The Many Dimensions of Data Quality

Reporting and decision-making depend upon data, and in particular, data that is of high quality—syntactically and semantically correct, current, and correct in context. No customer likes seeing their name misspelled; an incorrect address can add unwanted delays to invoice payment; an incorrect e-mail address can result in information lost or even sent to the wrong individual.

Enforcing Data Quality at the Source

The most effective way to ensure data quality is to enforce it at the point of entry, for example, by either restricting the types of input allowed or by using validation code in a form.

For example, developers can create Power Apps Forms to create, view, or update data in Dynamics 365. Developers can then connect to schema-restricted tables (for example, if the table column the form is populating only supports “US” or “UK” as valid countries) and/or can add validation scripts to ensure that fields are populated and that their types (for example, numeric-only) or their patterns (for example, a phone number entered as 123-456-7890) are entered correctly and in the format expected by the application.