Why it shows up in your week
Data integrity is a trust problem before it is a database problem. If teams cannot trust the number, record, or status, every downstream decision gets weaker.
In plain language
Integrity issues show up as duplicate leads, mismatched revenue reports, missing consent records, overwritten fields, broken imports, or workflows that depend on manual spreadsheet repair.
Good integrity work defines the source of truth, validates inputs, records changes, handles failures visibly, and makes reconciliation possible.
Where it bites
Data integrity bites when sales, finance, product, and compliance teams each see a different truth. The cost includes cleanup and lost trust in reports, automation, audits, and customer communication.
What to check
- What is the source of truth for each critical record?
- Where can data be duplicated, overwritten, dropped, or transformed without review?
- Can the team trace who changed a record, when, and through which system?
Common questions
What is data integrity?
Data integrity means data stays accurate, complete, consistent, and traceable as it moves through systems and business workflows.
Why does data integrity matter?
Without it, reports, automation, compliance records, customer communication, and operational decisions are based on data the business cannot trust.
What should you check first for data integrity?
Start with sources of truth, validation rules, duplicate handling, audit logs, failed syncs, manual edits, and reconciliation between critical systems.