What data governance indicators are most effective for identifying data issues?

Uniqueness: Are there duplicates in (or across) my dataset? If there are, why are they there? Did a process not run correctly? What can I do to remediate this? It is important to consider uniqueness because it may tell us about some other processes or problems that are occurring elsewhere in our workflow or in other datasets.

Consider regularly tracking key metrics like accuracy, completeness, and timeliness to detect issues early. For example, aim for 95%+ data accuracy and completeness as a reasonable goal. When designing capture processes, build in validation checks to catch errors. Seek to minimise manual data entry when possible to reduce mistakes. Document your indicators and data quality rules so everyone is on the same page. Involve both IT and business users in deciding which indicators provide the most value. Technical and business perspectives are important. Leverage visual dashboards to make tracking and reporting on metrics easy. While targets help guide quality, remember data issues happen. Focus on continuous incremental improvement vs perfection.

As per Informatica, customer data changes at a staggering 30% CAGR. So every 3 years the customer data will be completely different from the reality. In today's world where hundreds of online applications and systems generate a huge amount of data. A single customer data is stored in all these systems differently which may not be unified, consistent, complete and accurate. Data Governance and Master Data Management can solve this problem and generate a unified, consistent and complete profile of a customer. This not only helps in improving customer experience but also this master data can be used in analytics and machine learning to better understand the customer behavior.

The most effective data governance indicators for identifying data issues include anomaly detection metrics, data quality reports, and compliance monitoring. Anomalies can highlight inconsistencies, while regular data quality reports provide insights into accuracy and completeness. Compliance monitoring ensures adherence to data governance policies, creating a comprehensive approach to identifying and resolving data issues proactively.

On a more preventative note, data issues often stem from inconsistencies in data collection policies and practices across an organization. While different departments may use their data differently, it's important that everyone follows the same data quality guidelines and standards (and that these are documented clearly and accessible to everyone). This also supports better cross-departmental collaboration and helps people trust data platforms. A way to assist in streamlining data best practices is to implement automations where possible to reduce human error. For example, if an external stakeholder is filling out a form, build an integration between the form builder and your CRM to automate data carryover.

Data downtime drills (outage/blackout drills) are helpful in testing downtime processes. It is also helpful to test the ability to access data remotely, and to run drills to assess hardware and software issues. The time to discover that back up systems, software updates, hardware have not been maintained is not during an actual emergency.

Data availability indicators are most effective in ensuring integrity in whatever they are monitoring. Customized data indicators are more effective than ready-made, tool-based ones. Designers of such indicators will understand the levels of criticality of products to the business. Many issues can be avoided if the indicators are set at the right levels. Being proactive with these tools, rather than reactive, can prevent various problems. One example is ServiceNow's ticket monitoring, especially regarding equipment.

Strive for near 100% data uptime by having redundancy built into platforms. Minimise single points of failure. Document SLAs around availability and recovery time objectives. Track performance against those goals. Implement robust backup and disaster recovery processes, testing them periodically. Monitor usage and satisfaction proactively. Survey users and customers if metrics dip to better understand issues. Evaluate whether systems are scaling effectively as data and user volumes grow over time. Availability, speed, reliability all impact usability. Optimising connectivity, queries, access controls improves satisfaction. Make reliability and uptime core design across architecture. Availability enables utilisation enables value.

Data Availability Indicators: Scenario: During a critical project, data unavailability became a bottleneck. Indicator: We established indicators to monitor data accessibility and timeliness. Outcome: This proactive approach enabled us to ensure that required data was consistently available, minimizing project delays.

Downtime to “uptime” drills also essential to process the information and verify the recovered data as well as new data being gathered during this time is not corrupted and can be trusted.

Make data security a priority across teams - it's a shared responsibility between IT, security, legal, and business users. Classify data by sensitivity level and put controls around sensitive data. The most sensitive data should have the strongest protections. Implement 'security by design' principles when developing systems and data pipelines, building in encryption, access controls etc. Continuously monitor access, transfers, and usage of data to detect suspicious activity. Analytics can help here. Establish incident response plans for security events or breaches. Periodically conduct third party audits and penetration testing to validate controls. Educate employees on security policies and data handling. Human errors often compound risk.

Data Security Indicators: Scenario: Security concerns arose due to unauthorized access to sensitive information. Indicator: We implemented security indicators, tracking access logs and permission changes. Outcome: Detecting unusual patterns, we promptly enhanced security protocols, safeguarding sensitive data.

Outcome and Key Results .. business indicators. Data Availability indicators .. or Data Market Place availability and use, where all of the above should be indicated.

In summary, my experience with data governance indicators emphasizes the importance of a multifaceted approach, continuous monitoring, collaboration, and adaptability for effectively identifying and addressing data issues.

Firstly, determine the implications of data accuracy or integrity on the usage. If a high degree is required, then set benchmarks for failure and monitor them. Different uses require different standards and thus different benchmarks are needed.

Comprehensive metadata management stands out as a key indicator, offering insights into data lineage, quality, and usage patterns. Data lineage helps trace the flow of information, pinpointing potential bottlenecks and inconsistencies. Anomalies in data quality metrics, such as completeness, accuracy, and consistency, serve as red flags, signaling potential issues within datasets. Access and permissions monitoring is crucial, providing visibility into who accesses and modifies data, and aiding in the early detection of unauthorized changes.