This article covers the importance of Data Governanace and the tools which can be used for improving the data quality and Hence giving better results in Visualization

1. 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.

2. 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

3. 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.

4.  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. 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.