What metrics and KPIs should you use to manage data governance risks?

Keeping an eye on data quality, following rules, protecting data, how people use tools, and sticking to policies helps manage risks in data. Checking these regularly finds problems, makes sure rules are followed, and improves how data is handled. Changing plans based on these checks lowers risks and makes data management better over time.

* Data Quality Index: Measure accuracy and reliability of data. * Compliance Tracking: Monitor adherence to data governance policies and regulations. * Incident Response Time: Gauge efficiency in addressing data governance issues. * Data Access Controls: Track and manage authorized data access

-Accuracy Rate: The percentage of data that is correct and error-free across systems. -Completeness Ratio: Measures the extent to which required data fields are populated. -Uniqueness: The degree to which data entries are not duplicated across the database. -Consistency Rate: Ensures data across all systems reflects the same information and is in sync. -Timeliness: Assesses if data is up-to-date and available when needed. -Validity: Checks data against a set of rules or a range of acceptable values to measure compliance. -Integrity: Evaluates the correctness and soundness of data relationships and foreign key connections. -Data Utilization: Tracks how often and effectively the data is used, indicating its relevance.

One thing I've found helpful is the concept of Data Quality Metrics. These metrics are quantitative measures used to assess the accuracy, completeness, consistency, reliability, and timeliness of data. Data Quality Metrics help organizations evaluate the reliability of their data assets, ensuring they meet specific standards and business requirements. Common metrics include accuracy rates, completeness percentages, error rates, and consistency measures. By regularly monitoring and analyzing these metrics, businesses can identify data quality issues, rectify them, and maintain a high standard of data integrity, enabling better decision-making and fostering trust in the data-driven processes.

In most customer contexts, it is often found that the Data quality is defined poorly in the absence of right DQ metrics. The majority of underlying expectation was achieved through Data completeness, Data accuracy, and Data consistency.

Data security metrics are critical for understanding and improving your organization's security posture. From Secureframe's perspective, important metrics include data quality score, data availability percentage, rate of data incidents, and data governance training levels, among others. Almsec Security adds to this by highlighting the significance of tracking intrusion attempts versus actual security incidents, mean time to detect/respond/contain (MTTD/MTTR/MTTC), patching cadence, security audit compliance, and phishing training effectiveness for 2023. These metrics offer a comprehensive view of not just the security of data, but also its quality, availability, and the effectiveness of governance policies.

Evolving Data Governance Metrics for Industry Transitions: From some of my past products/projects, adapting from fashion to health and smart manufacturing industries , data security metrics should encompass health data privacy, machinery data encryption, and incident response times. Industry-specific usability metrics must align with unique stakeholder needs. Smart manufacturing requires scalability and redundancy evaluation, while health data governance focuses on patient privacy and regulatory compliance. Adapting templates ensures ROI optimization by directly addressing sector-specific challenges and objectives. Regular reassessment ensures governance practices remain effective amidst evolving industry dynamics.

Optimizing Data Availability Metrics for Global Industries: In the evolution from fashion to health and smart manufacturing products/industries, customizing data availability metrics is essential for achieving optimal ROI on a global scale. For smart manufacturing, scrutinize scalability, uptime, and redundancy to guarantee uninterrupted access to vital data. In the healthcare sector, prioritize patient data availability while adhering to international privacy regulations. The refinement of templates ensures alignment with industry-specific intricacies, fostering effective risk management strategies. Regular reassessment guarantees that data governance practices uphold an ROI-centric approach in the dynamic landscape of global industries.

Usability metrics are also important and many of them stem, and naturally so, from data quality metrics. We may ask how long is it taking users to complete their report with the new data item than it used to? Do we have data sets that aren’t being used, but are still being processed? That is an area for clean-up, for example. All of these can be measured and evolved into a metric. Data quality methods exist to make data more usable. Data usability metrics can be gleaned from data quality metrics, like timeliness. Is the data updated frequently enough that it is being used enough? If it isn’t there in time it won’t get used and therefore will be of no value. Quality is indeed next to Usability.

As the custodian of data, I would lead the process to comprehend the purpose within its context that facilitate the selection of vital metrics to : 1. Address the gaps in data quality to ensure accuracy, reliability, and relevance. 2. Eliminate extraneous data, aligning with relevant benchmarks. 3. Implement security control compliance with sensitive data protection regulations. 4. Define Data Life Cycle measurements which ensure availability aspects 5. Manage Data Catalogue with designated stewards responsible for data governance, integrity, and accessibility. 6. Facilitate effective decision-making in support of the business strategy. This methodical approach ensures a robust data management framework and required metrics.

Data and Business Intelligence give you the information and ability to make intelligent decisions. Businesses need to define the metrics that help track the overall health of the organization. KPI stands for Key Performance Indicator, used to display the metrics used for the organization's overall health and the targets for business intelligence. But metrics and targets are also a part. BI tools are used to build business intelligence, but they do not provide it by themselves.