We Have Expanded! Sixcel Services And Resources have now joined the Newbury Partners’ Family
We Have Expanded! Sixcel Services And Resources have now joined the Newbury Partners’ Family
Bullhorn Data Quality
Identifies and resolves duplicate candidates, contacts, and companies insideBullhorn, preserving all placement history, submissions, and relationshipactivity in a single authoritative record.
Why it works
Years in Staffing Technology
Implementation
Merge with Activity Roll-Up
Methodology
The Problem
Duplicate records are a structural problem, not a cosmetic one. In Bullhorn environments, they accumulate through manual entry, imports, integrations, and recruiter workarounds, until the same candidate exists two or three times, each record holding a different piece of the truth.
Recruiters lose trust in search results and work around the system rather than through it.Reporting becomes unreliable when placement activity is scattered across records, and analytics built on fragmented data produce conclusions no one can act on.
When firms invest in Bullhorn Amplify or other AI tools, poor data quality is the most common
reason the investment underperforms. In our experience across implementation and migration
projects, the scope of the problem is consistently larger than leadership expects.
What the Tool Does
Efficiency gains and margin lift that compound over time.
Identifies matching and near-matching records acrossall three entity types, with detection logic configured toyour data so surface-level differences do not maskreal duplicates.
Before any merging begins, we deliver a detailed analysis of your duplication landscape: record counts, match confidence levels, and a prioritized view of where to focus first. For the staffing firms we have worked with, this analysis alone has changed how leadership understood their data.
A winner record is defined by set criteria: most recent activity, most active placements, most associated contacts. The winner's field data survives. The losing records' field data does not carry over, but all their activity, including placements, submissions, interviews, and assignments, rolls up into the winner.Nothing earned through recruiting work is lost.
Systematic, auditable workflows for reviewing,prioritizing, and resolving duplicates at scale, withoutcreating new data integrity problems in the process.
Why the Merge Methodology Matters
Most deduplication approaches force a binary choice: keep this record or delete theothers. In Bullhorn environments, that is a costly trade-off. A deleted record takes itsactivity history with it.
The winner/loser model solves this. A winner is selected by defined criteria: recency, active placements, contact volume. The winner’s field data becomes the surviving record; the losers’ field data does not transfer, but all their activity rolls up into the winner. Every placement, submission, and assignment is preserved and re-linked, with a complete audit trail of every merge decision.
Step 1
Surface the Duplicates
Step 2
Define the Winner
Step 3
Consolidate and Audit
How It Fits Your Stack
The tool serves as the foundational layer beneath data enrichment, automation, and AI readiness,each of which depends on clean source data to deliver results.
Data Health
Deduplication is the necessary first step beneath Bullhorn Automation’s Data Health pillar. Data Health automates field hygiene, status normalization, and data consistency, but only on clean source data. A fragmented database limits what Data Health can achieve.
Enrichment
Once deduplicated, records are ready for enrichment. CandidateIQ appends current, verified information to candidate and contact records so what recruiters find in Bullhorn actually reflects reality.
AI Readiness
Bullhorn Amplify’s search and match capabilities are only as accurate as the data they operate on. Clients with duplicate-heavy records cannot extract meaningful value from AI tools until the underlying data is resolved, making deduplication a prerequisite for any Amplify deployment.
Who It Is For
Your go-live was years ago and your database has grown organically, duplicates included. The tool gives your team a clear read on the scope and a structured path to resolution.
A new Bullhorn implementation is only as clean as the data migrated into it. Addressing duplicates before migration prevents inherited problems from undermining adoption from day one.
Investing in Bullhorn Amplify, Bullhorn Automation, or third-party AI tools? Your data quality determines your results. Firms that skip deduplication consistently fall short of their own projections.
If you own the health of your Bullhorn environment, this tool gives you the visibility to understand the problem and a systematic process to resolve it.
We run a Duplicate Analysis before any merging begins, so you have a clear picture of the problem before committing to a solution.
Fill in your details and a member of the Newbury Partners team will be in touch soon.
The Future of
Staffing Technology is Here
Newbury Partners has acquired Sixcel LLC to deliver unmatched innovation, expanded capabilities, and AI-driven solutions for staffing firms.
