Most Salesforce admins already know their org has duplicate records, missing fields, and inconsistent formatting somewhere. Fewer have a plan for actually breaking that cycle instead of just running another one-time cleanup.
Salesforce data quality was the focus of a recent joint webinar between FormAssembly and DataGroomr, and the session made one thing clear: as AI agents get layered on top of the CRM, the data underneath them matters more, not less.
Watch the presentation on-demand at your leisure, or read on for a re-cap of what was covered.
What Counts as Quality Salesforce Data in 2026?
In 2026, quality Salesforce data means data that is clean, consistent, complete, contextual, correctly formatted, and connected to every system that touches it. FormAssembly built that six-Cs framework from a survey of about 500 AI decision-makers conducted at the end of last year, and the results explain why data quality keeps climbing the priority list even as AI tooling matures.
Ninety percent of respondents said their AI usage had increased over the past year, which was not surprising. What was more concerning: 60% said the AI tools they were using conflicted with other data in their systems, and the CRM was called out specifically. Teams reported spending 43% of their time cleaning CRM data before it could be used in AI or analytics tools, with three to five hours of cleanup required for every one hour of actual analysis. When asked why AI projects underperform, respondents pointed to integration challenges and a lack of clear strategy as the top two reasons.
Bad data does not stay contained to a spreadsheet, either. Once it moves from a form into a system of record, incomplete, inconsistent, or incorrect values affect sales teams, support teams, and the AI agents referencing those same records. This is exactly how organizations end up with automation and agents that quietly stop working the way they’re supposed to.
Finding the Source of Bad Data Before You Clean It
Before an organization can fix its Salesforce data quality, it needs a clear picture of where things stand. DataGroomr builds that picture with a data quality dashboard: within a few minutes of starting a trial, the platform uses machine learning to profile an org’s data and surface duplicate rates, field-level completeness, and consistency across whichever standard or custom objects a team chooses to analyze.
The dashboard shows the symptoms, but DataGroomr’s Insights View is built to trace root causes. In a sample org walkthrough, one particular user account, along with the org’s Web-to-Lead form, turned up as the two biggest sources of duplicate records. From there, a pattern-analysis screen breaks down what kinds of duplicates are being created, and a recurrence-trend graph tracks whether duplicate groups keep reappearing after a merge, confirming whether the actual root cause has been fixed, not just patched over.
That root-cause focus is the difference between a cleanup project and an actual fix. DataGroomr isn’t built around a one-time cleanup: once those data quality guardrails are in place, the same tools that established the baseline get used continually to assess what’s wrong, understand where issues originate, fix issues in real time, and prevent new problems within a maintainable and repeatable process.
Stopping Bad Data at the Door With FormAssembly
The fastest way to reduce Salesforce data quality issues is to stop them before they ever reach the CRM. A demo during the presentation showed how FormAssembly handles that at the Salesforce connector and form level, starting with field-level validation in the form builder: required fields, character and word limits, expected input formats for emails, and custom regex-style formats for anything from phone numbers to structured ID fields.
Dynamic picklists take that further by pulling their values directly from a connected Salesforce object, so a respondent can only choose from options that already exist in the CRM. Update the picklist values in Salesforce once, and every form referencing that field updates automatically – no separate list to maintain, and no chance of a stray value entering the system that does not match anything downstream.
The bigger duplicate-prevention move happens inside FormAssembly’s workflow builder, where a Salesforce lookup step searches for a matching contact before anything gets written to the CRM. If a match is found, its contact ID is captured as a variable and used to attach any new form submission (such as an event registration) to the existing record instead of creating a second one. If no match is found, a new contact is created and that same variable pattern applies going forward.
Fixing the Data Already Inside Salesforce
Even with strong intake controls, duplicate and inconsistent records inevitably build up inside a Salesforce org over time, which is where DataGroomr’s dedupe, cleanse, verify, and enrich modules come in.
- Dedupe uses matching models – a blend of classic, rules-based matching and AI matching trained on an org’s own data patterns – to group likely duplicates side by side for review. Admins can adjust the master record, apply field-merge rules to pull the most complete value from each field, or run mass merges across an entire group of matches instead of resolving records one at a time.
- Cleanse applies transform rules to standardize formatting issues that survive a merge, such as inconsistent phone number formats, mismatched capitalization, or states written out instead of abbreviated.
- Verify checks physical addresses, phone numbers, emails, and websites against outside data sources and returns a suggested correction where one exists.
- Enrich fills in missing fields, such as annual revenue, industry, or employee count, using outside data tied to an account or company.
Every one of those modules connects back to prevention, too: the guardrails a team sets up in dedupe are automatically enforced the next time someone imports an outside list, so a bulk import cannot silently reintroduce the same duplicate patterns DataGroomr just cleaned up.
What the Live Q&A Revealed
Attendees asked pointed questions that mapped directly onto the “find, fix, prevent” theme of the session. A few stood out.
Q: Is DataGroomr part of FormAssembly, or a separate app?
A: The two are separate products with separate teams and no native integration today, though both companies said they are gauging interest in a deeper connection based on the volume of questions asking about one.
Q: Is DataGroomr’s AI matching actually AI, or just a complex algorithm?
A: DataGroomr uses a mixture of both; they have complex math and unique IP combined with AI. Furthermore, DataGroomr’s machine learning models train on an organization’s own data patterns to predict likely matches. AI recommendations can be turned off entirely for teams that prefer a rules-only approach, or an organization can bring its own LLM key.
Q: How often should a team assess Salesforce data quality?
A: Both companies gave the same answer: continuously, not as a one-time project. DataGroomr’s dashboard tracks quality metrics over time so teams can see whether they are actually improving, and FormAssembly’s team added that data collection processes deserve the same ongoing attention, since compliance requirements and the data an organization actually needs both shift over time.
What This Means for Salesforce Teams
The through-line across both platforms is that Salesforce data quality is not a project with an end date. Stopping bad data at intake and cleaning up what already exists are two halves of the same job, and neither one holds up on its own if the other gets ignored. Teams that treat data quality as a continuous, measurable process – with clear visibility into where bad data originates and guardrails that hold up under real-world imports – are the ones positioned to actually get value out of AI agents and analytics tools built on top of Salesforce, rather than fighting their CRM data every step of the way.
Watch the Full Webinar
The full session includes live demos of DataGroomr’s dashboard, dedupe, and enrichment tools, a deeper walkthrough of FormAssembly’s form validation and pre-fill capabilities, and the complete live Q&A, including guidance on validating non-US phone numbers and how DataGroomr’s dedupe compares to Salesforce’s native duplicate rules.
Curious to learn more?
Explore FormAssembly’s and DataGroomr’s solutions for Salesforce teams.
