Data Quality
The same customer, four different ways
Master data clean-up is reconciling your customer, supplier, item and site records to one version — and then putting standing checks in place so the mess does not quietly rebuild itself over the next three years.
Symptoms
If people don’t trust the data, they stop using it
They start keeping their own version instead, which is how one business ends up with four answers to the same question and a month-end reconciliation nobody can explain.
Two reports on the same question disagree
The same supplier appears four times
Three sites classify a product three ways
People keep their own spreadsheet instead
Month end includes a manual reconciliation
A migration slipped because of the data
An AI pilot returned answers nobody believed
What gets reconciled
Four masters. Everything else hangs off them
Almost every reporting argument in an operating business traces back to one of these four being inconsistent.
Customer master
One customer, one record, one credit limit — with the old spellings kept as aliases so nobody loses their history.
Supplier master
Duplicates merged, ABNs verified, payment terms reconciled to what was actually agreed.
Item and product master
One classification across every site, so a group number means the same thing everywhere.
Site and location master
Consistent addresses, codes and hierarchies, which is what makes site comparison possible at all.
Keeping it clean
A clean-up without standing checks is a temporary result
Master data degrades because people are doing their jobs quickly, not because they are careless. The answer is checks that catch drift while it is one record rather than four hundred.
Why this comes before AI
An AI assistant answering from inconsistent master data will give you a confident wrong answer. This is the work that has to happen first, and it is why HeyMi and Data Mi are better together even though neither needs the other.
Your data
Handled carefully
What you get at the end
Reconciled masters, the rules that produced them, the exception list, and the standing checks — documented, and running in your environment.
Questions
Data quality, answered
Data quality is whether the information in your systems is accurate, consistent, complete and trusted enough for people to act on. In practice the symptoms are recognisable: two reports disagree, the same customer exists four times, and people keep private spreadsheets because they do not believe the system.
By profiling the data and matching on the attributes that actually identify a business — name, ABN, address, contact details — with rules your people agree to. Likely matches are merged automatically. Uncertain matches are escalated to somebody in your business, because a wrong merge is worse than a duplicate.
Because AI amplifies it. A report with duplicate customers produces a number a person can sense-check. An AI answer built on the same data produces a confident sentence with no visible working. If people do not trust the data, they stop using what is built on it.
No. Data quality work is worth doing where a decision depends on it. Fixing the customer master because pricing decisions run through it is worth the effort. Fixing a field nobody reads is not.
Talk to us
How many ways does your biggest customer appear?
Most people guess two. It is usually four or five once you count the systems nobody mentions.