Services
RevOps
Data Quality & Dedup Program
Identify, quantify, and resolve data quality issues across your CRM including duplicates, incomplete records, and inconsistencies.
Foundation
Duplicate records are the visible symptom. The underlying problem is the import practices, integration logic, and data entry workflows that create them continuously. A one-time deduplication run without addressing those sources produces a clean database that's back to its previous state within six months. This engagement fixes both: the current state through a structured cleanup, and the ongoing state through governance rules that address the actual causes.
Duplicate records are the most visible data quality problem. But they're usually a symptom, not the root cause. The root cause is the import practices, integration logic, and data entry workflows that create duplicates faster than any one-time cleanup can resolve them. A deduplication run without addressing the source produces a clean database that degrades back to its previous state within six months.
Beyond duplicates: inconsistent field values, blank required fields, records with conflicting information across objects, and property values that were accurate at creation but have since become outdated. Each of these creates noise in every report and analysis built on top of the data.
What we do
We run a full data quality audit to quantify the scope and distribution of quality issues across your CRM. We identify the sources creating duplicates and inconsistencies, build a deduplication strategy with merge logic specific to your data model, and execute the cleanup. Critically, we also build the governance rules that prevent recurrence: import standards, integration checks, and data validation workflows.
For context on what this type of analysis typically surfaces, read what data quality issues are actually costing you.
Deliverable
A data quality report with issue quantification and source analysis, a deduplication execution plan with merge logic documentation, post-cleanup validation, and a data governance framework with ongoing standards your team can maintain.
Outcome
A clean database and a plan for keeping it clean. Reports that don't require manual reconciliation. Segmentation and automation that work because the underlying records are accurate. And a team that trusts the data they're working with.
How Veloxa Stopped a Leaky Funnel and Grew Conversions by 22% — recovered $1.2M in pipeline, cut CPL by 40%, and grew conversions by 22%.
See how it worked in practice: Meridian Health reduced their sales cycle by 35%.
Best Fit
For any company that has been in operation for more than two years, has imported data from multiple sources, or has gone through any significant team or system change. If your current deduplication approach is periodic manual runs rather than ongoing prevention, this engagement will fix the structural problem rather than the surface symptom.