Data Quality: How to Clean Up Your Company’s Data

Data quality: how to clean your company’s data step by step

Before any new system, KPI dashboard or use of AI, you need data you can trust. This is a practical guide to cleaning the data you already have and setting rules that keep it clean.

Data and AI 5 min read

By Nour Aldein Tleti. Updated 7 October 2026.

How do you clean your company’s data?

Start with the most used data, such as customers, items and invoices, and check it for duplicates, missing fields and inconsistent formats. Correct it in batches after saving an original copy. Then set entry rules that stop the errors recurring, and assign someone to monitor quality regularly.

The most common quality problems

Duplicates: The same customer is recorded under two names or with a phone number in two formats, so they appear as two customers in reports.
Inconsistent names: The same item is written in several ways, or in Arabic one time and English the next, so its sales are split up.
Missing fields: Invoices with no date, customers with no area and items with no unit of measure.
Mixed currencies: Amounts in Syrian pounds and others in US dollars in the same column, with no indication of the currency.
Outdated data: Cost prices that haven’t been updated for a long time, which produce profits that aren’t real.

Rules that stop errors coming back

Pick lists instead of free text: Area, item and currency are chosen from a list, not typed by hand.
Mandatory fields: An invoice can’t be saved without a date, a customer and a currency.
One phone number format: Phone numbers are stored in a single international format, which also makes linking with WhatsApp easier.
Duplicate alerts: The system warns you if a customer is entered with a phone number that already exists.
Training on entry rules: A short explanation to staff of why each rule exists makes them follow it instead of working around it.

Signs your data has a quality problem

Two reports, two answers: Accounting gives one figure and sales gives another for the same period.
System stock doesn’t match the shelf: A recurring gap between the stocktake and the system points to incomplete or wrong entries.
Messages that don’t arrive: Wrong or incomplete phone numbers waste WhatsApp reminders and marketing messages.
Parallel notebooks: When staff keep their own notebooks alongside the system, the official data is usually unreliable.

A practical cleanup plan

01
Take a backup
Save a complete copy of the data in a separate place before making any change.
02
Set priorities
Start with the tables that invoices and daily reports depend on.
03
Inspect and count the problems
Pull lists of duplicates, empty fields and differing formats to see how much work there is.
04
Correct in batches
Handle each type of problem separately, and check a sample after each batch.
05
Apply entry rules
Adjust the system or the files so the same errors can’t come back.
06
Review monthly
A simple report that shows new duplicates and missing fields every month.

Data problems, their impact and the fix

Problem	Impact on management	Fix
Duplicate customers	An inflated customer count and scattered collection	Merge records and standardise phone numbers
Items under different names	Item sales look lower than they really are	A fixed code for each item and one unified list
Mixed currencies	Misleading profits and reports	A mandatory currency field and an exchange rate for every transaction
Outdated cost	Wrong pricing and an unreal margin	Update the cost with every purchase

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Frequently asked questions

How long does data cleanup take?

It depends on how much data you have and how many problems it contains. Inspect first to see the size of the job, then split it into stages rather than trying to finish it in one go.

Should I clean data before moving to a new system or after?

Before, as far as possible. Moving unclean data into a new system carries the problems with it and weakens staff trust in the system.

Who should do the cleanup?

The staff who know the customers and items, helped by a technical person who prepares the lists and tools.

Can AI be used for cleanup?

It can help suggest duplicates and standardise formats, but the final decision needs human review, especially with similar Arabic names.

What about old paper records?

Don’t enter the whole archive at once. Start with what you need for daily operations and leave the rest for gradual archiving.