I Help Teams Clean CRM Data for AI. These 5 Steps Make the Biggest Impact

In part one of this series, we established an uncomfortable truth: most CRM systems contain data that's nowhere near ready for AI implementation. We explored how your AI strategy is likely built on quicksand, examined the causes of poor data quality, and detailed the four devastating ways poor CRM data will sabotage your AI initiatives.

Now it's time to focus on solutions. With over six Salesforce certifications and experience as Editor-in-Chief at SFApps.info, I've seen firsthand what works and what doesn't when preparing CRM data for AI. 

Let's dive into the practical steps required to salvage your CRM data before it poisons your AI investment.

Bad CRM data will break your AI. Fix it first

In my previous article, I unpacked the problem and the costly consequences of ignoring it. Now, it’s time to focus on fixing it. 

Below, I outline a series of steps and best practices that can stop bad CRM data from quietly sabotaging your AI efforts. These aren’t just theoretical best practices — they come from a mix of hard-won industry lessons and firsthand experience. 

The goal? To clean up your data, establish better processes, and put tools in place that give your AI strategy a real chance to succeed!

What makes this approach different is that each recommendation comes with specific actions you can implement immediately, regardless of your organization's size or technical sophistication.

Let's begin transforming your CRM data from AI's greatest obstacle into its most powerful enabler.

Step 1. Data detox: Conduct a thorough data audit and cleanup

Before you can fix the problem, you need to measure it. Start with a CRM data audit. This means systematically evaluating the current state of your data. 

Pay attention to:

• Completeness: E.g., what % of contacts have all key fields filled? 
• Accuracy: Perhaps by spot-checking some entries against known truths. 
• Consistency: Are naming conventions and formats standardized?
• Duplicates: How many duplicate accounts or leads does your CRM contain?

In Salesforce, you might utilize built-in reporting or data assessment tools. 

For instance, Salesforce has a "Duplicate Records" report type if you have duplicate rules in place. You can also use the free Salesforce Data Quality Analysis Dashboards App, which contains pre-built data quality reports on standard objects. There are also third-party auditing and data cleansing tools that can scan your CRM data for common issues.

Once you know the damage, it's time for a Salesforce data cleanup. A data cleanup project involves such tasks as:

Deduplication 

Merge or delete duplicate records. This can be time-consuming, but there are tools to help. Many CRM data quality solutions include duplicate management features. For example, there are tools that use AI to identify likely duplicates even if names aren't exact matches by recognizing patterns.

Addressing missing data 

Decide how to handle blanks. For crucial fields, you may undertake a research project or use an external data service to fill in missing information. For less important fields, you might simply note the gap but not consider it a critical problem. One effective approach is data enrichment, using external databases or services to append information.

Correcting errors 

Fix obvious mistakes. If states or countries are misspelled or in the wrong field, correct them. Normalize formats. For example, use MM/DD/YYYY for all dates or all phone numbers include country codes, etc., depending on your standards. This might involve exporting data to Excel or using a tool to standardize values. Many CRMs allow bulk update operations, which can be handy. Just be careful and perhaps test on a small subset first.

Update outdated records 

Identify data that hasn't been touched in a long time and verify if it's still relevant. For example, if there are leads from five years ago with no activity since, those might be safely archived or deleted. If a major client's record hasn't been updated in two years, maybe it's time to confirm their details. Some companies perform automated checks, like sending emails and seeing if they bounce, as a way to mark contacts for updating.

Validate critical data 

For key data points that will feed AI, consider a more rigorous validation. For example, if your AI heavily uses the "Annual Revenue" field of accounts, make sure those values are correct and maybe cross-check against a financial database or recent news. The principle is: check the things that matter most twice.

This cleanup phase can be time-consuming, but it's absolutely worth it.

Importantly, treat data cleanup not as a one-off but as the beginning of a cycle. Depending on how fast your data changes, you'll likely need to do major cleanup periodically, perhaps annually or quarterly.

Once you've done the big clean, focus on prevention so you don't end up in the same spot a year later.

Step 2. Process overhaul: Improve Salesforce data entry practices

A huge portion of CRM data problems comes from how data gets into the system. This usually means how your team is entering or importing data. Salesforce data entry might sound routine, but it's the front line of data quality. 

If you can improve how data is entered from the start, you'll prevent countless feature issues. Here's how:

Establish clear guidelines and training 

Don't assume everyone knows what "good data entry" means. Create a data entry policy or playbook that spells out things like required fields for new leads, proper formatting, and what to avoid. Train your team on these guidelines. Make it part of onboarding for new CRM users.

Simplify the data entry process 

Sometimes, data degrades when the entry process is too complicated. If a Salesforce form has 50 fields, a rep will be tempted to skip half of them or fill them with whatever just to move on. Review your CRM interface. Perhaps you can reduce the number of required fields to just the essentials. Or use conditional logic — only show certain fields when relevant.

Use validation rules 

Modern CRMs like Salesforce allow you to set up validation rules — checks that trigger when data is entered that doesn't meet criteria. You can even create more advanced rules and ensure that if "Country = USA", then "State" must be one of the 50 states. These rules act as filters to keep obviously bad data out. 

Be cautious not to overdo it (overly strict rules can annoy users), but a few smart validation rules can dramatically improve quality.

Automate data entry where possible 

One of the benefits of AI and advanced software is automating tedious data entry tasks. For example, if reps are manually entering data from business cards or email signatures, consider tools that can scan and populate that for them. There are AI-powered tools that can, say, read an email from a customer and auto-update the CRM with any new contact info in the signature.

Create a culture of data quality 

Ultimately, tools and rules help, but the people using the CRM need to care. Encourage your team to view the CRM as the single source of truth for customer information. Recognize or reward team members who consistently keep their data clean. Conversely, make it clear that inaccurate data entry is not acceptable because it hurts the business.

Think of this step as plugging the holes in a leaky boat. Your earlier Salesforce data cleanup effort might have helped pump out the water, but without better data entry practices, bad data, like water, will keep leaking in.

Step 3. Tech advantage: Use CRM data quality solutions and automation tools

You don't have to tackle data quality manually. In fact, given the volume of data most companies handle, manual cleaning alone won't scale. There is a growing market of CRM data quality solutions designed to help. These range from features already available in your CRM to third-party software add-ons. 

Some popular solution categories include:

• Duplicate detection tools: Many CRMs (Salesforce included) have built-in duplicate detection these days. Turn those features on. Salesforce's Duplicate Management can alert users or auto-block when they try to create a record that looks like a duplicate. Beyond built-in features, there are third-party tools that do advanced fuzzy matching to catch dupes that simple rules might miss. Some solutions use AI to continuously scan and suggest merges, learning from patterns.
• Data validation and enrichment services: These are external services that integrate with your CRM to verify and enhance data. For example, services that validate email addresses or phone numbers as they're entered to ensure they're real and reachable. Or services that enrich a lead with missing information. There are also address validation tools.
• Workflow automation and quality monitoring: You can set up automated workflows or scripts that maintain data quality. For instance, a nightly job that flags any accounts created without an industry and sends a list to the ops team, or an automation that, if a contact's email permanently fails to deliver, it marks that contact record as "invalid email" so it doesn't get used again until updated. Modern CRM ecosystems often have low-code tools (like Salesforce Flow or other automation builders) where you can implement these quality checks.
• AI-powered cleansing tools: As meta as it sounds, AI can help you prepare for AI. There are AI-driven solutions that specifically focus on data cleansing. They might analyze patterns in your data to find anomalies or automatically fill obvious gaps. These Salesforce data cleaning tools are especially useful if you have huge datasets and limited human resources to process them.

When choosing any tool or solution, make sure it integrates well with your CRM and is trusted. If your CRM is Salesforce, you'd look on the AppExchange for data quality apps, ensuring they have good reviews and security compliance. It's also wise to run a small pilot: test the tool on a subset of data to ensure it does what you expect.

Also, don't blindly trust an automated solution without oversight. These tools are helpers, not magic. You still need a human-in-the-loop to review suggestions or handle exceptions. Customize your CRM data quality solutions to avoid errors.

Step 4. Salesforce data cleaning for AI: Prepare your data for specific use cases

Once you have day-to-day data quality management under better control, think about the specific AI applications you plan to implement. Preparing data for AI might require additional steps beyond general cleaning. This is where Salesforce data cleaning for AI really comes into play: tailoring your data prep to the AI's needs.

Consider what AI you are deploying

Is it a machine learning model you're training internally? Is it a generative AI that will use CRM data to draft emails or answer questions? Or maybe an analytics AI that finds patterns for segmentation? Each might have unique data requirements.

Feature selection and quality 

If you're training a predictive model, identify which fields and features from the CRM will be used. Often, not every single field in CRM will feed the model; you choose likely relevant ones. For those key fields, ensure they are extra clean and consistent. For example, if "Lead Source" is a feature in your model, you must make sure lead source data is reliably entered and standardized.

Data unification 

Many AI use cases require merging data from multiple sources to get a full picture: CRM, marketing automation, customer support logs, etc. Ensure you have a unified dataset. That might mean linking records by a common identifier or performing an extract, transform, load (ETL) process to combine data into a data warehouse or data lake for the AI to access. The key is to break down silos.

Address privacy and bias before feeding AI 

When preparing data for AI, consider excluding or anonymizing certain data to prevent unwanted outcomes. For example, to avoid biases, you might strip out fields like race, gender, or other sensitive attributes from the training data, unless they are absolutely needed and ethically used. Also, ensure any personal data complies with privacy laws (GDPR, etc.) when used for AI — sometimes that means anonymizing or aggregating data in the AI training set.

Test AI outputs on known scenarios 

A part of prepping data is also validating that your cleaned data leads to reasonable AI behavior. I recommend doing a test run. Take your freshly cleaned dataset and feed it to a test instance of the AI before full deployment. See if the outputs make sense given what you know. This testing phase can catch issues that weren't obvious in raw auditing.

Some companies think they can shorten this process by allowing AI to work with data without proper AI-oriented data preparation. Doing so can create "data chaos" and bad results. 

The recommendation is clear: organize and govern your data first, then apply AI. In other words, don't expect AI to fix your data problems; fix your data problems so your AI can shine.

Step 5. Accountability: Establish ongoing data governance and ownership

Up to now, we've discussed a lot of tactics: cleaning, training users, using tools, and preparing for AI. But who is going to keep all of this going in the long run? Data governance is the answer. 

Data governance means having defined processes, roles, and policies to manage data quality continuously. It's not a one-time project; it's an ongoing discipline.

Key elements of data governance in the context of CRM data quality:

• Assign data owners or stewards: Identify who in your organization is responsible for CRM data quality. It could be a specific role like a Data Steward or a CRM Operations Manager. In smaller companies, it might be an extension of someone's job. They will be the ones to coordinate audits, run cleanup routines, and ensure new processes are followed.
• Define data standards and document them: We touched on having guidelines for data entry; governance formalizes this. Develop a data dictionary or a set of standards for your CRM data. Having these rules written down means there's less ambiguity. Share this documentation across teams. Everyone who touches the CRM should be aware of it.
• Regular data quality reviews: Make data audits a recurring event. Perhaps you review key metrics monthly and do a deeper audit quarterly. Many companies have a governance committee that meets periodically to discuss data issues and improvements. If you discover new types of issues, update your processes accordingly.
• Incorporate AI outputs into governance: When you do roll out AI tools, govern them too. AI cannot be exempt from evaluation. If an AI model suggests something that's wrong due to data, treat it as a data quality issue to fix. Also, monitor AI outputs for fairness and accuracy. The point is, include AI in your data governance scope.
• Measure and celebrate improvements: Data quality can feel abstract, so it's helpful to establish KPIs. Track the progress: if you were at 80% data completeness and now you're at 90% after certain actions, highlight that improvement. It will motivate the team and justify continued investment.

A strong governance practice ensures that all the work you put into cleaning and fixing data doesn't fade away. 

It creates accountability. Without it, it's too easy for things to revert to old problems: new hires might not follow protocol, a big import of leads from an event might flood the system with messy data, etc. Governance catches and addresses these quickly.

Turn data quality into your AI's superpower

In the rush to implement AI and get ahead of competitors, it's easy to overlook something as basic as data quality. But as I've learned, clean, reliable data is the overlooked essential element of every successful AI initiative. You wouldn't build a skyscraper without a solid foundation; likewise, don't build AI on a foundation of sand or, in this case, a database of doubtful accuracy.

The CRM data quality crisis is real, but it's also avoidable. It requires attention, the right techniques, and a commitment from the organization. 

By auditing your data, engaging your team, establishing good practices, and utilizing tools for Salesforce data cleaning for AI readiness, you can ensure that your AI projects start on the right foot. The process might reveal some uncomfortable truths about the state of your CRM, but confronting those truths is far better than having an AI project fail because of something you could have fixed.

If you've read this far, you're already ahead of many problems simply by being aware of the issue. Now, take action. Start by auditing your data, engaging your team in data quality, and implementing practices that will keep it at a high level. Your AI and your business outcomes will be so much better for it.

In the end, it's simple: better data leads to better AI. Solve the CRM data quality crisis, and you'll unlock the real power of artificial intelligence for your business. After all, even the smartest AI needs good data to shine.

Missed part 1? Read "I've Seen AI Initiatives Fail for One Simple Reason: Bad CRM Data" to understand the full scope of the problem before implementing these solutions.

Follow Dorian Sabitov for insights and practical advice to navigate and utilize the Salesforce ecosystem effectively.

Edited by Shanti S Nair