Why 80% of Data Quality Projects Fail Within Six Months
Most data quality projects fail because they treat a business problem like a technical one. Learn why six-figure tools don’t work and how to fix data quality at the source.
Ali Z.
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CEO @ aztela
Table of Contents
A company spends six figures on a “data quality tool.”
Engineers run cleansing scripts every week.
Teams hold weekly data quality dashboard reviews.
Six months later?
The dashboards are still wrong.
The ML models are still ineffective.
The CFO still doesn’t trust the numbers.
The problem isn’t your warehouse.
The problem isn’t even the tool.
The problem is that data quality projects are treated as technical problems instead of business problems.
Why Data Quality Projects Fail
They fix symptoms, not causes.
Bad data doesn’t start in your warehouse. It starts in the real world: sales reps skipping CRM fields, managers “fixing” numbers in Excel, and inconsistent definitions across teams.
(See also: Why BI Dashboards Fail Adoption)
They live in IT, not the business.
If Finance doesn’t own finance data, or Sales doesn’t own pipeline accuracy, you’ll never get accountability.
(Related read: Where Should the Data Team Report?)
They ignore incentives.
Policies don’t change behavior. Incentives do. If sales comp isn’t tied to CRM accuracy, no tool will ever fix it.
They try to fix everything.
When you try to clean every dataset, six months later nothing has changed.
(Instead, see: The ROI of Data Governance)
The Playbook: How to Actually Fix Data Quality
Step 1: Start with Discovery and Profiling
Interview key stakeholders (Finance, Sales, Ops) to identify which datasets drive business outcomes. Then profile those datasets for completeness, duplication, and accuracy. Focus where the pain is visible.
Step 2: Assign Ownership and Stewardship
Business leaders own accuracy. Data stewards enforce governance, monitor lineage, and partner with IT. Without both, accountability collapses.
Step 3: Align Incentives
Tie 5% of sales rep commission to CRM completeness. Ban Excel “fixes” in board meetings. Make accurate data a business KPI.
Step 4: Prioritize High-Impact Data
Focus on datasets that tie directly to ROI or risk:
Financial reporting → reduces audit exposure.
Compliance data → prevents regulatory fines.
Revenue-critical pipelines → accelerates growth.
Step 5: Make It Iterative
Don’t launch a two-year governance project. Start small, prove value, and expand. Great data quality programs evolve — they aren’t one-off cleanups.
Your data quality project isn’t failing because you picked the wrong tool.
It’s failing because you treated a business process problem like a technical project.
Until you fix ownership, incentives, and focus on ROI-critical data, you’ll keep spending six figures while executives keep defaulting back to Excel.
If your data quality project is stalling, don’t throw more tools at it.
Schedule a Data Strategy Assessment and learn how to fix root causes, rebuild trust, and finally get executives relying on your dashboards.
