Your reports say everything’s fine — but your gut says otherwise.
That uneasy feeling isn’t paranoia; it’s your data quietly eroding trust, one unnoticed error at a time. Duplicate records, subtle logic issues, or unclear ownership — small problems quietly become big headaches.
We’ve seen these problems play out again and again — and this month we’re sharing what our experience has taught us when it comes to spotting the issues early and fixing them quickly.
Hidden issues like duplicate records, subtle logic errors, and incomplete fields quietly accumulate, eventually sabotaging decisions.
The fix: Proactively profile and assess your critical datasets to find and fix hidden quality issues early, before they affect your business.
“‘We don’t have data quality problems’ is one of the most dangerous assumptions teams make — by the time issues become obvious, trust has already been damaged.”
— Marin Georgiev, Data Management Consultant
#2. Treating data quality standards as static, not evolving
When your business, tech, and processes change — so does your data. Data quality standards quickly become obsolete, creating gaps between what you’re measuring and the quality you need.
The fix: Regularly revisit your data standards and embed continuous improvement into your daily workflows.
“Believing data quality can be tackled once and done is unrealistic. It requires constant iteration, refinement, and attention.”
— Joshua Johnston, Data Strategy &
Data Management Consultant
#3. Believing data quality falls on IT
IT teams know systems — but they can’t define meaningful business rules in isolation. Without clear business input, technical fixes miss critical nuances and won’t address underlying quality issues.
The fix: Clearly define roles for business data stewards who actively participate in setting and reviewing data quality standards.
"Without business context, data quality solutions miss the mark. Business involvement is critical — not optional.”
— Jenna O'Jea, Business Intelligence &
Data Strategy Consultant
#4. Relying too heavily on tools to fix data quality problems
Powerful tools automate processes — but if those processes aren’t clearly defined or governed, automation just amplifies existing mistakes, spreading bad data faster.
The fix: Build your governance program first, clarifying accountability, roles, and standards. Then use tools to reinforce and sustain the quality framework you’ve established.
“Most clients think they need a fancy tool to fix data quality. What they actually need is better collaboration and clear ownership across teams.”
— Ben Rondou, Data Governance &
Analytics Consultant
Too many teams treat data quality like a cleanup job. But without embedded governance in your data strategy — clear roles, validation, and monitoring — you’re just cleaning up the same mess over and over.
