Your CRM is supposed to be your single source of truth, but its reliability hinges entirely on your data quality. We’ve all had that moment where a sales report looks a little… fictional, right? That’s what happens when inconsistent information leads to dashboards nobody trusts and a team that starts to doubt its most important tool.
This isn’t just a hypothetical problem; it has a direct impact on the bottom line. For example, we recently saw a client struggling with their revenue forecast because their CRM data was riddled with inaccurate deal values and outdated close dates. This is a classic data quality issue that led directly to flawed financial planning.
A shaky forecast like that isn’t an isolated incident. It’s a symptom of deeper issues. The root causes of poor data quality almost always fall into a few familiar categories. Here’s what we typically find hiding in the corners of a CRM.
Common Data Quality Issues
Seeing Double (and Triple, and Quadruple…)
This is the classic duplicate problem. Your system has three different contacts for Sibongile Khumalo because she used her personal email once, her work email another time, and a colleague imported a list with a typo in her name. Now her conversation history is split, and your sales rep has no idea which record is the right one. The same goes for companies, where you’ll find “ABC Corp” and “ABC Corporation (Pty) Ltd” floating around as two separate entities.
The Wild West of Data Entry
This is what happens when there are no rules. One person enters “VP of Sales,” another types “Sales Vice President,” and a third just puts “Sales.” Congratulations, you now have three different job titles for the same role, which makes building a targeted list a nightmare. You also see this with phone numbers in every format imaginable and country fields with “South Africa,” “ZA,” and “RSA” all competing for attention. A lack of standards is a common cause of poor data hygiene, making it nearly impossible to segment lists and run accurate reports.
The Ghosts in the Machine
These are the records with gaping holes where essential info should be. You’ve got a list of contacts, but half of them are missing a Lead Source, so you have no clue if your marketing efforts are actually working. Incomplete records are a key indicator of poor data quality, preventing effective automation and making reporting nearly impossible.
Digital Time Capsules
This is when you’re working with inaccurate data that’s just plain old. I’m talking about the contact who is still listed as the Marketing Manager at a company he left back in 2018. It’s the deal that’s been sitting in your pipeline since last year with no activity. This outdated information clogs up your system and gives your team a completely false picture of your sales pipeline and customer base.
Shouting Into the Void
This is all about the health of your email list and its impact on your business. The most critical issue is when your team sends an important product update or a service renewal notice, and it’s never received by the client.
This failure to communicate often reveals itself as a hard bounce. While your CRM is smart enough to stop sending to that address, the bounce itself is a clear symptom of inaccurate data – a contact who has left their job, a simple typo, or a fake address. A high bounce rate on any campaign signals poor list quality to email providers, which can damage your sender reputation. Good data hygiene also means monitoring contacts that soft bounce repeatedly, as a ‘mailbox full’ error several times in a row often means an address is about to become a dead end.
5 Ways To Improve Data Quality
Seeing the list of common data issues might feel familiar. The good news is that data cleaning doesn’t have to be a frantic, one-time fix. It’s about building better data hygiene habits to keep your CRM organised and reliable.
Here’s how you can start.
1. Start with an Audit (You Can’t Improve What You Can’t Measure)
Before you adjust anything, it’s best to get a clear picture of your data’s current state. This is your reconnaissance mission. An audit is the crucial first step in any data cleansing project, helping you identify the biggest opportunities for improvement so you can prioritise your efforts effectively.
HubSpot’s own Data Quality Command Center is the perfect place to start. It gives you a dashboard that highlights areas needing attention, such as formatting inconsistencies or potential duplicates. This initial review shows you where a little effort can make a big impact.
2. Review and Simplify Your Fields
Over time, any CRM can accumulate dozens or even hundreds of fields (or ‘properties’). Many of these are leftovers from old campaigns, previous sales processes, or integrations that are no longer in use. This digital clutter doesn’t just look untidy; it actively works against your data quality.
Take a step back and regularly audit your properties. Ask your team:
- Which of these fields do we actually use in reports?
- Is this information still critical to our sales or marketing process?
- Can this free-text field be converted to a dropdown menu to standardise the answers?
Regularly reviewing and archiving unused fields is a proactive data hygiene strategy that makes your CRM simpler and encourages better data entry for the fields that truly matter.
3. Create a Rulebook (and Actually Use It)
To maintain long-term data quality, you need to set up clear rules and guidelines for your team. This is about creating a “single source of truth” that prevents future disorganisation. Get everyone on the same page and agree on a consistent way to enter information.
This means establishing standard naming conventions and formatting. For example:
- Job Titles: Are they “VP of Sales” or “Sales Vice President”? Pick one standard.
- Phone Numbers: Do they all need a country code? Agree on the format.
- Ownership: What’s the rule for assigning new leads from the website?
Document these guidelines and make them part of your team’s training. A simple, shared rulebook is the best way to keep your data consistent.
4. Let the Robots Do the Heavy Lifting
Manually sifting through thousands of records is time-consuming and prone to human error. Instead, use automation tools to handle the repetitive work for you.
For instance, you can use built-in tools to merge duplicate records or set up workflows to automatically format properties and assign new leads. Automation is at its best when it’s proactive. A great example is automated attribution tracking, which captures a lead’s original source, like a Google Ad or a partner website, without any manual entry. This ensures your marketing ROI data is accurate from the very start.
Using automation for both reactive cleaning and proactive data capture is what keeps your CRM trustworthy, freeing your team to focus on their actual jobs.
5. Play Detective and Validate Your Data
For your most important records, you need to ensure the information is accurate and reliable. Encourage your team to validate data against trusted sources.
This doesn’t have to be complicated. Before a big sales call, have your rep spend 30 seconds on LinkedIn to confirm their contact’s job title is still correct. You can also use third-party data enrichment tools (like HubSpot’s Breeze Intelligence or ZoomInfo). These services automatically check and refresh your records with verified, up-to-date information, ensuring your team is always working with the best data available.
Conclusion: From Fictional to Factual
Improving your data quality isn’t about a single, heroic cleanup effort. It’s about building small, consistent habits that pay off every single day. By auditing, simplifying, and standardising, you transform your CRM from a source of frustration into a reliable engine for growth.
Stop letting inaccurate data make the decisions. When you commit to better data hygiene, you turn those fictional reports back into fact. You build a system your team can finally trust, and that’s a competitive advantage you can take all the way to the bank.
