The Data Debt Problem
Every business has data quality issues. Duplicate records. Missing fields. Outdated information. Inconsistent formats.
These problems start small but compound over time. And they make every other initiative harder.
How Bad Data Spreads
It starts innocently:
- 1A sales rep creates a quick lead without filling all fields
- 2Another rep creates a duplicate because they couldn't find the first record
- 3Marketing imports a list with slightly different field conventions
- 4An integration syncs data that doesn't match your format
Now multiply this across months or years. What started as minor inconveniences becomes a systemic problem.
The Real Costs
Lost Productivity
Your team wastes time searching for records, deduplicating contacts, and reconciling conflicting information. This isn't visible on any report, but it's very real.
Bad Decisions
Reports built on bad data lead to bad decisions. If your pipeline numbers are inflated with duplicates, you're planning capacity wrong.
Integration Failures
When you try to connect Salesforce to other systems, bad data causes sync failures. You end up building workarounds instead of solving problems.
Customer Experience
When reps can't find customer history or have conflicting information, customers notice. It erodes trust.
Fixing the Problem
1. Establish Standards
Define clear rules: How should names be formatted? What fields are required? What values are valid?
2. Enforce at Entry
Use validation rules and required fields. Make it harder to create bad data than good data.
3. Clean Existing Data
This is the hard part. Tools can help, but there's always manual review involved. Prioritize by impact — start with the records that matter most.
4. Monitor Continuously
Set up reports that flag data quality issues. Review them regularly. Don't let debt accumulate again.
The Integration Opportunity
Here's a silver lining: when you build integrations, you have a chance to enforce data quality as data flows between systems. Smart integrations can validate, clean, and standardize data automatically.
A Discovery Sprint identifies not just automation opportunities, but data quality issues that need addressing before you can automate effectively.