Blog
Data quality 101 for Salesforce and Marketo users.
Salesforce Data Quality Has Two Major Problems: Identity and Reachability
Ask a Salesforce admin what "bad data" means, and you'll usually get the same answer: duplicates. Two contact records for the same person. Three accounts for the same company. It's the problem everyone names first because it's the one you can see, right there in a list view, staring back at you.
Why Your Salesforce Duplicate Rule Is Creating Duplicates You’ll Never Find
Imagine you're a rep, doing the part of the job you love. Prospecting.
The Upstream Approach: Using Smart Forms to Prevent Duplicates from Being Created in the First Place
Most Salesforce teams spend the bulk of their data-quality effort on finding duplicate records and merging them one batch at a time. That work matters, but if duplicates keep entering your org faster than you can merge them, it will always feel like you’re fighting a losing battle.
How to Choose Salesforce Deduplication Tools in 2026
If you have an established Salesforce org, you already have duplicate records. That's not a criticism—it's just how CRM data works at mid-market scale. The real question isn't whether duplicates exist. It's whether you have a consistent, automated way to find, merge, and prevent them before they erode your reporting, slow down your sales team, and undermine AI-driven workflows.
What Agentforce Actually Requires From Your Salesforce Data
Salesforce data quality used to be an admin chore — something you got to when reports looked off or a merger dumped 40,000 duplicate leads into your org.
What Salesforce Teams Really Think About AI in Data Management
Teams are testing AI agents, automation features, and new ways to reduce manual work. It is natural that data management is part of that conversation. If AI can help identify duplicates, flag incomplete records, or support cleanup tasks, many admins would welcome it.
Can You Trust AI With Your Salesforce Data?
AI is rapidly becoming a priority for Salesforce leaders – but most conversations start with features, not fundamentals. Before organizations can scale AI, they’re running into a more difficult question: can we actually trust the data that it’s working with?
