🎯 Situation

A client's monthly revenue report showed a $240,000 discrepancy between what sales reported and what finance booked. Both teams were confident in their numbers. Three weeks of investigation later, the root cause: a customer account had been entered with two different IDs in the CRM — one used by the sales team, one created automatically by the billing system. Nobody owned the customer master data. Nobody noticed the duplicate for eight months.

👉 Data quality isn't a technology problem. It's an ownership problem. The question 'who is responsible for keeping this data accurate?' is almost never answered clearly in most companies — and every data quality issue traces back to that gap.

⚠️ Challenge

🔋 The 4 most common data quality failures

  • Duplicate records — same customer, supplier, or product in the system twice with slightly different names
  • Stale data — fields that were accurate when entered and never updated (wrong address, old email, changed price)
  • Missing values — mandatory fields left blank because the entry form didn't enforce them
  • Inconsistent formats — dates as DD/MM/YYYY in one system, MM-DD-YYYY in another; revenue in different currencies

👥 What 'ownership' actually means

  • One person or team is accountable for each critical data domain (customer, product, supplier)
  • They define what 'correct' looks like — the business rules for valid data
  • They receive alerts when data quality metrics drop below thresholds
  • They have the authority to enforce entry standards and reject bad data
  • They review a data quality dashboard weekly — not annually

🔍 Analysis

A data ownership model assigns a 'data steward' to each critical data domain. This isn't an IT role — it's a business role. The data steward for customer data is usually someone in sales operations or customer success. For product data, it's usually product management or operations. For financial data, it's finance.

The steward doesn't enter every record themselves. They define the standards, monitor compliance, and resolve disputes. When two teams disagree about a customer's revenue, the customer data steward is the tiebreaker.

The fastest way to implement data ownership is to start with one domain — the one that causes the most pain. Customer master data causes the most arguments in most companies. Start there. Assign an owner. Define 'valid customer record' in writing. Build a Power BI dashboard showing duplicate rate, completeness rate, and last-updated date per record. That's your data quality scoreboard.

✓️ Best Practice

The 5-step data quality framework:

  • Define the critical data fields for each domain — not all fields matter equally. A customer record with a wrong phone number is bad. A customer record with a wrong billing address is a revenue problem.
  • Assign a steward for each domain — one person, with a name and a responsibility documented somewhere. Not a committee.
  • Define 'valid' for each field — what does a valid date look like? A valid customer ID? A valid product code? Write it down.
  • Build a quality score in SQL — percentage of records meeting each rule. Connect it to Power BI. Review weekly.
  • Set threshold alerts — if duplicate rate exceeds 2% or completeness drops below 95%, someone gets a notification the same day.

💡 Summary

Data quality is the unsexy prerequisite to everything else. Clean KPI definitions, automated pipelines, and beautiful dashboards are all worthless if the underlying data is inconsistent, duplicated, or missing. Ownership is the fix — not technology. Assign a steward. Define valid. Measure weekly.

👉 No tool fixes bad data ownership.

Assign a steward. Define valid. Measure weekly. That's the entire framework.