--- title: "What Is Data Cleansing? Steps and Examples | Ant Assist" description: "What is data cleansing? A plain English definition, why dirty data costs UK firms, a five-step process with examples and when to automate or outsource." url: "https://ant-assist.com/resources/what-is-data-cleansing/" --- RESOURCES · ADMIN · DATA CLEANSING # What is data cleansing? Data cleansing is checking records against an agreed standard and correcting, merging or removing what falls short. Done regularly, it gives the business one reliable version of the truth. Tools our VAs use for this - Updated 23 October 2026 - 12 min read - By Ant Assist [Calculate your savings](https://ant-assist.com/calculator/) · [Book a free 15-min call](https://ant-assist.com/book/) In short Data cleansing means checking a set of records against an agreed standard and correcting, merging or removing whatever falls short, so the data is accurate, complete, consistent and free of duplicates. For a small business the job usually comes down to five steps: audit the data, standardise formats, remove duplicates, fix or flag missing and wrong values, then check the result and record what changed. Done regularly, data cleansing keeps your reports, mailings and customer contact working from one reliable version of the truth. On this page Admin Virtual Assistant [Book a free 15-minute discovery call](https://ant-assist.com/book/) On this page [Book a call](https://ant-assist.com/book/) · [Calculate your savings](https://ant-assist.com/calculator/) Part 2 ## Definition and meaning ### A plain English definition Every business collects data in more than one way: a website form, an old spreadsheet, an export from your accounts software, a list from a trade show. Each source has its own habits. One writes "Ltd", another "Limited"; one stores dates day first and another month first. Over time the records drift apart, and the same customer appears three times with three different phone numbers. Data cleansing brings those records back into line. You decide what a correct record looks like, then go through the data and make every record match: correcting what you can, merging what belongs together, and flagging what you cannot verify. If you would rather hand the work over, see [outsourced admin support](https://ant-assist.com/services/admin/). ### Cleansing, cleaning and scrubbing Yes, for practical purposes. "Data cleansing" is the more common term in UK business use, "data cleaning" is common in analysis and research, and "data scrubbing" usually means the same work with an emphasis on removing bad records. Deduplication is one part of cleansing: it deals with duplicates only, while cleansing also covers formats, gaps and errors. ### What counts as clean data There is a recognised way to describe it. The [Government Data Quality Framework](https://www.gov.uk/government/publications/the-government-data-quality-framework/the-government-data-quality-framework) published on GOV.UK sets out data quality dimensions, including completeness, which it describes as the degree to which the records you expect are present. In plain terms, we suggest six tests for clean data: - **Accurate:** it matches reality, so the phone number reaches the person. - **Complete:** the fields you need are filled in. - **Unique:** each customer, supplier or product appears once. - **Consistent:** the same thing is written the same way everywhere. - **Valid:** each value is in the right format, such as a real date or a properly formed postcode. - **Timely:** it is up to date enough for the job you are using it for. Costs ## Why data cleansing is important ### Duplicates, gaps and inconsistent formats Most dirty data falls into a few familiar types. Duplicates are the same record entered twice, often with small differences. Gaps are missing emails, missing postcodes or blank job titles. Inconsistent formats are mixed date styles, phone numbers with and without spaces, and names in capitals in one row and lower case in the next. Then there are plain errors: typing mistakes, values in the wrong column, and details that were right once and have since changed. ### Effect on reporting, sales and customer contact The cost shows up in ordinary work. A sales report counts the same customer three times. A mailing reaches one person twice and others not at all. A salesperson calls a number that stopped working last year. An import fails because one column mixes text and dates. Nobody trusts the master copy, so people keep their own, and the problem grows. There is no reliable published figure for what dirty data costs a small UK business, so the useful measure is your own: count the duplicates and blanks in your main list, and note how often a mailing bounces or an import fails. ### The UK GDPR accuracy principle If your records include people, such as customers, contacts or staff, the law cares about accuracy too. The ICO's guidance on the [accuracy principle](https://ico.org.uk/for-organisations/uk-gdpr-guidance-and-resources/data-protection-principles/a-guide-to-the-data-protection-principles/accuracy/) explains that, under Article 5(1)(d) of the UK GDPR, personal data must be accurate and, where necessary, kept up to date, and that every reasonable step must be taken to make sure inaccurate personal data is erased or corrected without delay. Regular cleansing is one of the practical ways a small business shows it takes that seriously. This is general information, not legal advice. [See the numbers for your own hours](https://ant-assist.com/calculator/) Part 4 ## The data cleansing process, step by step The five steps of a data cleanse, in the order that avoids rework. The data cleansing process, step by step. ### 1. Audit the data and decide what good looks like Copy the data before you touch it. Then look at what you have: how many records, which fields, where they came from. Write down the standard each field should meet, for example "UK phone numbers with no spaces", "postcodes in capitals with one space", "company names exactly as on Companies House". This short list is your rulebook, and it is the step most people skip. ### 2. Standardise formats Bring every field into the agreed format: trim stray spaces, set consistent capitals, put dates in one style, split full names into first and last where you need them, and use one spelling for repeated values such as county or job title. Standardising comes before deduplication because two records only look identical once they are written the same way. ### 3. Remove duplicates Decide what makes two records the same. An exact match on email address is a strong signal; a match on name alone is weak. Find the duplicates, choose which record to keep (usually the most complete or most recent), merge any useful details from the others into it, and only then remove the rest. ### 4. Fix or flag missing and wrong values Fill gaps only from a reliable source, such as the customer's own form, your accounts system or a public register. Where you cannot verify a value, flag it for follow-up and leave it blank. A guessed value is worse than a blank, because it looks trustworthy and is not. ### 5. Validate and document what changed Check the result against your rulebook: spot-check a sample of records, recount the totals, and test the data in the place it will be used, such as an import or a mailing. Then write a short note of what you changed, how many records were merged or removed, and what is still flagged. The next cleanse starts from that note. Part 5 ## Examples of data cleansing Examples of data cleansing include trimming and capitalising names, formatting postcodes and phone numbers consistently, merging duplicate records and converting mixed date styles into one. **Example: a small customer list before and after.** The rows below are invented to show the process. Examples of data cleansing | Before | After | |---|---| | **" jane SMITH ", jane.smith@example.com, 07700900123** | Jane Smith, jane.smith@example.com, 07700 900123 | | **"Jane Smith", JANE.SMITH@EXAMPLE.COM, (blank)** | Merged into the record above | | **"Acme ltd.", sw1a1aa** | Acme Ltd, SW1A 1AA | | **Order date "3rd March 25"** | 03/03/2025 | A small customer list before and after a cleanse. The rows are invented. ### Names, addresses and phone numbers Names are trimmed and given consistent capitals, with care for names such as McDonald that tools get wrong. Postcodes go into capitals with a single space, phone numbers get one format, and company names follow one rule for "Ltd". ### Dates and currencies Mixed date styles are converted into one, with particular care for dates that could be read either way, such as 04/05, where you need to know whether the source was UK or US style. Currency columns are checked for symbols stored as text, so totals add up. ### CRM and spreadsheet records In a CRM, cleansing usually means merging duplicate contacts and companies, archiving records with no activity, and filling required fields so reports and automations work. Our guide to [lead list building and CRM data](https://ant-assist.com/resources/lead-list-building-crm-data/) covers keeping sales data in order. In spreadsheets, it means the same steps done with formulas and built-in tools, which our step-by-step guide on [how to clean data in Excel](https://ant-assist.com/resources/how-to-clean-data-in-excel/) walks through. Clean data, kept clean Make data cleansing a regular routine A dedicated data entry VA audits, standardises and de-duplicates your records to your rulebook, then logs what changed. [Explore data entry support](https://ant-assist.com/services/admin/data-entry/) · [See your savings](https://ant-assist.com/calculator/) Skills and tools ## Data cleansing techniques and tools ### Manual cleansing For a few hundred records, a careful person with a spreadsheet does an excellent job, because they notice what rules miss: the customer who moved, the company that changed its name. The limits are time and consistency, so work from a written rulebook. ### Automated tools Spreadsheet tools, the deduplication features in most CRMs and dedicated data quality software apply your rules to thousands of records at once. They are fast and repeatable, and they only know the rules you give them. Start with the features in your CRM or spreadsheet before buying anything. ### Data cleansing in ETL Usually, yes. ETL stands for extract, transform and load, the process of moving data from one system into another, such as a data warehouse. Cleansing normally happens in the transform stage, so bad data is fixed before it lands in the new system. For most small businesses the same idea applies on a smaller scale: clean a list before you import it, never after. Part 7 ## Keeping data clean over time ### Entry rules and validation The cheapest cleanse is the one you never need. Use drop-down lists for repeated values, make key fields required, and check formats such as email addresses and postcodes at the point of entry. ### Cleansing frequency Match the frequency to how fast the data changes. A busy sales list may need a monthly check; a supplier list, a yearly one. Always cleanse before a big mailing, a migration or an import: the five steps take far less time on data that was clean last month. ### Data ownership Give each data set a named owner who keeps the rulebook and signs off each cleanse. Without one, everyone edits and nobody is responsible. Part 8 ## In-house, automated or outsourced cleansing ### When to automate Automate when the same cleanse runs again and again on data that arrives in the same shape, such as a monthly export. Keep a person in the loop for judgement calls: which of two records is right, or whether an odd value is a mistake. Most small businesses end up with both. For in-house pay benchmarks, see our guide to [executive assistant salary UK](https://ant-assist.com/resources/executive-assistant-salary-uk/). ### What outsourcing covers An outsourced cleanse usually covers the full process above: an audit, a written rulebook agreed with you, standardising, deduplication, gap-filling from sources you approve, and a report of what changed. There is no reliable published price per record, and quotes depend on the size and state of the data, the number of sources, how much needs checking by hand and whether the work is a one-off or ongoing. Ask any provider for the rulebook they will work to, how they decide which duplicate to keep, and how they will handle access to your systems. Ongoing record keeping of this kind is what our [outsourced data entry](https://ant-assist.com/services/admin/data-entry/) service covers. Part 9 ## Outsourcing data cleansing to a virtual assistant ### Staffing and location Ant Assist provides a dedicated virtual assistant, never shared, working full time from our own managed office in Cairo, recruited, managed and supervised there and UK-managed. Your VA works to your rulebook during UK business hours, 9am to 5pm Monday to Friday, from 20 hours a week up to a full 40-hour VA, so cleansing becomes a regular routine alongside your other data entry. Pricing starts at the £5 per hour introductory rate; put your own hours into the [calculator](https://ant-assist.com/calculator/) to see the comparison, and see [whether a VA can help with research and data entry](https://ant-assist.com/faqs/can-a-va-help-with-research-and-data-entry/) for the short answer. For the wider list of tasks, read [what does a virtual assistant do](https://ant-assist.com/resources/what-does-a-virtual-assistant-do/). ### Security and data access Cleansing means giving someone access to customer records, so access matters. We work under NDAs and data-processing agreements, give your VA access through password managers, and keep every login revocable by you. Our answer on [how lead data is kept secure](https://ant-assist.com/faqs/how-is-lead-data-kept-secure/) sets out the detail. For regular upkeep of customer records, a [virtual admin assistant](https://ant-assist.com/services/admin/) works to your rulebook. Rated 5.0 on Google. · Trusted by our clients FAQ ## Data cleansing FAQs ### What is the difference between data cleansing and data validation? Validation checks whether data meets a rule, such as whether an email address is properly formed. Cleansing goes further and corrects, merges or removes the records that fail. Validation at the point of entry reduces how much cleansing you need later. ### Which tool is best for data cleaning? The best tool is the one that fits where your data lives and how often you clean it. For small lists, a spreadsheet and a clear rulebook are enough. For large or repeated jobs, use the deduplication features in your CRM or a dedicated data quality tool, and test it on a copy first. ### Can you clean data without losing information? Yes, if you work on a copy and merge before you delete. When two records describe the same customer, move any useful detail into the record you keep, then remove the other. Keep the original file until the cleaned version has been checked and used successfully. ### Who should do data cleansing in a small business? Someone careful who understands what the data is for, working to a written rulebook that the data's owner has agreed. That can be a member of your team or a virtual assistant. What matters most is that the same person, or the same rules, are used every time. Didn't find your answer? Browse [more common questions](https://ant-assist.com/help/) in the Help Center. Get Started One clean version of your data. A dedicated data entry VA, from £5 per hour. Book a call. [Book a free 15-min call](https://ant-assist.com/book/) · [Calculate your savings](https://ant-assist.com/calculator/) Discovery call · Role brief · VA matched · Onboarded into your tools