How do you clean your CRM data so AI actually works?

You notice the Superbrain's recommendations aren't landing. The next best actions feel off. The deal velocity analysis doesn't match your gut instinct. The issue isn't the AI. It's the data underneath.

Half your contacts are missing job titles. You have three records for the same person under slightly different email addresses. A contact's company field says 'ABC Inc' in one place and 'ABC Incorporated' in another. The Superbrain is trying to score a relationship without accurate information, so its recommendations are inherently weak.

This is the unglamorous work that most consultants skip: data cleaning. But it's the prerequisite for every AI feature working well. You can't get good Superbrain recommendations if the contact records are incomplete. You can't spot patterns in your deals if they're misfiled. You can't trust Authority Score if the contact's company information is wrong.

The Problem

Relationship data degrades over time. Contacts join companies and their old email addresses are outdated. People move roles and their job titles become stale. Duplicate records get created when the same person signs up twice with slightly different names or email addresses. Over months and years, this accumulates into a messy database that's more work to maintain than it was worth to build.

Most consultants accept this as inevitable. 'My system is a mess, but I know the important contacts' is the refrain. The problem is that knowledge lives in your head, not in the system. The moment you want to use AI to scale that knowledge, the messiness becomes a liability.

Superbrain can't reason well about incomplete data. If a contact is missing company information, it can't calculate Authority Score. If you have duplicates, Nynch might surface the same person twice in your Connection Queue. If deal information is wrong, deal analysis becomes noise.

Cleaning data feels like unpaid work. There's no immediate revenue impact. But the payoff compounds: a cleaner database means better AI recommendations, which means better decisions, which means more revenue over time.

How Nynch Solves It

Nynch includes tools to find and fix the most common data quality problems. A duplicate finder surfaces records that are likely the same person. A data completeness analyzer shows you which fields are missing or stale. An enrichment tool fills gaps (company name, job title, LinkedIn profile) so the Superbrain has complete information to reason from.

You're not doing the cleaning manually. Nynch spots the problems, suggests the fixes, and you approve them in bulk. Ten minutes of data cleanup can fix issues that were dragging down AI recommendations for months.

See Finding and Fixing Duplicates for step-by-step duplicate resolution.

How It Works in Nynch

Duplicate Detection

Nynch scans your contact database and finds records that are likely duplicates: same name with slightly different email, same email with slightly different names, contacts at the same company with overlapping job titles.

For each potential duplicate, you see both records side by side with the matching confidence level. You decide: merge them, keep both (they're actually different people), or ignore the suggestion.

Merging is smart: Nynch combines the data from both records, keeping the most recent information and consolidating activity history. All deals and activities stay intact.

Data Completeness Scan

Nynch identifies which of your contacts are missing critical information: job title, company, email, LinkedIn profile. You see a report of the gaps and can bulk-fill them with enrichment data.

The enrichment is optional. You can review before applying any changes.

Authority Score Foundation

Before you can trust Authority Score, your company data has to be clean. Nynch's data quality tools ensure your contacts' company associations are accurate and consistent. Mismatches get flagged (is this person's company field right if their email domain doesn't match?).

Clean company data is the foundation for Authority Score calculations. When that's solid, the score becomes meaningful.

Ongoing Monitoring

Nynch continuously monitors for new data quality issues. When duplicates are created or fields go stale, you get a notification. This prevents data from degrading again after you've cleaned it.

Pro Tips

• Do one data cleanup pass when you first start using Nynch. Set aside 30 minutes, run the duplicate finder, merge obvious duplicates, bulk-fill missing job titles. That single session will immediately improve Superbrain recommendation quality.
• Focus on critical fields first. Job title, company, and email address matter most for AI recommendations. Those are the fields to prioritize when cleaning. Secondary fields like phone number or location can be updated gradually.
• Use enrichment to fill gaps, not to import new contacts. Enrichment is for completing records you already own. Don't use it to bulk-import contacts you're not actually tracking. That creates noise in your database.

See Understanding ICP Scores for how clean company data improves ICP matching.

FAQ

Q: If I merge two duplicate contacts, what happens to their activities and deals?

A: All activities and deals stay intact. Nynch merges the records but keeps all the history. You get a unified timeline of all conversations with that person and a consolidated list of deals they're involved in.

Q: How does Nynch find duplicates if the names are very different?

A: Nynch first matches on email address and company. If the same email appears on two records, that's a duplicate. If the same company domain appears with the same person name, that's a duplicate. It's conservative: it flags potential duplicates, but you make the final decision.

Q: Will cleaning my data slow down Nynch?

A: No, it speeds it up. A cleaner database means faster queries, better AI recommendations, and more reliable deal analysis. The performance gain is immediate.