Why Data Quality Projects Fail (And How to Actually Fix Them)

80% of Data Quality Projects Fail Within Six Months

Here’s the pattern I see:

• 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, not business problems.

Why Data Quality Projects Fail

1. They fix symptoms, not causes.

   Bad data doesn’t start in your warehouse. It starts in the real world: sales reps skipping fields, managers “fixing” numbers in Excel, inconsistent definitions between teams.

2. 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.

3. 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.

4. They try to fix everything.

   When you try to clean every dataset, six months later nothing has changed.

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 (audit exposure), compliance data (fines), revenue-critical pipelines (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.

The Blunt Truth

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 and executives will keep defaulting to Excel.

Don’t Miss This

If your data quality project is stalling, don’t throw more tools at it.

Book a 30-minute Data Quality Reset Call and we’ll show you how to fix the root causes, build trust, and finally get executives relying on your dashboards.