Data Governance Tools & Practices That Improve Data Quality
Bad data slows your business down. It creates operational friction, reduces confidence in reporting, and can even damage your reputation. But fixing errors after the fact doesn’t solve the real issue.
The real solution is proactive: strong data governance tools and practices that sustain data quality over time. When governance is built into your data strategy, your organization unlocks higher trust, faster decision-making, and better performance across teams.
In this blog, we’ll cover:
How data governance directly improves data quality
Best governance practices to maintain clean data
Top governance tools that support data quality
Common governance mistakes to avoid
A case example of governance improving real-world data quality
How Data Governance Directly Improves Data Quality
When teams don’t know where data came from, how it was created, or how it’s used, poor decisions happen. Data governance helps prevent this by establishing structure and accountability around data.
Here are four ways governance drives better data quality:
1) Clear Data Quality Standards & Policies
Governance defines what “good data” means — accuracy, completeness, consistency, timeliness, and validity. These standards become benchmarks for continuous improvement.
2) Defined Ownership and Accountability
Governance assigns responsibility: data owners, stewards, and custodians. This ensures issues don’t get ignored and quality is actively maintained.
3) Structured Improvement Programs
Governance supports disciplined processes like profiling, validation, cleansing, and prevention controls to reduce recurring errors.
4) Monitoring, Reporting & Alerts
Governance introduces continuous tracking of data quality — dashboards, anomaly detection, audits, and reporting so teams can fix problems early.
Together, these governance levers create consistent and reliable data across the organization.
Best Practices to Improve Data Quality Through Governance
To build a strong and sustainable data quality program, focus on these best practices:
Start with the current state
Run data profiling to identify inconsistencies, missing values, duplication, outliers, and broken logic. This baseline helps measure progress.
Standardize what “quality” means
Define measurable standards for:
Accuracy
Completeness
Consistency
Validity
Timeliness
Ensure the standards are documented and understood across teams.
Use the right tools + build workflows
Tools help, but only when paired with proper processes. Implement workflows for:
Regular validation checks
Scheduled cleansing
Issue logging and resolution
Assign data stewards and owners
Data quality improves when responsibility is assigned to specific roles — not “everyone” (which becomes no one).
Define KPIs for quality
Track quality using clear metrics such as:
% missing values
Duplicate rate
Exception rate
SLA compliance
Pipeline failure rate
Automate monitoring wherever possible.
Train people and create awareness
Educate teams about:
Why quality matters
How bad data impacts cost and decisions
How to follow governance rules
Treat governance as continuous
Governance isn’t a one-time project. Review standards regularly, improve rules, and adapt as business needs change.
Encourage business + IT collaboration
Data quality requires alignment between data producers and data users. Create shared rules, shared definitions, and shared ownership.
Top Data Governance Tools That Support Data Quality
Technology alone can’t fix poor data — but the right tools make governance scalable and measurable. Below are strong options commonly used to improve quality and maintain governance.
Data Platforms with Built-in Governance Capabilities
Databricks: Supports embedded quality checks in pipelines and centralized governance through cataloging and lineage capabilities.
Snowflake: Strong for managing data integrity and monitoring within the warehouse environment.
Enterprise Governance + Data Quality Tools
Informatica: Strong for profiling, cleansing, governance cataloging, and integration across enterprise systems.
Collibra: Combines governance, quality visibility, workflows, and stewardship accountability.
DEMS (Data Excellence Management System): Rules-driven governance and continuous validation tied to business outcomes.
Analytics Governance Tools
Tableau (server/cloud governance features): Supports metadata tracking, quality warnings, lineage, and secured data access.
Looker: Encourages “single source of truth” metrics, governed models, access control, and reusable business logic.
Sigma: Enables governed access to cloud warehouses with centralized logic and live queries to reduce reporting inconsistency.
Governance in the Analytics Engineering Layer
dbt: Supports testing, documentation, lineage, and enforcement of data contracts, improving quality within transformation pipelines.
Popular Open-Source Data Quality Tools
If you want open-source governance-friendly options:
Deequ: Scalable data validation for large datasets and automated reporting
Great Expectations: Human-readable rules and documentation + validation checkpoints
Soda Core: Data quality checks embedded into pipelines with monitoring and alerts
dbt Core: Model tests, documentation, lineage graphs, and governance processes
MobyDQ: Pipeline-based data quality checks and monitoring
Cucumber (BDD testing): Structured test cases that support reusable and maintainable validation logic
Governance Mistakes That Damage Data Quality
Avoid these common issues that cause governance initiatives to fail:
1) Trying to fix everything at once
Don’t “boil the ocean.” Start with one high-impact domain, show quick value, then expand.
2) No leadership support
Without sponsorship, governance becomes underfunded and ignored. Start small, prove value, and buy-in grows.
3) Weak stakeholder involvement
Governance fails when business users are not involved. Include stakeholders early so standards reflect real operational needs.
4) Poor assessment of data health
Without detailed profiling and feedback, teams cannot prioritize what matters.
5) Missing stewardship ownership
Governance needs responsible roles — empowered to resolve conflicts and enforce standards.
6) No measurement or tracking
If quality isn’t tracked, improvement isn’t real. Automate monitoring and report results regularly.
Case Example: Restoring Data Trust Through Governance
A fast-growing healthcare organization faced major discrepancies because departments used different definitions for the same data. This created inconsistent reporting and poor decision-making.
To solve it, the team:
Implemented a data governance strategy in phases
Standardized definitions across departments
Established clear lineage and documentation
Introduced continuous quality monitoring
Result: trusted reporting, unified data access, and reliable insights that supported scalable growth.
Key Takeaways
Data governance is essential to data quality — standards, ownership, and accountability build trust.
Profiling helps teams understand current issues and prioritize improvements.
Strong KPIs and monitoring ensure quality is measurable and sustainable.
Tools create value only when paired with people and processes.
Start small and scale over time to build momentum and long-term adoption.
