is contextual.
What is considered correct early in a process is not the same as what is considered correct later. A value that is acceptable for initial qualification may be unacceptable for forecasting, pricing, or contractual decisions.
This becomes especially visible when data comes from outside the organization. Large customers often provide spreadsheets with consumption figures, contract end dates, or site details. The data looks official, structured, and complete — and is therefore taken for granted.
In reality, those records are often outdated. Internal changes aren’t reflected. Meters are added or removed. Contracts are renegotiated. The spreadsheet represents a snapshot in time, not a continuously maintained source of truth.
Treating externally supplied data as inherently correct is a contextual mistake. It may be acceptable early on, when rough orientation is sufficient. It becomes dangerous later, when decisions depend on it.
Correctness is not a property of a field.
It is a property of a moment in a process.
Readiness: When a Record Is Actually Allowed to Move Forward
Every stage transition in a CRM implies readiness, whether that readiness is defined or not.
When a record moves forward, the system is implicitly claiming that something is now true. The deal is qualified. The information is sufficient. The next team can act.
In many mid-sized businesses, this assumption is where things start to break.
Take annual consumption (AQ) in energy brokerage as a concrete example. AQ is often required early in the sales process. Without a recent bill or another reliable source, the number is usually an estimate. Sometimes educated. Sometimes optimistic. Sometimes entered under pressure simply to move the deal forward.
From the CRM’s point of view, the record looks complete. The field is populated. The deal advances. Pricing models, margin expectations, and internal forecasts begin to rely on that value.
Only later does the assumption surface — often when an actual bill arrives and the numbers no longer align. At that point, pricing changes, margins shift, or the deal becomes unviable.
The CRM didn’t fail. It enforced what it was asked to enforce.
What failed was the definition of readiness.
The system allowed the record to progress without a shared understanding of what “good enough” meant for a value that materially affected the outcome. Downstream teams inherited an assumption they didn’t know was an assumption.
Every industry has its own version of AQ: budgets, deal size, delivery scope, contract dates. Values that are easy to enter, hard to verify, and dangerous when treated as final too early.
When readiness isn’t explicitly defined, people optimize for flow. Records move because they can, not because they should.
Correctness is not a property of a field.
It is a property of a moment in a process.
Who Owns Being Correct — Not Updating, but Being Right
CRMs are collaborative by design. Records are touched by many people: sales creates them, operations enriches them, management reviews them, reporting aggregates them.
Over time, responsibility becomes diffuse.
Updating a record is not the same as owning its correctness.
In many organizations, sales is expected to “own” deals, but that ownership is rarely defined beyond initial progression. Operations may add or adjust data, but without authority to challenge assumptions. Management influences outcomes indirectly through targets and pressure, without touching records themselves.
When a value turns out to be wrong, it’s often unclear who was responsible for ensuring it was right at that stage. Everyone interacted with the record. No one owned correctness explicitly.
This becomes particularly visible when estimates are never formally upgraded. Early assumptions quietly turn into facts because nothing in the process requires proof. An estimated value that was acceptable for orientation is later treated as decision-grade data.
Correct ownership means deciding not just who owns a value, but when evidence is required. An estimate may be acceptable early on. At later stages, a verified source becomes mandatory.
Correctness without ownership is just optimism.
Why CRMs Drift Away from “Correct” Over Time
Even systems that start with clear intentions rarely stay aligned.
Definitions are made once, often during implementation, and then assumed to hold forever. Meanwhile, the business evolves. Suppliers introduce new options. Commercial models change. Regulatory requirements shift.
The CRM, however, continues to encode yesterday’s reality.
At first, this shows up as inconvenience. Users work around missing categories. Free-text fields appear. Notes replace structure. Over time, the system loses the ability to represent what is actually happening in the business.
At that point, correctness becomes impossible to express — not because users don’t care, but because the system enforces outdated assumptions.
This kind of drift is subtle. Reports still produce numbers. Pipelines still move. The loss of trust happens quietly, through accumulated exceptions rather than visible failure.
Correctness is not something that can be defined once and forgotten. It has to be revisited as the business changes.
What Defining “Correct” Actually Changes
Defining correctness does not require more rules or more training.
It requires clarity.
When teams explicitly decide what must be true at different points in a process — and who is responsible for ensuring it — several things change almost immediately:
- Records stop moving forward on assumption alone.
- Handoffs become cleaner because expectations are explicit.
- Reporting debates shift from “is this wrong?” to “what does this represent?”
- Data becomes boring in the best possible way.
This does not mean everything becomes perfectly accurate. It means inaccuracies are visible, understood, and owned.
Correctness Is a Design Decision, Not a CRM Feature
CRMs are very good at enforcing decisions. They are very bad at making them.
If “correct” is not explicitly defined, the system will still function — but it will function on guesses, habits, and convenience. Over time, those guesses harden into structure, and the gap between what the CRM claims and what the business experiences grows wider.
Correctness has to be designed. It has to account for context, readiness, and ownership. And it has to be revisited as reality changes.
Until that happens, even the most sophisticated CRM will quietly drift away from reality — not because people refuse to use it, but because no one ever decided what being right was supposed to mean.