Monitor Data Quality Through Governance and Stewardship
Learning Objectives
After completing this unit, you’ll be able to:
- Explain why data monitoring is essential to maintain data quality over time.
- Identify the types of monitoring solutions that can support your organization’s data quality journey.
This module was produced in collaboration with Dreamin’ in Data, a nonprofit and part of the Datablazers community. Learn about partner content on Trailhead.
If you have taken Data Management Fundamentals, you learned that data quality management is a multistep process. In the Salesforce data quality management framework, data monitoring is the final step before cycling back to data profiling.
As you work through the data quality management process, you might find yourself moving back and forth between steps. This is expected. New insights often lead you to revisit earlier work, repeat activities, or adjust your approach. Use this process as a guide, not a fixed sequence.
Why Data Monitoring Is Essential
Data monitoring helps ensure data remains accurate, consistent, and fit for business use. This is necessary as processes, users, systems, and metadata change over time. Changes can gradually introduce data drift, where data or configuration slowly evolves in ways that reduce quality, consistency, or business value. Over time, this erodes trust in the data and the processes that depend on it.
By continuously monitoring data and metadata, organizations gain early visibility into these changes and can respond before operations, analytics, automation, or AI are affected.
Support Data Governance and Stewardship
Data governance is the structured approach to managing data so it remains trusted, secure, usable, and compliant. It establishes the policies, standards, processes, and accountability that define how data should be managed and what quality expectations it must meet.
Data stewardship puts governance into practice. Data stewards monitor data quality, investigate issues, coordinate remediation, and help ensure data continues to meet business and governance expectations as systems and business processes evolve.
Data monitoring sits at the intersection of the two, supporting both governance and stewardship by measuring how data and configuration metadata change over time and whether they continue to meet defined quality, business, and adoption expectations. Monitoring provides the evidence needed to detect meaningful changes, assess their impact, and determine whether corrective action is required.
Monitoring makes these changes visible so data stewards can take action. It also reinforces accountability by clarifying who is responsible for reviewing data health and responding when issues are detected.
Want to learn more about data governance? Check out the Guide to Data Governance.
Monitoring Solutions That Support the Journey
There is no single way to monitor data quality. Organizations typically use a combination of approaches, depending on risk, scale, and maturity.
Monitoring solutions can include:
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Field history and audit logs to understand how and when data or metadata changes
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Reports and dashboards to review values at a point in time and spot emerging issues
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Automated data profiling to capture snapshots over time and detect trends in completeness, value distributions, or usage patterns
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Targeted reviews and stakeholder feedback to validate whether data remains fit for purpose
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Flow and automation to trigger alerts or actions when impactful deviations are detected
Together, these solutions provide different levels of visibility and support an incremental approach—starting small and expanding monitoring as needs grow.
NTO’s Journey to Data Governance
After completing a comprehensive data readiness project for the upcoming Case Deflection AI agent, Luna was asked to ensure that Northern Trail Outfitters (NTO) wouldn’t need to repeat such an extensive cleanup effort again.
Luna partnered with business and technology stakeholders to start a lightweight data governance and stewardship team. They began with regular assessments of a small set of business-critical fields, focusing on whether the data remained reliable and fit for purpose for the people and processes that depend on it.
To make monitoring actionable, the team established a simple operating rhythm.
- Use reports to provide point-in-time evidence of current data health.
- Use automated monitoring and alerts to detect data drift early and respond quickly.
Over time, these practices created shared accountability and a scalable foundation for governance. It supports service agents today while raising the reliability bar needed for future automation and agentic experiences.
Unit Recap
Monitoring transforms data quality from a series of projects into an ongoing governance practice. By establishing clear expectations, controls, and stewardship responsibilities, organizations can sustain data health as business needs evolve.
In the next unit, explore how monitoring must account for business purpose, persona needs, and visibility.