Figures that reconcile first time
Duplicate suppliers, spend without a cost centre and reports that disagree across systems. How to spot and fix the data quality problems that cost a finance team the most time.
The 5 most common problems
In finance, wrong data costs money directly: overpayments, longer closes and hours of reconciliation.
Duplicate supplier and customer master data
The same supplier set up twice, under another name or bank account. The risk of paying twice, and of bank-detail fraud, goes up.
How to spot itMatches on VAT number, IBAN and address, plus new suppliers and IBAN changes in the last quarter.
Misclassified entries and invoices
Account, cost centre or project left empty or wrongly chosen. Reports by area stop reflecting reality.
How to spot itShare of spend entries without a cost centre and amounts posted to catch-all accounts (“sundry”).
Figures that disagree across systems
The CRM, the ERP and the board report show three different sales figures, and time is lost arguing which one is right.
How to spot itMonthly reconciliation of totals across systems, with every difference explained.
Data arriving late for the close
Invoices, expenses or adjustments posted after the close that force reports already sent to be redone.
How to spot itEntries posted after the close date and the number of days the close takes.
Calculations nobody can rebuild
Chained spreadsheets, formulas overwritten by hand and nobody knows where each figure comes from. In an audit, that is a serious problem.
How to spot itCan you trace every figure in the report back to its source system?
Recognise any of these?
Measure your area with the free checklist: 20 checks with automatic scoring.
Download the checklist →Which indicators to track
Eight indicators for master data and the close. Thresholds are a starting point: adjust them to your company and review them every quarter.
| Indicator | How it is calculated | Starting threshold |
|---|---|---|
| Duplicate suppliers | Suppliers with a repeated VAT number or IBAN ÷ total | 0% |
| Complete supplier master | Active suppliers with VAT number, IBAN and payment terms ÷ active | above 98% |
| Verified IBAN changes | Changes confirmed through a second channel ÷ changes | 100% |
| Spend without cost centre | Spend without a cost centre ÷ total spend | below 2% |
| Amount in catch-all accounts | Amount in “sundry” accounts ÷ total spend | below 1% |
| Late entries | Entries posted after the close ÷ entries in the month | below 3% |
| Unexplained differences | Unexplained difference between ERP and reports ÷ total | 0% |
| Days to close | Working days from month end to approved close | going down |
Where to start, in three steps
Each step links to the guide and, where there is one, to the ready-made template.
Measure your master data
Export the supplier and customer masters and calculate duplicates and completeness. Start with suppliers: that is where an error costs money directly.
Rules and reconciliation
Write the rules for setting up suppliers (valid VAT number, IBAN confirmed through a second channel) and a monthly reconciliation between ERP and reports with every difference explained.
Traceability for audit
Document where every figure in the key reports comes from and who is accountable for each master data set.
Frequently asked questions
Who owns financial data quality?
The owner of each process: the supplier master usually sits with procurement or accounts payable, the customer master with sales administration and the chart of accounts with the controller. Finance validates, but every master data set needs an owner.
What does data quality have to do with fraud?
A common fraud against companies is a request, posing as a supplier, to change its bank account. One simple rule, confirming every IBAN change through a channel other than the one the request came from, closes that door.
How do you prove data quality to an auditor?
With written rules, stored periodic measurements, issues logged with their fix and the lineage of key figures: which system each piece of data comes from and what transformations it goes through.
Do you need a data quality tool?
First define rules and measure with what you already have (ERP, Excel or your reporting tool). A dedicated tool pays off once the rules are clear and the volume can no longer be checked by hand.
Where does your data governance stand?
The free assessment tells you what to prioritise in five minutes.