Data Governance via CDE Impact Matrix for Predictive Risk Valuation
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Solution Overview
Problem
Current data governance systems are unable to predictively assess and mitigate the risk of data quality issues before they cause significant impact, often detecting problems after negative effects have occurred, and lack the ability to dynamically calculate or associate damage with specific data elements.
Innovation Solution
Implementing a data governance system that automatically identifies critical data elements, maintains a CDE cost per event matrix to store impact information, and predicts subsequent risks using this data to prioritize and classify criticality, enabling proactive risk management.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional data governance systems are used to detect data quality issues, then problems can be identified, but the detection occurs after negative impact has already been realized
Solution Approach 1:
The system performs preliminary actions by maintaining a CDE cost per event matrix that pre-calculates and stores the impact of data quality issues on critical data elements before actual problems occur. This enables the system to predict potential risks and their business impact in advance, allowing enterprises to take corrective actions before negative effects are realized, thus resolving the contradiction between detection accuracy and detection timing.
2Reliability
If comprehensive data governance is implemented across all data elements, then complete coverage is achieved, but the complexity and resource requirements increase significantly
Solution Approach 1:
The system segments the vast universe of data elements by identifying and focusing only on Critical Data Elements (CDEs) - those specific data elements whose quality directly impacts business events. By segmenting governance efforts to focus only on CDEs rather than all data elements, the system achieves comprehensive coverage of critical areas while significantly reducing the complexity and resource requirements compared to governing all data.
Solution Approach 2:
The system applies local quality by assigning different levels of governance attention to different data elements based on their criticality. Critical Data Elements receive detailed governance and impact analysis through the CDE cost per event matrix, while non-critical data elements receive standard governance. This localized approach ensures reliable governance coverage for critical areas without the overhead of applying the same level of complexity to all data.
3Measurement precision
If reactive data quality monitoring is used, then issues are detected after impact, but proactive risk prevention is not achieved
Solution Approach 1:
The system implements preliminary anti-action by using the CDE cost per event matrix to predict potential data quality risks and their associated business impacts before they materialize. By identifying critical data elements and pre-calculating their impact on business events, the system enables enterprises to take preventive actions against potential data quality issues, thereby reducing or eliminating business impact before it occurs rather than merely detecting issues after impact.
Data Source
AI summary
A data governance method comprises the following steps. One or more data elements associated with an enterprise are identified as one or more critical data elements. A data structure is maintained for the one or more critical data elements. For a given critical data element, the data structure stores information that reflects an impact that the given critical data element had on at least one event associated with the enterprise. The method predicts a risk associated with a subsequent impact that the given critical data element may have on at least one subsequent event associated with the enterprise, wherein the risk of the subsequent impact is predicted using at least a portion of the information stored in the data structure.