4.4. Fit-for-Purpose Name: Fit-for-Purpose Statement: • Information and Data are Fit-for-Purpose Rationale: • Information and Data are suitable and of the appropriate quality for the intended purpose, as defined by the business programs and tested as close to the source as possible. Implications: • Often assumed to be a 100% data quality requirement and not the pragmatic reality. • Approach should be to get the data ‘right’ for the need, as defined by the responsible party. • Management and architecture of the information / data must support not only operational needs but also and analytical / research and marketing requirements, otherwise the data has no value in bettering program outcomes. • Information and Data quality must be tested and remediated as close to the source as possible. • Information and Data quality will degrade over time, even if nothing ‘happens’ to the actual data, therefore validation of its quality must be performed as an ongoing function, not one-time efforts and monitoring and reporting on quality metrics must be part of everyday operations. • Data quality functional expertise (i.e., center of excellence) can establish a standardised framework and support to business programs in their quality validation processes. • Dimensions of quality utilised to validate the appropriateness and usefulness of the data will vary by necessity (i.e., uniqueness, validity, completeness, accuracy, etc.). • Business purpose of data must consider upstream and downstream uses, and not just the immediate use. • Information and data repositories may need differing levels of quality if utilised for research versus operational needs. References: • CDO