Data Quality Management

The Entity shall publish its data quality policy following the below guidelines:

• Review the draft policy with the Entity’s Data Committee or equivalent body
• Incorporate Data Committee or equivalent body’s feedback
• Review draft policy for auditability
• Incorporate internal audit feedback
• Seek policy review and guidance from National Data Governance Office and incorporate the provided feedback
• Publish the policy on Entity’s digital channels (e.g. The entity’s website)
• Socialize the policy within the Entity to drive adoption and change management

Based on the initial data profiling results, the Entity shall develop a data quality plan, in alignment with the Entity’s data strategy, that will be used and monitored to implement and activate data quality initiatives across the Entity. The plan shall include, at a minimum:

• List of activities and milestones. The plan should account for quick wins and longer-term strategic changes
• Owners and stakeholders
• Interdependencies, assumptions, and risks
• Implementation timeline and cost

The Entity shall implement a data quality tool aligned with the Entity’s architecture. The tool shall, at a minimum, support the implementation of the following:

• Automated data profiling (i.e.: support continuous analysis of data to understand and detect anomalies)
• Creating the data quality rules (as defined in DQM.2.1.1)
• Conducting data quality assessment by executing the defined data quality rules
• Generate data quality assessment reports containing the list of data quality issues identified

The Entity shall enhance its data quality tooling capabilities to support the following:

• Automated data cleansing and standardization (i.e.: rules for cleansing and standardizing data to remove duplicates, correct the format of a data field, etc.)
• API integration support (i.e.: facilitate integration with external systems through APIs)
• Data quality issue logging (i.e.: to record data quality issues and track workflow)
• Collaboration Features (i.e.: support collaboration and communication among data stewards and stakeholders)

The Entity shall further enhance its data quality tooling capabilities to support the following:

• AI Powered Data Quality Management (i.e.: providing an AI/ML approach to data quality management such as AI-powered rules for automatically generating common data quality rules)

The Entity shall monitor and report on the Entity's data quality based on the defined Data Quality rules and KPIs. This shall include, at a minimum:

• Data quality dashboards to track, analyze, and display data in an aggregated format to gain deeper insight on the overall performance and trends across data quality KPIs
• Data quality scorecard report providing a high-level view comparison of data quality rules with the thresholds, highlighting areas which do not meet the thresholds (e.g.: with red-yellow-green colors)

The Entity shall enhance its data quality monitoring and reporting capabilities as follows:

• Generating process control reports, primarily for use by data engineers. The reports will include custom reports (e.g.: for monitoring quality checks during data processing, based on operational metadata) or quality gate reports (e.g.: for capturing key business KPIs and ensuring data consistency across various data layers in the data warehouse or data lake)
• Generating automated Alerts and Notifications by implementing a system/ dashboard that automatically alerts data stewards and owners when data quality issues are detected or when certain thresholds are breached

The Entity shall establish and implement service-level agreements (SLAs) for business-critical data quality issues. The SLAs shall include, at a minimum:

• Data elements covered by the agreement
• Business impacts associated with data flaws
• Data Quality dimensions and business rules associated with each data element
• Methods for measuring against those expectations
• Acceptability threshold for each measurement
• Steward(s) to be notified in case the acceptability threshold is not met
• Timelines and deadlines for expected resolution or remediation of the issue
• Escalation process when the SLA is not met

The Entity shall enhance its data quality SLAs capabilities as follows:

• Automate the monitoring of SLAs by implementing a system that automatically monitor compliance with SLAs
• Generate automated notifications and actions by implementing systems that automatically trigger emails or notifications to relevant stakeholders if SLAs are breached