Your CRM is full of garbage. Duplicate contacts named 'John Smith' and 'john smith' sit three rows apart. Companies have three spellings across your database. Email addresses are missing. Phone numbers point to the wrong person. Sales reps skip fields entirely because nobody enforces them. And when you try to run a forecast or an automation, half your pipeline falls through the cracks. This isn't a one-time problem—it's structural. But it is fixable, and it doesn't require a months-long migration or a consultant. Here's how to audit, clean, and lock down your CRM data in 30 days. Week 1: Audit and Measure Before you fix anything, you need to know what's broken. Spend your first week measuring the damage. You can't motivate a team to clean data without showing them the scale of the problem. Days 1–2: Map Your Contact Universe Export your full contact list (or work directly in your CRM's reporting view) and count total records. This is your baseline. Sort by creation date. Older records are usually dirtier. Note which year saw the biggest influx—usually a sales push or platform migration. Segment by company. Count how many companies are represented and how many contacts sit in each. Top-heavy customer bases (one contact per company) are often missing decision-makers. Check completeness by field. Pull a report showing % fill rate for: email, phone, job title, company, owner. Anything below 85% is a red flag. Days 3–4: Find Duplicates Manually First Most CRMs have deduplication tools, but they miss soft duplicates—same person, different spellings or missing middle names. Before automation, find the obvious ones by hand. Search for common names: 'John', 'Mike', 'Sarah'. Count how many sit in your top 10 companies. Sort contacts alphabetically by last name + first name. Obvious duplicates (same person, slightly different spelling) will cluster. Search for partial email addresses: anyone with an @gmail or @yahoo that might be a personal account for a business contact. Look for contacts with no company assigned. These are usually data entry errors or test records. Days 5–7: Document Required Fields and Owners Define what 'clean' means for your business. Not every field matters to every team. For sales: company, email, phone, job title, decision-maker indicator (yes/no). For marketing: email, company size, industry, preferred contact method. For customer success: company, primary contact email, account owner, contract end date. Assign one owner per contact. If multiple reps have touched a contact, assign it to whoever owns the relationship or account. One owner, one source of truth. Document these requirements in a shared doc. Share with the team. Get buy-in. Key insight: Don't make the list too long. Every required field you add is a field your team will skip under pressure. Pick 4–6 fields per contact type. Everything else is nice-to-have. Week 2: Deduplication Most CRMs have a dedup tool. Use it. But it's a blunt instrument—it catches exact matches, not soft duplicates or data quality issues. Here's the process. Days 8–9: Run Your CRM's Native Deduplication If you're using Orin's CRM , Salesforce, HubSpot, or Pipedrive, start with the built-in dedup tool. Set it to: Match on email address first. This catches most obvious duplicates. Review matches before merging. Don't auto-merge. A contact with two email addresses (work + personal) is common and shouldn't be collapsed. Prioritize: keep the record with the most complete data. Merge secondary records into the primary. Don't lose phone numbers or job titles during the merge. Log what you merge. You'll need this to explain changes to the team. Days 10–11: Handle Soft Duplicates by Hand These are the hard ones. Same person, different spellings. Same company, different domain formats. No tool catches these perfectly. Names: 'Robert Smith' and 'Bob Smith'. 'Jennifer' and 'Jen'. Search by first name + phone. If phone matches, merge. Emails: Same person with work + personal email. Same email, typos ('example.com' vs 'exampl.com'). Cross-check by phone and company. Company names: 'Acme Corp', 'Acme', 'ACME Corporation', 'Acme Corp Ltd'. Standardize the master name, link variants as duplicates. Use a three-person team (one person calls them, one verifies, one merges). Speed > perfection. Aim for 50–100 merges per day. Days 12–14: Delete Dead Records Don't merge everything. Delete what's clearly garbage. Spam: anyone with an obviously fake email (test@test, asdf@asdf, etc.). Test records from old product demos or migrations. Usually easy to spot by creation date or no activity in 3+ years. Competitors researching your product (check the company name + email domain). Contacts with no email, no phone, no company, and no activity in 2+ years. Not worth keeping. Aim to delete 5–15% of your database. If you're not deleting anything, you're not being aggressive enough. Week 3: Fix and Standardize Now that you've merged and deleted, fix the data that remains. Days 15–17: Fill Miss