Data health check
We export customers, suppliers and items and count duplicates, empty mandatory fields, inconsistent spelling and mixed units.
When the sales dashboard and the finance team quote different figures, the software is rarely to blame. More often the same customer sits in e-conomic or Business Central three times, one item number is counted in pieces by some people and in cartons by others, and nobody knows who may edit the records. We clean this up and put guard rails in place so the mess does not creep back.
Tooling is the smaller part of the job. What really matters is a named person responsible for each list and written rules that a new starter can follow on day one.
We export customers, suppliers and items and count duplicates, empty mandatory fields, inconsistent spelling and mixed units.
Business customers and suppliers are matched on their CVR number, and EAN numbers for public sector buyers are verified so NemHandel invoices are not rejected.
Duplicates are combined in a way that keeps invoices, orders and ledger entries linked to the surviving record.
How names are written, which fields are compulsory and who is actually allowed to add a new record.
Dormant private customers kept beyond your retention period are flagged, so you can delete or anonymise them in line with your own policy.
A short summary of new duplicates, blank fields and inactive records, so data quality can be tracked over time.
The assessment takes one to two weeks. The clean-up depends on volume and is usually spread over a couple of months so daily work carries on undisturbed.
Through secure remote access or a data export we count faulty records and show where they distort reporting the most.
We draft them with the people who create records every day, not only with management.
Active customers and items come first, archived ones last. The data owner signs off every merge.
Validation on entry plus the monthly report keep quality from sliding back.
Cleaning without rules is wasted effort. Six months later the duplicates are back, because a salesperson could not find the existing record and simply made another. That is why rules and entry checks come first and the merging comes afterwards.
Creating a new customer is quicker than searching for the old one. An extra »ApS«, a hyphen or an outdated address is enough for the search to miss it. The cure is better search, a CVR lookup and a simple rule: search before you create.
Software can spot likely pairs and saves many hours doing so. The merge itself should still be approved by a person. Two companies with almost identical names are not always the same business, and a wrong merge mixes up documents that are hard to separate again.
Rarely, for a company with fewer than 250 employees. Most get a long way with rules, validation in e-conomic or Business Central and a recurring report. A standalone master data platform starts to pay off when many systems must share the same records in real time.
Tell us which reports do not add up and which lists look untidy. We begin by measuring how large the problem really is.
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