Metrics for data lifecycle management

Recovery compliance rate: The percentage of recovery operations that meet defined recovery time objectives (RTO) and recovery point objectives (RPO). Improve this metric by regularly testing and optimizing recovery procedures, train teams, and investing in reliable recovery tools. For each recovery operation, determine if both RTO and RPO were met. Calculate the ratio of compliant recoveries to total recovery attempts.

Backup failure rate: The percentage of backup and attempted recovery operations that fail within a given period. This metric provides insight into the reliability of backup and recovery processes. A high failure rate indicates potential issues with the systems, policies, or tools in place and can jeopardize business continuity in the event of data loss or system failures. Calculate this metric by dividing the number of unsuccessful data backups and recovery operations by the total number of successful operations, multiply by 100 to get the percentage.

Data quality score: The combined quality of data in a system, encompassing facets such as consistency, completeness, correctness, accuracy, validity, and timeliness. In the context of data lifecycle management, this score reflects the effectiveness of automated governance and effective data management practices. You may choose to track more granular metrics across multiple systems, such as adherence to data classification, retention, provenance accuracy, and encryption requirements. Derive the data quality score by individually assessing each facet. Then aggregate and normalize them into a single metric, typically ranging from 0 to 100, with higher scores indicating better data quality. The specific method for aggregating the scores may vary depending on the organization's data quality framework and the relative importance assigned to each facet. Consider factors like the uniformity of data values (consistency), the presence or absence of missing values (completeness), the degree of data accuracy relative to real-world entities (correctness and accuracy), the adherence of the data to predefined rules (validity), and the currency and relevance of the data (timeliness).