Fixing the Duplicate and Inconsistent Records Behind Your Numbers
Data cleaning and deduplication finds duplicate customers, vendors and items, standardises inconsistent codes and formats, and corrects broken records across your systems — with a review step before anything is merged.
Fixing What Is Already Wrong in Your Records
Most businesses running on more than one system, or on years of manual entry, end up with the same customer or item recorded under several codes, dates stored inconsistently, or categories left blank. This work finds those problems and corrects them, so anything grouped, counted or totalled by that record is accurate rather than approximate.
What Gets Cleaned
- Duplicate customer, vendor or item records created under different codes over time
- Inconsistent units and formats — quantities recorded in different measures, dates stored as text
- Blank or inconsistent categories that break any report grouped by them
- Obvious entry errors — impossible dates, negative quantities where they should not occur, mismatched totals
How Duplicates Are Found and Resolved
Fuzzy Matching
Records that are near-identical but not an exact match — a name spelled slightly differently, a phone number entered with different formatting — are flagged as likely duplicates rather than missed.
A Review Step Before Merging
Where a match is not certain, it goes to your team as an exception list rather than being merged automatically, since only someone who knows the business can confirm two records are really the same.
Rules Applied Consistently Afterward
Once your team's decisions on the exception list are recorded, the same logic is applied going forward, so the same kind of duplicate does not need deciding again each time.
One-Time Cleanup Versus an Ongoing Process
A single cleanup fixes what already exists in your data today. It does not stop a new duplicate from being created next week if nothing changes about how new records get added. Where duplicates keep recurring, the underlying process needs an ongoing check — covered under our master data management service — rather than repeated one-time cleanups.
Frequently asked questions
Will records be merged automatically without our input?
Only where a match is unambiguous. Anything less than certain is sent to your team as a list to confirm, so a cleanup does not accidentally combine two records that were genuinely different.
How far back can this go — years of old data?
Yes, this is commonly applied to years of accumulated records, not just recent ones. The volume of history affects the time the work takes, not whether it is possible.
What happens to transaction history attached to a duplicate record once it is merged?
It is reassigned to the surviving record, so historical sales, invoices or stock movements stay attached to the correct party rather than being lost or orphaned.
Can this be done without disrupting our live system while it is in use?
Yes — the review and matching work happens against a copy of your data, and changes are applied in a controlled batch once your team has confirmed the exception list.
Is this a one-time service or does it need repeating?
It can be either. A single cleanup addresses what exists today; if new duplicates keep appearing because nothing changed about how records are created, an ongoing process is the better fit.
What decides how long a cleanup takes?
Mainly the number of records involved and how much ambiguity there is in the matches — a dataset with clear, distinct names moves faster than one with heavy abbreviation and inconsistent spelling.
Tell us what you need.
Send a short brief and one of our engineers will come back to you — usually the same day.
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