Glossary · Method
Cross-system reconciliation
Cross-system reconciliation is matching records between systems, such as CRM and billing, or HR and the CRM, to find where they disagree. Every system can look clean on its own; the truth is in the gaps between them.
- 2,176matched in both systems
- 117active in CRM, no billing account
- 86duplicates inside the CRM
- 41in billing only
Each system looks clean on its own. The gaps only show when they are compared.
In plain terms
Accountants reconcile a bank statement against the ledger. Cross-system reconciliation applies the same idea to the rest of the business: match the records in one system against another, and list where they don’t agree.
Every system was built to be right about one thing. The CRM is right about the pipeline, billing about invoices, HR about people. None was built to agree with the others, and so each can look clean on its own while the gaps between them grow.
Typical gaps include customers active in the CRM with no billing account; closed-won deals with no matching invoice, which is a traceability gap rather than proof of lost revenue; accounts still owned by people who have left; billable project hours never invoiced; and board-pack figures the ledger can’t reproduce.
These gaps matter for AI because most AI plays worth paying for read across systems. A churn model, a collections agent or a revenue forecast inherits every disagreement between the systems it reads.
It is the core of the Truth Score Read: exports from two or more systems, compared record by record, with every red flag checked by hand before it reaches you.
- See it in a real memoThe sample Truth Score ReadReal engine output on generated test data, not a client
- How we measure itThe method, including the capPillars, weights, bands and what is never guessed
- What happens to your exportsSecurity and data handlingExports only, NDA and data processing agreement first
Questions
Cross-system reconciliation, answered.
The questions people ask about Cross-system reconciliation, answered in plain English.
Book the callWhich systems can be reconciled?
Any two that record the same customers, people, projects or invoices and can export to CSV or Excel, including spreadsheets.
Is a closed-won deal with no invoice lost revenue?
Not necessarily. It is a traceability gap: the deal may have been invoiced under another name, cancelled or never recorded properly. The finding is that nobody can show the link, and the fix list says how to trace it.
How many systems do we need to send?
Two are enough to start. Add the systems the AI decision depends on; we say which on the first call.
Related terms
Keep reading.
- Method Entity resolution Entity resolution is working out which records, in one system or across several, refer to the same real customer, person, supplier or deal, even when they are spelt differently. Read the definition
- The six pillars Data connectivity Data connectivity is whether records link up across systems: whether a customer in the CRM can be matched to the same customer in billing, and a person in HR to the accounts they own. Read the definition
- The Truth Score Truth Score™ The Truth Score™ is DataTruth’s score out of 100 for how far a company’s data can be trusted, built from six weighted pillars and placed in one of four bands, A to D. Read the definition
- AI readiness Data quality Data quality is how far data is fit for the job it is about to do: correct, filled in, consistent, owned, connected across systems and traceable to its source. Read the definition
Definitions are the easy part. Your own number is the useful one.
Exports from two or more systems, matched between them, every red flag checked by hand. A signed memo with your Truth Score and a Leverability read, within 5 working days of complete exports.
Starts with a 30-minute call. Commits you to nothing.