ISO25010

Sub-characteristic

Metrics

Functionality

· Data coverage: Percentage of enterprise data domains covered by the data warehouse.

· Data freshness: Frequency or timeliness of data updates.

· Data lineage tracking: Ability to trace data from its source to its destination in the warehouse.

Data redundancy: Percentage of redundant data or repeated information.

Reliability

· Data availability: Uptime or accessibility of the data warehouse.

· Data accuracy: Percentage of records without discrepancies when validated against source systems.

· Backup frequency: How often backups of the data are made.

· Backup recovery success rate: Percentage of successful data recoveries from backups.

Usability

· Query simplicity: Average complexity or length of typical queries (can be used to gauge the structure and organization of the data).

· Documentation quality: Completeness and clarity of data dictionaries, ETL (Extract, Transform, Load) process descriptions, and entity-relationship diagrams.

· Metadata quality: Completeness and accuracy of metadata that describes the data.

· User-friendly interfaces: Number of training hours required for new users to proficiently query the warehouse.

Efficiency

· Query response time: Average time taken to execute standard complex queries.

· ETL process time: Time taken for data to be extracted, transformed, and loaded into the warehouse.

· Storage efficiency: Ratio of data storage used to the total storage capacity.

· Indexing efficiency: Time taken to index new data and speed improvements from using those indexes.

Security

· Data encryption: Strength and type of encryption used for data at rest and in transit.

· Access violations: Number of unauthorized access attempts or breaches.

· Audit trail capabilities: Availability and quality of logs for user access and data modifications.

· Data masking: Percentage of sensitive data fields that are masked or anonymized.

Maintainability

· ETL modularity: Ease of modifying or adding new ETL processes without affecting existing ones.

· Schema change frequency: Rate at which the data warehouse schema or structure changes, indicative of stability.

· Change propagation time: Time taken to reflect changes from source systems in the warehouse.

· Deprecation rate: Rate at which old data structures or fields are deprecated or become obsolete.

Portability

· Data exportability: Ease with which data can be exported into different formats or to different platforms.

· Integration capabilities: Number and flexibility of interfaces or APIs available for connecting external systems to the data warehouse.

· Cross-platform compatibility: Ability of the data warehouse to be migrated or to operate across different hardware or software platforms.

· Data format diversity: Number of data formats (CSV, Parquet, Avro, etc.) that the warehouse can natively handle.

Performance

· Load scalability: How well the system performs as the volume of data increases.

· Concurrency: Number of simultaneous queries or operations the system can handle without significant degradation in performance.

Operability

· Monitoring tools integration: How well the data warehouse integrates with monitoring and alerting tools.

· Automated health checks: Frequency and coverage of automated system health checks.