Why Your Company’s Data Is Always Wrong (And How to Fix It at the Source)
Most bad data starts in business processes not your warehouse. Learn why expensive tools fail and how to fix data quality at the source.
Ali Z.
𝄪
CEO @ aztela
Table of Contents
You just spent $200k on a data quality tool. Six months later, your dashboards are still wrong.
I see this every month:
A company buys a shiny “data observability” platform.
Engineers spend weeks running cleansing scripts.
Executives sit through another round of dashboard reviews.
And still — the CFO says: “I don’t trust these numbers.”
By then, the tool gets blamed. The engineers get frustrated. And the leadership team quietly drifts back to Excel.
The real problem?
You don’t have a data quality problem.
You have a business process problem.
Bad Data Is Born Upstream
Dashboards don’t break themselves. Pipelines don’t spontaneously corrupt.
Bad data starts where people and processes collide:
A sales rep skips CRM fields to close a deal faster.
Finance adjusts revenue in Excel before the board meeting.
Marketing defines “lead” differently than Sales.
Your data team is mopping the floor while the pipe is still leaking. No $200k tool fixes that.
(If this sounds like your company, see our guide: Why BI Dashboards Fail Adoption)
Why Expensive Tools Don’t Save You
Data quality software isn’t useless. But it’s like buying a fire alarm for a house without walls.
Here’s why tools won’t save you:
They don’t fix inputs. If sales isn’t incentivized to complete CRM records, no script can fill the gaps.
They don’t fix definitions. If Finance and Sales can’t agree on “revenue,” no monitoring tool will reconcile it.
They don’t fix ownership. If no one is accountable, issues just get passed back and forth.
This isn’t a tech issue. It’s an operating model issue.
(For more on ownership and reporting, read: Where Should the Data Team Report?)
The Playbook: How to Actually Fix Bad Data
Step 1: Go to the Source
Don’t start with dashboards. Sit with the people entering data — sales, finance, ops. Watch their workflows. You’ll identify 80% of root causes in a single afternoon.
Business Impact: Faster fixes, fewer downstream escalations, millions saved in rework.
Step 2: Assign Owners and Stewards
Sales owns sales data. Finance owns financial data. Pair them with data stewards to enforce definitions and connect business to IT.
Business Impact: Accountability cuts data firefighting time in half.
(Related: The ROI of Data Governance)
Step 3: Change Incentives
Policies don’t change behavior — incentives do.
Tie part of sales comp to CRM completeness.
Ban Excel “fixes” in board prep.
Make data accuracy a measurable KPI, not an IT aspiration.
Business Impact: Aligns executive trust with frontline behavior.
Step 4: Profile and Prioritize
Don’t boil the ocean. Profile datasets, identify the riskiest ones, and fix in ROI order: financial reporting, compliance, revenue-critical.
Business Impact: Avoids wasted months fixing data nobody uses.
Your data isn’t wrong because of a warehouse problem. It’s wrong because of messy processes, broken incentives, and no ownership.
No $200k tool will fix that.
If you’re ready to stop firefighting and finally get executives to trust the numbers, it starts with fixing data at the source.
Schedule a Data Strategy Assessment today and see how to rebuild trust in 90 days?
