Most Salesforce orgs don’t start with a data problem. They create one over time.
At first, everything works as expected. Salesforce provides a structured, reliable way to manage customer information, and teams trust what they see.
But as usage grows, so does complexity. More users enter more data in more ways, and slowly, the CRM starts to drift. Duplicate records appear, key fields go unfilled, and reports become harder to trust.
At that point, it’s easy to blame Salesforce. But the reality is more fundamental: it’s not a CRM problem, it’s a data problem.
The Danger of Dirty Data
Salesforce is a powerful platform for structuring and managing customer data. But without the right processes in place, poor data quality can quickly become the default.
In practice, this shows up as duplicate records, unverified contact details, and incomplete fields. At a small scale, these issues are frustrating but manageable. Individual users can often spot errors and correct them as they go.
As the business grows, however, the problem compounds. What starts as a few inconsistencies turns into a systemic issue that affects teams across the organization.
Dirty data is not just a reporting issue. It is a trust issue. When data cannot be trusted, the impact is felt everywhere:
- Forecast calls are derailed by conflicting or incomplete information.
- Leadership begins to question dashboards and reports.
- Admins are blamed for problems they did not create.
- “We’ll clean it up later” becomes the default mindset, but later never comes.
Over time, this erodes confidence in Salesforce as a system of record. Teams stop relying on the data and start working around the CRM instead of within it. Then, decision-making becomes steadily slower and less accurate.
The result? Missed opportunities, weaker customer relationships, and ultimately, lost revenue.
So how do you solve the problem?
How to Define Data Quality and Enforce It
If you recognize the challenges from the last section, there’s a good chance you’re suffering from poor Salesforce data. Often, the instinct is for teams to jump straight into fixing the problem. But that approach often leads to more frustration than progress. In fact, one of the biggest mistakes you can make is trying to fix data without first defining what “good” actually looks like.
Instead, it’s important to establish a structured framework that defines what high-quality data means for your organization, and how it should be maintained over time.
But what does that involve? Of course, the details will vary by organization. But realistically, you should aim to ensure Salesforce data is:
- Complete: With key fields consistently populated.
- Accurate: Reflecting real and up-to-date information.
- Consistent: With standardized formats and values.
- Trustworthy: So teams can rely on it for reporting and decision-making.
Here are four stages to help you do just that:
1. Audit Your Data
Before you can clean up your data, you need to understand what you’re working with. There are several built-in tools in Salesforce to help you do this:
- The best place to start is by viewing reports of your Salesforce contacts, which you can do through the Reports tab. Here, you’ll see key fields from your records in a straightforward, spreadsheet-style format. This enables you to easily see empty fields and dummy/placeholder records.
- If you want to export the data to CSV, you can use Data Export or Salesforce Data Loader.
- To analyze larger datasets, you can use tools like OrgCheck. This gives you further visibility into the data model, as well as role hierarchy, metadata quality, and more.
These tools can give you an overview of the data you have and how it’s structured. But if you want to perform more complex analysis on larger datasets, you may need to consider third-party tools instead. These can be found through the Salesforce AppExchange.
2. Deduplicate Existing Records
Duplicates cause some of the most common data quality issues in Salesforce. But they can be hard to find with the techniques we discussed in the last section.
Instead, Salesforce offers some rudimentary tools to help identify duplicate records. These include Matching Rules (to identify which fields to match and how) and Duplicate Rules (to control when and where to find duplicates). You can define and customize these in Setup.
These work on simple, rules-based logic, such as detecting records where the first and last names match, but the email doesn’t (Exact Match). You can also detect common variations on first names, like ‘Jonathan’ vs. ‘John’ (Fuzzy Match).
Rules like this are good for the most obvious offenders – but there will always be edge cases that they can’t catch. They’re also more effective at identifying duplicates than resolving them. By default, you have two options when duplicates are found: Alerting the user or blocking the record. In practice, this can leave a significant amount of manual work for admins.
With large datasets, therefore, it’s generally more effective to use third-party apps. These can offer more granular rules to identify duplicates, alongside the ability to merge them at scale.
3. Decide What to Control and How
Now, it’s time to make some decisions about what good quality data looks like in the future.
Here’s the bottom line: Not every field in Salesforce is critical. And if you make every field compulsory, you can end up causing more problems than you solve. Having ‘N/A’ or ‘no information’ in every field isn’t going to improve your data quality – even if the fields are required.
Nonetheless, certain information definitely is important. For most contacts/leads, that would include details like first name, last name, email, phone number, and company. You might also want to require specific information that’s particularly relevant to your sales processes, such as location, lead source, or projected revenue.
To work out what these rules look like, there are a few key questions it’s helpful to ask:
- What fields should be mandatory, and which can be optional? Choose carefully – going too far in either direction can do more harm than good.
- At what stage should detail be required? Information like ‘projected revenue’ might be optional at the prospecting stage, but should probably be mandatory during the price/quote stage.
- What format should the data be stored in? Here, you can choose from open text fields, picklists, or numerical figures.
4. Enforce Policies With Salesforce Functionality
Salesforce offers a rich array of tools and policies that let you define what information is recorded in Salesforce – as well as when and how. These include:
- Schema Builder: A useful way to review and visualize your existing field structure. This gives you oversight over what formats and requirements are already defined, so you can review and build on them.
- Picklists: Setting certain fields as picklists forces end users to choose from a set of predefined options – that you choose. You can also create dependent picklists, which change the options presented to users based on their previous input. For instance, if a Salesforce user clicks ‘USA’ in one picklist, the second (dependent) picklist would list all US states. This helps avoid errors like Country: France, State: Texas.
- Dynamic Forms: This lets you show particular fields to Salesforce users, based on previous input. For instance, if the opportunity status is changed from ‘prospecting’ to ‘contract’, Dynamic Forms could show new required fields – like deal size or contract type.
- Validation Rules: These enable you to set more granular rules about what is recorded in Salesforce and when. A simple example might be requiring a ‘justification for discount’ field if the Salesforce user adds a discount into Salesforce. You may also want to prompt follow-up information if an organization is in a particular region or has revenue over a certain threshold.
- Salesforce Flow: For more complex requirements, Salesforce Flow provides additional options. Here, you might want to ensure deals over a certain size are passed over to a specific account manager, or that they should be validated by the finance or RevOps team before proceeding to the contract stage. The world is your oyster here – it all depends on your specific organizational structure and sales processes.
Together, these rules ensure that data is collected in a consistent and comprehensive format. This makes it much easier to rely on in the future.
Plauti: Clean and Complete Salesforce Data, at Scale
With these techniques, you can quickly make some effective changes to the quality of your Salesforce data. But, in truth, data quality requires more than just a one-time fix – it has to be a continuous and iterative process.
Governing data like this in Salesforce is possible, but it’s not easy. At scale, issues like duplicates, unverified contact details, and missing information become progressively more difficult to solve. That’s where Plauti comes in.
Plauti builds on the Salesforce functionality we’ve discussed throughout this piece, making it quicker and easier to govern data quality at scale. Here are some key tools we use to do that:
- Deduplicate: Find, clean, and prevent duplicates at scale, with the most advanced, Salesforce-native deduplication app on the market.
- Verify: Automatically validate customer information like emails, phone numbers, address fields, and more.
- Manipulate: Manage customer records at scale, including tools to update, delete, enrich, and assign ownership of records in batch.
- Assign: Automatically assign standard or custom objects, without manual effort required.
Want to find out more about the topics we discuss in this article? Check out our recent e-guide: Untapped Potential: The Data Quality Story.
