Glossary · 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.
- CRMBilling91% matched
- CRMHR84% owners found
- ProjectsFinance63% invoiced
Unmatched records are listed, one by one. That is where the red flags are.
In plain terms
Connectivity asks whether your systems can find each other’s records. It carries 14% of the Truth Score™, and it is where the most expensive gaps usually hide.
Most AI plays worth paying for need more than one system. Churn prediction needs the CRM and billing to agree on who the customer is. A collections agent needs invoices, payment terms and contacts in the same place. A revenue forecast needs closed-won deals to have matching invoices.
The pillar measures how many records in one system can be matched to the same entity in another, how many contacts are linked to a company, and whether shared or external IDs exist to do the matching reliably. Where there is no shared ID, matching falls back on names, domains and dates, which is slower and less certain, and the read says so.
Connectivity can only be measured by comparing systems. That is why a Truth Score Read always starts with exports from at least two systems.
For how this gap breaks real projects, read where AI projects actually break: the cross-system gap.
- 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
Data connectivity, answered.
The questions people ask about data connectivity, answered in plain English.
Book the callWhat is a match rate?
The share of records in one system that can be matched to the same customer, person or deal in another. Unmatched records are listed, because they are usually where the red flags are.
We have an integration between the CRM and the ERP. Isn’t that connectivity?
An integration moves data; it doesn’t prove the records agree. Integrations often sync only some objects, or create new records instead of matching existing ones. We check what actually matches.
Why only 14% if it matters so much?
Because its effects also show up in other pillars: unmatched records drag down accuracy, completeness and governance too. The cross-system red flags are reported separately on the memo, so they are never hidden inside an average.
Related terms
Keep reading.
- 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. Read the definition
- 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
- 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
- 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
See how your data scores on connectivity. Measured, not self-reported.
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.