‘Trustworthy data’ is used so often it has stopped meaning anything. Here is what it actually requires.
‘Trustworthy data’ is used so often it has stopped meaning anything. Here is what it actually requires.
Trust is not a feeling about your dashboards. It is a set of properties your data either has or does not.
First, a single source of truth. If two reports disagree on last quarter’s revenue, you do not have a data problem, you have a trust problem, and every decision downstream inherits it.
Second, ownership and definition. Every important dataset should have a name attached to it and an agreed meaning. ‘Active customer’ should mean one thing across the business, not five.
Third, lineage. You should be able to take any number in front of a board and trace it, end to end, back to where it came from. If you cannot, you are asking people to act on faith.
Fourth, controlled access. Sensitive data should be encrypted, access should be least-privilege, and every touch should be auditable. Trust and security are the same conversation.
Trustworthy data is simply data that can survive scrutiny. That is the standard we build to, and the standard we hold our own work to.