Bad data is expensive in ways that are hard to see until something goes wrong. A marketing campaign sending to outdated email addresses. A financial report that does not reconcile because the underlying records are inconsistent. A customer service team working from records where the same customer appears three times with different spellings. Data cleansing is the process of fixing this.
What data cleansing actually involves
Data cleansing is not a single process. It is a collection of specific tasks applied to specific problems in your data. Deduplication - identifying and merging records for the same entity that appear multiple times. Standardisation - ensuring the same information is formatted consistently across all records. Validation - checking that data values are plausible and complete. Enrichment - filling gaps in existing records from secondary sources. The right combination depends on what problems your data actually has.
How to diagnose your data problems
Before commissioning a data cleanse, you need to understand what is wrong. The quickest diagnostic is to pull a sample of 100 records and review them manually. How many duplicates do you find? What proportion have missing fields? How many email addresses bounce when you test them? How many phone numbers are in different formats? This gives you a realistic picture of the scale of the problem and informs a much better brief for any external work.
What it costs to get it done professionally
Data cleansing costs depend on the size of the dataset, the complexity of the problems, and whether it involves manual review or can be automated. For a contact database of 5,000 records with standard deduplication and validation, expect to pay between £500 and £2,000 depending on the provider. For complex financial data requiring manual reconciliation across multiple systems, the cost scales with the time involved.
When to do it yourself
Simple deduplication and standardisation of a well-structured dataset is something most businesses can do themselves with Power Query in Excel. Power Query has built-in functions for removing duplicates, standardising text case and formatting, and identifying missing values. For a dataset of up to 50,000 records with straightforward problems, a Power Query model built once can process the data in minutes and be rerun whenever new records are imported.
Making it stick after the cleanse
A data cleanse that is not followed by better data entry processes produces the same problems again within six months. Mandatory fields in your CRM. Dropdown selections instead of free text for standardised values. Import validation that checks new records against existing ones before they are added. These changes are the difference between a one-off fix and a sustained improvement.
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