Completeness (Metadata)
The degree to which the metadata are fully described.
- •Thorough understanding of data assets, including definitions, lineage, business rules, and ownership.
- •Facilitates effective data discovery, self-service analytics, and data governance.
- •Reduces reliance on tribal knowledge and accelerates onboarding of new data users.
- •Builds trust in data as its context and meaning are fully documented.
- •Difficulty in finding, understanding, or trusting data due to missing or inadequate descriptions.
- •Increased risk of data misinterpretation and misuse.
- •Inefficient data management and governance efforts.
- •Slower development of new analytics, reports, or AI models due to lack of data understanding.
%
Logistics: Metadata clearly defines 'Dwell Time' calculation, including source systems, specific filters applied, the unit of measure, and business owner.
Finance: A data catalog provides comprehensive metadata for all key financial metrics, including business definitions, technical lineage, data steward, and update frequency.
Data Science: Each dataset in the repository is accompanied by metadata detailing its schema, data types, descriptions of variables, collection methods, and known limitations.
Logistics: Data dictionary definition for 'TEU Factor' is ambiguous or missing crucial details like how different container sizes are converted.
Finance: The metadata for a financial report lacks information on the source systems for each data element or the last refresh date.
Data Science: A dataset provided for analysis has no metadata describing the meaning of column headers or the range of possible values.