If you’re a CRM manager, Sales Operations Director or marketing manager who deals with customer and sales data, you know how time-consuming and tedious data cleansing can be.
The problem is, if you avoid taking data cleansing seriously, it’ll cause a ripple effect throughout your organization that affects everything from your operations to your bottom line.
In fact, over 25% of businesses find that poor data quality negatively impacts their revenue. It makes sense because “dirty” data leads to missed opportunities, sales efforts without direction, lower productivity and inefficient processes.
Luckily, implementing a smart data cleansing strategy can help you avoid the pitfalls of dirty data, give you accurate and actionable insights and create effective campaigns.
In this article, I’ll cover everything you need to know about data cleansing and give you the techniques to painlessly practice good data hygiene.
What Is Data Cleansing?
Data cleansing – also known as data cleaning, data wrangling or data scrubbing – means validating that the data you have is accurate and up-to-date. It involves fixing errors in your raw data set like misspelled names, filling in incomplete fields, removing duplicates and clearing out irrelevant or corrupted data.
For example, suppose you have two entries for Jon Snow. One says he works for Company A and the other says he works for Company B. Data cleansing will consolidate the entry with the right info so when you send him your email, it’s getting to the right person.
The goal? Making sure your information is accurate, so you can make the right decisions and avoid wasting your efforts.
Why Does Data Cleansing Matter?
As mentioned, data quality is vital for business success. A report from Experian found that bad data affects 88% of US companies, costing them an average of 12% in revenue.
Beyond its effects on your bottom line, bad data can impact your business in other ways. When your company relies on data analysis to make sales projections and plan for the future, the accuracy of your predictions is directly correlated to the quality of your data. In other words, bad data means bad predictions.
Also, bad data can lead to employee and customer dissatisfaction. Imagine your marketing team sending marketing emails to the wrong customer and your sales team contacting them only to be frustrated because they’re an unqualified lead. It’s a lose-lose for everyone involved.
With all of that said, let’s take a look at how data becomes bad in the first place.
What Makes Data Become “Dirty”?
The biggest contributor to bad data is time. As time goes on and you collect more data, it becomes outdated and inherent issues build up over time. This is called data decay.
However, other sources of bad data include human error – entering in wrong info or mislabelling it, usually as a lack of standardized methods for data collected from multiple sources and data being appended as a lead moves through the pipeline.
In general, dirty or bad data can be broken down into five categories:
- Duplicate data is having two or more entries for the same customer/lead with conflicting or inconsistent data.
- Outdated data is old info that’s either inaccurate or irrelevant.
- Invalid data is data that doesn’t match what’s expected, such as a phone number containing letters or an address in the name field.
- Incomplete data means it’s missing relevant info such as a customer name or email address.
- Inconsistent formatting refers to formatting errors like using First Name/Last Name for some entries and Last Name/First Name for others.
Understanding and identifying dirty data is a key step in the data cleansing process. Of course, tools can help with this (I’ll share below) because relying solely on manual methods is virtually impossible.
How to Effectively Clean Your Data: Our Data Cleansing Workflow
Now that you know what to look for, it’s time to clean house. We sat down as a team and outlined the practices that all of us have found to work the best in the past decades of our working experience.
And it all starts with a solid data cleansing workflow.
1. Data Profiling
Without a goal, you and your team might get lost. That’s why the first thing you need to do is understand the structure and the quality of your CRM data. In short: what are you dealing with?
Look at your customer data to identify the following:
- Patterns (Do some data issues occur repeatedly?)
- Anomalies (Are there some errors that happen sporadically and perhaps come from the same source?)
- Potential issues (Do you see significant gaps in the data?)
Then, check for missing information, duplicates and inconsistencies.
For example, it’s not strange to see that one of your many data sources has been feeding your system improperly formatted data. Similarly, if you’ve used multiple tools for the same purpose in the past, your data could be duplicated.
At this point, involve everyone who uses the data in the cleansing process. This includes your internal stakeholders and subject matter experts, as well as any external professionals you might need for guidance.
2. Data Quality Assessment Is Crucial in Data Cleansing
Once you’re aware of the real scenario, think about the ideal one: what would your ideal data look like?
Create data quality standards you’ll adhere to in the cleansing (and in the future), specifically looking at data quality metrics such as:
- Accuracy (E.g., All your lead contact information should be correct)
- Completeness (E.g., All lead contact information should include their first and last name, email address and phone number)
- Consistency (E.g., All lead contact information should be formatted as first name followed by last name, not joined or in the reverse order)
- Timeliness (E.g., All lead contact information should be updated monthly)
Then, evaluate your existing CRM data based on the predefined quality standards. Is it meeting the mark for accuracy, completeness and/or consistency? Where does it pass with flying colors and where do you need to put in the work?
A Note on Security
If you deal with sensitive information (particularly financial or health information), ensure you stay compliant with data protection regulations throughout the data cleansing process, especially if you use third-party services to help you.
3. Identify and Consolidate Duplicates
If your CRM is connected to multiple lead gen and data collection tools, duplication errors become more prevalent.
First, you can manually go through the data and check for redundant entries. Of course, you can see how that becomes extremely difficult if you’re dealing with thousands of contacts. I always say: automate as much as you can.
You can also foster a company culture that emphasizes accurate data entry and good data management practices so duplicates don’t occur in the first place.
Your second (and the best) option is to rely on software that can automatically detect and merge duplicates for you (deduplication). For example, Google Contacts offers this feature for free.
4. Make Sure Data Is Fresh and Up-to-Date
Data decay is unavoidable, but it can be mitigated. Research by Vainu found that 30% of data goes bad annually.
Think about it – people move companies, switch roles, get new email addresses or change their phone numbers. The data that was once fresh becomes instantly outdated and, without a process, you won’t know until it’s too late.
You can address this in a few ways. First, we have the manual methods such as calling the number to check if it’s valid. You can also remove emails from your contact list that bounce or go undelivered.
Second, you can use parsing tools like Zapier which pulls relevant info from your email inbox and saves it for you in a usable format. If connected with your CRM, your system will be automatically updated so you have the most accurate and relevant data.
Finally, if you want to prevent having to deal with this issue for your email address data, use a data tool like Findymail that provides email address information for your contacts, ensuring it’s verified and usable before you contact them. It integrates with the most popular CRM software, so you can use it on the go.
5. Fill in Missing and Incomplete Data
Of course, you can’t know everything about each and every contact. However, you can do your best to fill in as much as you can. The more data you can suss out, the more accurate your campaigns and predictions will be.
Again, you can do this manually by going through each entry and filling in any missing data by cross-referencing other info available. You can also simply remove or flag entries that don’t have enough data.
6. Address Structural and Formatting Issues
Another issue that affects data quality is formatting and other errors such as typos, incorrect abbreviations or punctuation and unique naming conventions. Or if you are combining datasets from sources, info may not transfer properly.
This can all be addressed by fixing structural issues. When you have a consistent data entry method, it cleans up your dirty data and makes it follow a standard format for fields like addresses, phone numbers and email addresses. This will keep info from ending up in the wrong category or ending up completely unusable.
7. Correct Inaccuracies
If you’ve spotted inaccuracies and misspellings, correct them. Depending on your initial findings, if you’ve noticed there are patterns related to specific errors, follow them straight to the source.
Is a specific data channel problematic because it sometimes results in errors? Is there a person on your team who often enters inaccurate data?
Once you understand the cause, it’ll be much easier to implement the right process.
8. Validate and Verify Your Data Accuracy
Your data isn’t fully valid – and that’s just a fact of life that many organizations struggle with. That’s why you need to validate data accuracy and verify it against external sources.
The first step is creating and implementing validation rules to ensure your data adheres to specific criteria. This is mentioned in step two, where you create quality control criteria to make sure all your data is high-quality.
Next, you’ll verify and update customer information by cross-referencing with external databases or third-party services.
For example, suppose your email address information is incorrect. You can use a data tool like Findymail to check all your records, verify them and replace them with correct email addresses.
9. Implement a Standardized Data Cleaning and Entry Process
Data cleansing is a continuous process that never ends. Remember, time is your enemy here and errors can crop up at any time. That’s why you need a standardized practice to mitigate (and prevent) the issues that can occur if bad data has piled up.
Design Your Data Cleansing Workflow for the Future
Don’t settle for a “Frankenstein” workflow if your data needs don’t currently require a robust process. Your company will grow and so will your data. At some point, you’ll just have to undergo the entire process again.
Set the right foundations. Design the workflow to scale as your data volumes grow.
Train Your Team (and Ask for Their Thoughts)
You can offer training that teaches your team how to enter data, how to spot duplicates or errors and why data is relevant. The third point is the truly crucial one as you’ll want to motivate your team to keep everything up-to-date, even if there are some things they have to do manually.
Make sure you encourage your team to give you feedback on data quality:
- Are they spotting any issues?
- Are some data types missing?
- Is the data accurate when used in practice?
Use that feedback to refine your process and add the missing data to get a better view of your customers.
Set Clear Data Entry Rules
You should also have clear and defined rules about the data entry process. For example, ensure everyone uses the same date format (e.g. day/month/year), how to handle abbreviations, whether all entries should be capitalized, etc.
My previous point is the most important one. If your data entry system is flawed, then issues will only compound over time. Following this step alone will increase the accuracy and confidence level of your data.
Iteration Leads to Perfection
Finally, make sure you implement ongoing data quality checks. You can set up automated monitoring for data quality metrics, so you receive alerts whenever an anomaly pops up in your records.
Maintain version control over every dataset that’s been cleansed, so you can map changes to a specific version and keep track of changes as your records grow.
Top 6 Data Cleansing Tools in 2024
As promised, here are some of my personal favorites when it comes to data cleansing tools.
1. Demand Tools
DemandTools from Validy is a powerful data quality tool that integrates seamlessly with CRMs like Salesforce and Microsoft Dynamics 365. It helps businesses manage their lead generation and conversions. Its advanced algorithm finds and fixes duplicates, manages and updates data in bulk and standardizes data entry.
There are several modules, such as the Discovery Tool, which validates your CRM data by comparing it to external data sources. I also like the Maintenance Tool for streamlining how data is managed in the CRM.
2. OperationsOS (RingLead)
OperationsOS (formerly RingLead) by ZoomInfo bills itself as a data orchestration platform. In reality, it’s a data management solution for CRMs that offers data cleansing features, as well. You can use it to find and remove duplicates, standardize your data and link leads together.
However, it does more, including segmentation, lead list building and automatic routing. If you’re looking for a comprehensive solution that not only helps you clean data but also enriches and protects it, give OperationsOS a shot.
Findymail offers first-class contact data and integrates seamlessly with tools like Sales Navigator, Salesforce, Apollo.io, Hubspot and more. It helps you find and eliminate email address duplicates, enriches your data and provides verified email addresses right off the bat.
It uses a proprietary algorithm that brings back verified email addresses and accurate contact data you can start using right away.
4. WinPure Clean & Match
WinPure Clean & Match is a dedicated data cleansing tool that helps you eliminate duplicates, clean your data, fill in incomplete details and fix any data-related issues. It takes business and/or consumer data found in databases, email lists, CRMs, spreadsheets and more.
5. Operations Hub
Operations Hub provides an all-in-one data management solution from Hubspot. It automates data cleansing by finding and merging duplicates, fixing formatting issues and more.
It also provides a data quality command center where you can see the health and accuracy of your CRM data at a glance. If you already use the Hubspot CRM, it’s a no-brainer to use Operations Hub to help sync and clean your data.
6. Melissa Clean Suite
Melissa Clean Suite offers data cleansing software that integrates with some of the most popular CRMs like Salesforce, Microsoft Dynamics CRM and Oracle CRM/ERP.
The fact that it works with so many leading tools makes it one of the most popular data-cleaning tools on the market. It offers deduplication, data enrichment & verification, automatic updates, autocompletion for missing data and real-time and batch processing.
Embark on Your Data Cleansing Journey Today
With data being king in the business world, your data must be accurate, relevant and usable.
Get rid of duplicates, fill in missing fields, remove wrong data and develop a company-wide standard for data entry so when the time for data analysis comes, you can be confident your data is pointing you in the right direction.
Of course, data cleansing is a continuous process. You need to constantly check and make sure your data management practices are getting you the best quality data possible. Not only will your employees thank you, but you’ll see a significant increase in productivity, efficiency and, ultimately, revenue.
Don’t let dirty data derail your business. With this article, you have all the info and tools you need to clean up your data effectively. Remember, the quality of the input determines the quality of the output, so make sure you’re feeding your strategy with the right information!