What is Data Cleansing and Why Does it Matter?
Somewhere in your CRM platform right now, there are three versions of the same contact. Let’s go with a popular name here, John Smith. So, one is “John Smith”, one is “Jon Smith”, and another is “JOHN SMITH (OLD DO NOT USE)”
Bad data is happening with CRM teams everywhere.
The problem is that bad data doesn’t announce itself, it hides in plain sight. Your reports? Yep, they’re still running. Your dashboards? Yep, they’re still populating. Everything looks fine… until you realise your “active pipeline” includes contacts who left their company two years ago, leads that were never properly qualified, and deals that closed months ago still sitting at 90%.
Gartner (The source we swear by and always go to!) puts the average cost of poor data quality at $12.9 million a year, per organisation. We’ll give you a moment with that one. 🤯
It’s crazy, really, with all of that, a CRM/ESP platform grows messier with every passing quarter, quietly undermining the very processes it was built to support. Make it make sense… oh wait, we can in our article below!
Excel is not a database
It never was, and it never will be. Guys, we say this with love. If your “CRM” is a shared spreadsheet with colour-coded tabs, you are not managing customer relationships, you are just losing them more neatly. Excel gives you accidental overwrites and a panic attack every time you hit “sort.”
You can read more about how to get away from those pesky spreadsheets in our article:
> The Reasons to Move from Spreadsheets to a CRM
Data Cleansing – What it Means and Why It Matters
It isn’t just annoying admin, it’s leaking the revenue, the big bucks! The groundwork may seem unglamorous, but by doing it you are on the right track to a successful engagement and retention strategy later on.
| Data Cleansing Activity | What It Means | Why It Matters |
| Removing Duplicates | Eliminating repeated contact or company records | Prevents duplicate messaging and improves reporting accuracy |
| Correcting & Validating Data | Fixing errors, invalid entries, and inaccurate information | Ensures data is accurate, usable, and reliable |
| Filling in Missing Data | Adding missing key details like emails, job titles, or phone numbers | Improves targeting, segmentation, and customer insight |
| Standardising Data | Making formats and labels consistent across the CRM (e.g. countries, job titles, tags) | Improves automation, reporting, and segmentation |
| Archiving or Deleting Outdated Records | Removing inactive or irrelevant contacts | Keeps CRM clean and focused on active opportunities |
With this you’ll get a CRM platform that reflects reality. Contacts you can reach – tick. Segments that make sense – tick. Campaigns that land – tick, tick, tick!
You’ve probably heard of the Pareto Principle (the 80/20 rule.) When it comes to data work, roughly 80% of your time goes into finding, cleaning, and organising it. The remaining 20% is the insights, the strategy, the “aha” moments that drive real decisions.
The Types of Bad Data Examples
Basically, is everything valid, accurate, consistent and up to date… Not all data problems look the same. Here’s what you’re actually checking for:
1. Is it valid?
Look at your data… does it look like it should? For example, an email without an @ symbol or a phone number that has three digits, etc. These validity rules are simply about catching the obvious before it even reaches your CRM platform!
2. Is it accurate?
A phone number that has one digit off will pass validation checks, right up until your sales rep rings up a confused stranger (we’ve all done it). Accuracy is actually harder to automate, which is why data matters so much.
3. Is everyone using the same format? Is it consistent?
In other words, the VP of Sales and the Sales VP are the same person, but your CRM platform doesn’t recognise that. As a result, inconsistent data starts to creep in, which in turn damages your segmentation and reporting. From there, it also undermines the team’s trust in the system itself. Before long, it becomes a downward spiral.
Inconsistent data is one of the biggest reasons email campaigns underperform. See how you can tackle this in our article: Why Are My Email Campaigns Underperforming?
4. Is your data complete? Are there missing bits?
We’re talking about a contact with no email, a company with no industry or a deal with no close date. Gaps in your data mean gaps in your campaigns, your automation, and your forecasting. We recommend running a regular audit of your key fields, you might be surprised how many are sitting empty!
5. Is it duplicated?
The same contact is sitting in your CRM twice (or five times). It inflates your numbers, skews your reporting, and means the same person gets the same email multiple times. Nobody wants that.
6. Is it outdated?
People change jobs, move companies, and update their details. Data decays at around 20–30% per year just through natural change. That “hot lead” from 2022 might not even work there anymore, and you wouldn’t know it!
7. Is it labelled correctly?
For example, a contact might be tagged as a “Customer” even though they never actually purchased anything. Likewise, a deal could be marked as “Closed Won” when in reality it fell through. As a result, these incorrect labels quietly corrupt your pipeline, distort your reporting, and ultimately break any automation that relies on those tags.
How to Check if Your Data is Clean: The WeDoCRM Checklist
Run through this checklist and be brave!
- Do you have duplicate contacts in your CRM platform?
- Are there records with missing key fields (email, company, phone)?
- Is your contact data more than 12 months old without a refresh?
- Do you have inconsistent formatting across fields (e.g. phone numbers, country names)?
- Are there contacts in your database who have unsubscribed but are still being mailed?
- Could you confidently segment your contacts by industry, lifecycle stage, or purchase history right now?
- Do you know the last time someone deliberately reviewed and cleaned your data?
If you ticked more than three boxes, your data needs attention. If you ticked all seven, put the kettle on and let’s have a chat.
If you have done the checklist and you are completely sure your data is clean, you should have the results below.
The Results of Lovely Clean Data (Gold Stars For You)
Once your data is in good shape, everything else gets easier. Here’s what that looks like in practice:
1. You Stop Missing Sales Opportunities
Your sales team can see what’s in the pipeline. Follow-ups happen on time. Nothing falls through the cracks because it was assigned to a contact record that got merged six months ago.
2. Personalisation Becomes Real
You’ve heard it before: personalisation is now the expectation, not the exception. However, the reality is that you simply can’t personalise effectively with poor-quality data. Instead, clean data gives you a clear and accurate picture of who your customers are, what they’ve bought, what they’ve enquired about, and exactly what stage they’re at in the journey.
3. Your Reporting Reflects Reality!
When your data is clean, the numbers you pull actually reflect reality. Your conversion, customer lifetime and pipeline forecast is (much closer to) real. Decisions made on clean data are better decisions, full stop.
4. (The Most Important One) You Stay on the Right Side of GDPR
Data privacy isn’t optional, and the ICO doesn’t accept “we didn’t realise it was in there” as a defence. Put simply, clean data means having accurate consent records, up-to-date preferences, and, just as importantly, no nasty surprises when someone exercises their right to erasure. In other words, it ensures everything is properly aligned, so you’re not caught off guard when managing data requests or compliance obligations.
The fines for GDPR breaches are not small!
How WeDoCRM Can Help You
Think your data might be due for a Spring/Summer/Autumn/Winter clean?
That’s right, a data clean can be done all year round. If you have a creeping suspicion that your CRM/ESP platform is not quite the well-oiled machine you would like it to be, you are most likely right (and that is okay!) Every platform gets messy, the difference is what you do about it.
Start a small habit, put the rules in place and make a data clean part of how your team works.
Alternatively, if you’d prefer to skip straight to the good bit, you can put the kettle on (only one sugar for us, please, we’re sweet enough as it is), sit down, and, before anything else, take a deep breath. Then, once you’re settled, you can simply let us take care of the hard work for you.
Book a free CRM audit with WeDoCRM, we’ll take an honest look at what you’ve got, tell you exactly what needs fixing, and show you what a clean, well-structured CRM could do for your revenue.
Get in touch, we are excited to hear from you!
Sales@wedocrm.co | Book a call with us
Frequently Asked Questions – Data Cleansing
Here are some of our FAQ’s regarding data cleansing:
How often should we clean our CRM data?
We’d say, at an absolute minimum, quarterly. However, the best and most sustainable approach is to build data cleanliness directly into your ongoing processes from the beginning. That way, data quality is maintained consistently and proactively over time, rather than only being addressed reactively during stressful annual panic-cleaning sessions.
How do I find duplicates in my CRM?
Most platforms (HubSpot, Salesforce, Pipedrive) have built-in duplicate detection. There are also third-party tools that do this more thoroughly. A simple export to Excel and a VLOOKUP is a scrappy but surprisingly effective starting point if you’re budget-conscious.
Is data cleansing a one-time job?
Oh no, no, it’s definitely not, and if anyone who tells you otherwise is probably being overly optimistic. In reality, it’s an ongoing process, and because of that, you have to stay disciplined and consistent with it over time.
Think of it like tidying your house: yes, you can do one big deep clean and get everything looking perfect temporarily. However, if you don’t consistently tidy up after yourself afterwards, then gradually, little by little, the mess starts to build back up again. Before you know it, within a few weeks, you’re right back where you started!
What if we have tens of thousands of records to clean?
That’s where specialist tools and agencies come in. Manual cleaning at scale isn’t viable. A CRM agency (like us) can help you put the right tools and processes in place to do this efficiently.
Does data cleansing affect our email deliverability?
Yes. Sending to old, invalid, or disengaged email addresses damages your sender reputation. Cleaning your list regularly keeps your deliverability healthy and your emails out of the spam folder.
Can’t we just start fresh with a new CRM platform?
Tempting, but no. Migrating dirty data into a shiny new system just gives you the same problems in a prettier interface. Clean first, migrate second. Always.
How do we stay GDPR compliant while cleaning data?
Document your process carefully, ensure you have a lawful basis for retaining each record, and, importantly, make sure you honour any suppression or unsubscribe requests both before and after carrying out the clean-up. Additionally, if there’s ever any uncertainty around compliance or data retention, it’s always best to consult someone who fully understands the regulations. After all, when it comes to GDPR, “we meant to sort it” definitely isn’t considered a valid defence.
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