Salesforce data quality, deduplication and migration.

Duplicate accounts. Fields half the team stopped filling in. Two systems disagreeing about the same customer. It is the most common thing Ignyto is asked to fix, and the most commonly underestimated — because bad data is rarely a data problem on its own. Fix it properly and your dashboards become trustworthy, your automation starts working, and AI becomes possible. Picture opening a report and not checking it against a spreadsheet first — that is the point of this work. It is also the precondition for AI: duplicate accounts and half-empty fields are the reason most agent pilots stall, not the model.

Why your last data clean-up did not last.

Most organisations have run a data clean-up before. Someone exported to a spreadsheet, merged the obvious duplicates, filled in the gaps, and within a few months it looked much the same again.

That is because duplicates and blanks are symptoms. The causes are upstream: an interface creating records instead of updating them, a form with no matching, a process that does not match how people actually work, and a system users route around because it costs them more than it gives back.

So Ignyto fixes the records and the reasons together. Otherwise you are buying the same project twice.

Trustworthy reporting, working automation, viable AI.

When leadership stops trusting the dashboards they stop using Salesforce, and everything downstream follows. Reporting you cannot rely on is very often a data completeness problem showing up two steps later — which means it is often an adoption problem showing up three.

Automation has the same dependency. Routing, scoring, entitlements and alerts all assume the record is right. Built on data that is not, they generate work rather than remove it.

And AI is the sharpest test of all. An agent retrieving from duplicated records will answer confidently and incorrectly in front of a customer. If you are considering Agentforce, the data assessment is not a prerequisite you can defer — it is the project.

Why does our Salesforce data keep going bad?

Because a clean-up on its own never holds. Data is only as complete as the adoption behind it — if part of the team works around the system, the gaps come straight back within months. Ignyto treats data quality and adoption as one piece of work rather than two.

How do you deal with duplicate records?

In order. Profile what you actually have first, then agree matching rules with the people who own the records, then merge and dedupe safely with a rollback path. Running a dedupe tool before agreeing the rules is how good records get destroyed.

Where do duplicates actually come from?

More often an interface than a person. Web forms without matching, a marketing platform syncing on email alone, an ERP integration creating rather than updating. Fixing the records without fixing the interface means doing it again next quarter.

Can you migrate data from our old system?

Yes. Ignyto has migrated from legacy CRM, spreadsheets, bespoke databases and HubSpot into Salesforce, including in regulated environments where the audit trail of what moved has to hold. Migration is scoped with a data quality gate rather than as a lift and shift.

How does data quality affect AI?

It decides whether AI is viable at all. An agent grounded in duplicated or half-filled data answers confidently and wrongly, which is worse than not answering. Data work is the single largest determinant of whether an Agentforce project succeeds.

What stops it degrading again?

Governance that people can live with: validation and required fields where they genuinely matter, clear record ownership, matching rules on every entry point including integrations, and reporting that surfaces decay early. Then adoption work so the system is worth filling in.