Why Your ERP Data Is Wrong and How to Fix It

Almost every ERP we inherit has data quality problems. Some are obvious within the first hour of looking at the data. Most are not. Here is how to find them and fix them before they corrupt your reports.

The most common problems

Duplicate records are the most prevalent issue across all ERP implementations. Customers entered twice under slightly different names. Parts with multiple item numbers. Suppliers with variations in their name or address fields. Duplicates create double-counting in reports and operational confusion in fulfillment.

Orphaned records exist in detail tables without corresponding parent records. Line items without headers. Costs without transactions. These create subtle errors in reports that aggregate across both tables.

Inconsistent categorization happens when the same field is used differently by different users or different business units. If your item category field contains 40 different values that should map to 8, your category-based analysis is meaningless.

Stale data is information that was accurate at some point and has never been updated. Standard costs that have not been revised in three years. Customer contacts who left the company two years ago. Vendor payment terms that changed but were not updated in the system.

The audit process

Start with your most-used reports and work backward to the tables they query. For each table, run frequency counts on categorical fields - you want to see the distribution of values and identify outliers that should not exist.

Cross-reference related tables to find orphaned records. Check date fields for implausible values. Identify records with required fields left blank.

The fix

Data cleanup is a one-time effort. Data governance is the ongoing process that prevents the same problems from returning. Both are necessary. Cleaning without governance recreates the problem. Governance without cleaning is working on top of a corrupted foundation.

If you want help auditing your ERP data quality before a migration or a new BI implementation, reach out. Data quality assessment is often the most valuable work we do before any technical project begins.