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. |