Glossary

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Glossary / Evaluation and implementation guide

Data Integrity

Data integrity is the assurance that records remain complete, accurate, and uncorrupted as they move through integrations.

It covers structural soundness (no truncated fields, no broken references), uniqueness (no accidental duplicates), and traceability (you can tell where a change came from).

It is broader than consistency: a system can be internally consistent yet hold wrong or lost data.

A practical example

Example: a two-way sync between a CRM and an email platform creates a second contact record because the tools match people on different keys — email in one, external ID in the other — splitting one customer's history in two.

What to evaluate before investing

  • Ask how the vendor matches records across systems (email, ID, fuzzy matching) and what happens on ambiguous matches.
  • Test a round-trip: create a record, sync it out and back, and verify no fields were truncated, re-typed, or dropped.
  • Check whether the platform keeps an audit trail showing the origin of every field change on a synced record.

Limitations and tradeoffs

Integrity safeguards add processing overhead and can slow syncs; a vendor promising both real-time speed and heavy validation on every record is making a tradeoff you should see quantified.

Plan your next step with MeshLine

Connect this decision to your automation, organic marketing and customer lifecycle management. In a MeshLine demo, discuss your existing tools, the scope you need and how to measure the result.