Clarity
The ease with which data consumers can understand the metadata.
- •Faster and more accurate data interpretation, analysis, and usage by all stakeholders.
- •Reduced ambiguity and miscommunication regarding data meaning, lineage, and business rules.
- •Improved data governance, easier onboarding for new data users, and effective self-service analytics.
- •Increased confidence and trust in data, leading to more data-driven decisions.
- •Misinterpretation of data leading to incorrect conclusions, flawed strategies, and poor business decisions.
- •Wasted time, resources, and duplicated effort trying to understand poorly defined or undocumented data.
- •Inconsistent data usage and reporting across the organization due to varying interpretations.
- •Reduced trust in data and analytics, hindering adoption of data-driven culture.
Grade
A grade (1-10)
Logistics: Metadata clearly defines 'Dwell Time' calculation, including source systems, specific filters applied, and the unit of measure (e.g., hours).
Finance: The business glossary clearly defines 'Customer Lifetime Value (CLV)' including the formula, data sources, and assumptions.
Scientific Research: Metadata for a climate dataset thoroughly documents each variable, its units, collection methodology, and any known data quality issues.
Logistics: Data dictionary definition for 'TEU Factor' is ambiguous, missing, or uses highly technical jargon not understood by business users.
Finance: The metadata for a financial report column 'AdjNetRev' does not explain what adjustments were made or the source of the net revenue figure.
Scientific Research: A dataset's metadata lacks clear descriptions of experimental variables or the units of measurement used.