Most organizations have some version of a data quality framework. Fewer have one that works. The gap between having a framework and having a functioning one is wide, and it tends to widen quietly — through gradual neglect, accumulated technical debt, and the slow erosion of accountability that happens when no one is specifically paid to care.
Here are seven signs that your data quality framework has stopped doing its job and what each one tells you about the underlying problem.
1. Your Quality Metrics Are Green but Business Complaints Are Constant
This is the most common pattern and the most diagnostic. The data quality dashboard shows acceptable or good scores across completeness, accuracy, and timeliness dimensions. Meanwhile, the business analytics team regularly flags data problems, analysts spend significant time cleaning data before they can use it, and reports occasionally publish numbers that don't match reality.
When this gap exists, the framework is measuring the wrong things or measuring them at the wrong level of granularity. Technical completeness metrics, for instance, confirm that a field is populated. They do not confirm that the value is correct, that it was populated using consistent logic, or that it means the same thing across systems.
The fix is to work backward from actual business pain.
- Document recent data quality failures that caused business impact.
- Ask what metric, if it had been in place, would have caught each failure.
Build your measurement framework from that list, not from a generic data quality model.
2. No One Can Name the Owner of a Quality Problem
When a data quality issue surfaces, what happens? If the answer involves a period of investigation to determine whose domain the data belongs to, followed by a negotiation about whether the issue is a source system problem or a transformation problem, followed by someone filing a ticket that ages in a queue, the framework has an accountability gap.
Quality frameworks require explicit ownership. Not nominal ownership listed in an RACI document, but operational ownership: a named person with clear responsibility for defined datasets, the authority to take corrective action, and a defined process for escalation when the issue exceeds their scope.
If your framework does not have this, quality issues will be discovered, discussed, and inadequately resolved. The same categories of problems will recur because there is no one whose job it is to prevent recurrence.
3. Rules Are Defined But No One Reviews Exceptions
Many organizations have invested in data quality tooling that runs automated checks and generates exception reports. The checks run. The exceptions accumulate. The reports sit unread, or are reviewed by someone who acknowledges them and moves on.
Automated quality checks are inputs to a process, not the process itself. They require a human review loop with defined response thresholds:
- Exception rates above X trigger investigation
- Recurring exceptions trigger root cause analysis
- Systemic failures trigger escalation to data governance.
Without that loop, you are generating increasingly accurate data about your quality problems and doing nothing with it.
Review the volume of unaddressed exceptions in your current quality tooling. If it is large, the framework has a process gap between detection and resolution.
4. Quality Standards Were Set Once and Never Revisited
Data quality standards should reflect use case requirements. A field that feeds a regulatory report has different accuracy requirements than one that feeds an internal exploratory dashboard. The appropriate timeliness standard for a real-time pricing engine is different from that for a monthly financial summary.
Frameworks that define quality standards once, at implementation, tend to drift out of alignment with actual use cases as the business evolves. New data products get launched that inherit standards that weren't designed for them. Thresholds that were appropriate for historical query volumes become inadequate when data starts feeding machine learning models.
Quality standards should be reviewed whenever a dataset acquires a significant new use case. They should also be reviewed periodically — at minimum annually — to confirm that they still reflect how the data is being used and what failures would actually cost.
5. Downstream Systems Have Their Own Duplicate Quality Checks
When the analytics team is running their own validation scripts before using data, when the reporting team has a set of "sanity checks" they run on every extract, when multiple teams have independently built processes to detect the same categories of problems — that is a signal that the central quality framework is not trusted.
Each of those independent checks represents rework. It also represents risk, because teams are applying different standards and definitions, which means the same underlying data can produce different results depending on who processed it. When those results eventually collide — in a presentation, a regulatory filing, a cross-functional analysis — it creates more damage than the original quality problem would have.
The duplication problem is usually a trust problem. Rebuilding trust requires transparency about quality status, consistent resolution of reported issues, and reliable communication when something has changed.
6. Data Quality Is Treated as a Data Team Problem
In healthy data quality programs, business stakeholders are active participants. They define what good looks like for their use cases. They report issues through a clear channel and receive timely responses. They participate in root cause analysis for significant failures. They understand that some quality problems originate in the business processes that generate the data, not in the data systems that store it.
When business stakeholders have opted out — when they view data quality as a technical problem they don't need to engage with — the framework is missing a critical input. The data team cannot fully understand the business impact of quality failures without that input. And quality problems that originate in business processes, like inconsistent data entry practices, cannot be fixed from the data layer alone.
Reconnecting business stakeholders requires demonstrating that their participation produces better outcomes for them — faster resolution of the issues that affect their work, data they can trust without running their own checks.
7. There Is No Feedback Loop From Production Issues to Prevention
The most mature quality frameworks treat production issues as learning opportunities. When a quality failure causes a business problem, the response includes not just remediation but root cause analysis and process improvement. The knowledge gained from the failure is used to build better checks, close governance gaps, and prevent the category of problem from recurring.
Organizations without this feedback loop fix the same types of problems repeatedly. The fixes are reactive, the root causes persist, and the true cost of poor data quality — in engineering time, business disruption, and eroded trust — compounds over time.
Building the feedback loop requires designating someone responsible for it — typically within the data governance function — and creating a regular cadence for reviewing quality incidents, documenting patterns, and translating those patterns into framework improvements.
Final Thoughts
If several of these signs are present, the problem is not technical. It is structural. No amount of tooling investment will fix a framework that lacks clear ownership, genuine accountability, and regular review processes.
The technical tools serve the framework. The framework requires human commitment to function. Even one small improvement that is actually implemented is worth more than a comprehensive framework that exists on paper.
