The Candidate Database Problem
Freelance recruiters live and die by their candidate databases. But spreadsheet-based candidate databases are almost always a disaster: duplicate entries, inconsistent status labels, missing contact info, and outdated notes mixed with current ones.
A messy database means missed follow-ups, duplicate outreach, and a reputation for disorganization. Cleaning it properly — and keeping it clean — is a competitive advantage.
The Most Common Candidate Database Errors
Duplicate candidates: The same person appears twice — once from a job board import and once from a referral. You reach out twice. It's embarrassing.
Sohovi automatically finds every duplicate in your dataset — including near-matches — and shows you exactly which rows are affected.
Inconsistent status labels: "Interviewed", "Interview Done", "Interviewed - Round 1", "Phone Screen Complete" all mean something similar but are stored as four different values. You can't filter by status reliably.
Mixed name formats: "John Smith", "Smith, John", "JOHN SMITH" — all one person, but Excel won't know that.
Outdated records with no date stamps: You have 500 candidates but no idea which ones were added in the last 6 months versus 3 years ago. Sourcing dates are critical.
Missing or invalid emails: An email column with "not provided", "N/A", blank cells, and malformed addresses mixed together.
A 5-Step Cleanup Process
Step 1: Freeze the data
Export your current database to a CSV. Never clean the live version — always work on a copy. Name it: CandidateDB_Cleanup_2026-05-31.csv
Step 2: Standardize status labels Create a controlled vocabulary: Sourced, Screened, Submitted, Interviewing, Offer, Placed, Rejected, On Hold. Do a find-and-replace to map all variants to these canonical values.
Step 3: Find and merge duplicates Sort by last name, then by first name. Scan for consecutive entries that look the same. For confirmed duplicates, decide which record is canonical, copy any unique information from the duplicate, and delete it.
Step 4: Validate emails Filter the email column. Remove anything that doesn't have an @. Flag anything with obvious typos (gmial, yhaoo). For missing emails, mark as "Email needed" rather than leaving blank.
Step 5: Add or verify date stamps Every record should have at least a "Date Added" field. If historical records lack dates, add a placeholder ("Before 2024") rather than leaving it blank. Sort by date descending to verify your current pipeline is at the top.
Keeping It Clean Going Forward
A database you clean once will get dirty again within months without a maintenance system:
- Intake standard: every new candidate entry must include: full name, email, current company, date added, status
- Monthly review: 30 minutes once a month to scan for status inconsistencies and obvious duplicates
- Duplicate prevention: before adding a new candidate, search by email first
These habits are far cheaper than periodic big cleanups.
