As mentioned in my post describing the major business processes that comprise a data governance function, the Measure and Monitor processes i) capture and measure the effectiveness and value generated from data governance and stewardship efforts, ii) monitors compliance and exceptions to defined policies and rules, and iii) enables transparency and auditability into data assets and their life cycle.
The most relevant processes that comprise the Measure & Monitor stage include:
Proactive Monitoring. Proactively monitor data quality or compliance exceptions as they are identified in real time as transactions and interactions are captured, in order to more quickly identify and mitigate critical issues that can cause costly process breakdowns.
- Business and IT stewards alike are responsible for ensuring compliance with data policies, rules and standards, and when necessary are required to mitigate or reconcile a data quality, privacy or security issue. Proactive and reactive monitoring data capabilities provide the visibility stewards need to observe and mitigate any issues.
Data Lineage Analysis. Perform root cause, impact and data lineage analysis of data throughout its lifecycle.
- The ability to reconcile and provide transparency and visibility to the supporting metadata of your most critical data is a foundational element of your data management reference architecture. Data lineage visualization and auditing capabilities also allow data architects and stewards to effectively assess impact analysis of potential changes to data definitions, rules or schemas – as well as root cause analysis capabilities when responding to a data quality or security failure. This capability also provides transparency necessary to support auditability requirements of many regulatory edicts.
Reactive operational DQ audits. Provide data stewards with visibility to reactively mitigate any data quality-related issues routed to them through predefined stewardship workflows implemented in the Apply process stage.
- Business and IT stewards alike are responsible for ensuring compliance with data policies, rules and standards, and when necessary are required to mitigate or reconcile a data quality, privacy or security issue. Proactive and reactive monitoring capabilities provide the visibility stewards need to observe and mitigate any issues.
Dashboard monitoring/audits. Data monitoring acts as an early warning system for catching data quality, security or privacy compliance problems before they wreak havoc on your dependent applications, reports, and processes. Combined with facilities to report on the state of data quality or data security problems, data monitoring ensures the right level of checks and balances are in place to quickly react to changes as needed.
- Themes for these operational metrics include data accuracy, completeness, integrity, uniqueness, consistency, standardization, and audits ensuring compliance with privacy and security policies.
Program Performance. Measure performance of the data governance efforts itself. For example, measure the number of lines of business, functional areas, system areas, project teams and other parts of the organization that have committed stewardship resources or sponsorship. In addition, categorize and track status of all issues that come in to the data governance function, and capture all other types of value-added interactions such as training, consulting and project implementation support.
- An important measure of success is level of engagement, participation and influence the data governance program is having. While these metrics may not demonstrate business value, it will help early stage data governance efforts show progress to its sponsors as drivers work to operationalize data management efforts.
Business Value/ROI. Measure business value from data governance investments ranging across a variety of benefits and can include, among others, reducing penalties by ensuring regulatory compliance; reducing enterprise risk (e.g., contractual, legal, financial, brand); lowering costs (e.g., business, labor, software, hardware); optimizing spending (e.g., procurement, supply chain, services, labor); improving operational efficiencies (e.g., employee, partner, contractor); increasing top-line revenue growth; and optimizing customer experience and satisfaction.
- Data governance efforts will not receive sponsorship, resources, funding or prioritization without a means to measure the value and effectiveness of the efforts.