Glossary

Data quality

The degree to which data are accurate, complete, consistent, timely, valid and unique.

The degree to which data are accurate, complete, consistent, timely, valid and unique. It is a precondition for automation: an automated decision on poor data is not automation, it is operational risk at speed.

It is a design condition, not a clean-up campaign. Quality is sustained at the point of capture — validations, domains, state-based mandatory fields — because cleaning afterwards only fixes the past.

Related terms

Data as a business asset

The view of data as a resource that enables operating, deciding, controlling, measuring and learning, and which therefore demands its own policies, roles and management standards, like any…

Data owner

The role accountable for the definition, quality, use, integrity, security, lineage and evolution of a critical entity.

Intelligent document processing

Extracting, classifying and validating information from unstructured documents so that business processes can act on it.

Mandatory by state

A rule defining which data are required at each point of the process, rather than demanding everything up front.

Organizational adoption

The extent to which people actually use the designed process, capture data with quality and use indicators to decide.

Purposeful data

The rule requiring every captured data item to declare its function: execute, decide, comply, evidence, measure or integrate.

Referential integrity

The condition ensuring relationships between entities remain valid and free of broken references.

Rework

The repetition of work already performed because of errors, incomplete data or returns.