Organizations face an annual loss of $15 million on average due to poor data quality. This happens because poor data quality impacts the decision-making processes by giving inaccurate insights, which produces incorrect analysis. If you don’t want your business to be a part of this annual loss, it’s time to focus on data governance and quality.
Data governance sets the rules and processes for managing data, while data quality keeps your data accurate and ready to use for analysis. Strong governance leads to better data quality, and high-quality data powers better decision-making.
In this blog, we will discuss how these concepts work together and the best practices to implement them.
What is data governance?
Data governance is the practice of setting up policies and standards for evaluating data quality to make it a valuable business asset. Its key components are:
Data ownership: Assigns clear responsibility for data assets to relevant personnel or teams for proper management and usage.
Accountability: Defines who is responsible for maintaining data quality and resolves issues while staying compliant with governance policies.
Policy enforcement: Establishes rules and controls for data collection and retention to meet regulatory compliance.
All these components set a strong foundation for a well-structured data governance program. So, if your organization has a complex data ecosystem, you must create an enterprise data governance plan to avoid any inconsistencies.
What is data quality?
Data quality sets standards to keep data accurate and consistent in a format businesses can rely on for decision-making. Some of its key dimensions are:
Accuracy: Ensures data is free from any duplication and formatting errors.
Completeness: No critical information is missing.
Consistency: Data remains uniform across systems.
Timeliness: Data is updated in real time.
Relevance: Data aligns with business needs.
When these dimensions are absent, data quality is poor and severe consequences such as incorrect reports, compliance failures, wasted resources, and lost revenue arise.
Since our data constantly changes, maintaining its quality requires continuous, systematic effort. And that’s exactly why we need data governance — it provides the structure and processes required to sustain data integrity and keep companies data-driven and competitive.
The symbiotic relationship between data governance and data quality
Data governance and data quality are two sides of the same coin — one cannot thrive without the other. Governance sets the rules that ensure data is well-maintained, while data quality measures how well those standards are met.
Organizations define data ownership and access controls through governance to keep their data trustworthy and compliant. On the flip side, strong data quality initiatives reinforce governance by ensuring that data remains clean and usable.
High-quality data makes governance efforts more effective, as organizations can confidently impose policies without constantly fixing errors. This creates a continuous feedback loop where governance improves quality, and quality strengthens governance with reliable data flows.
Data governance practices that enhance data quality
75% of people say improving data quality is the primary goal of data programs. Let’s look at some of the best data governance practices that you must follow to improve data quality:
1) Data standards and policies
Here are some best practices to maintain data quality:
Define rules for accuracy and completeness, such as mandatory fields in customer records.
Automate processes to flag missing and incorrect data before it enters our systems.
Align governance policies with legal frameworks like GDPR and HIPAA to avoid data-related risks.
Clearly define data ownership so teams know who retains data integrity.
Start small with critical data assets and gradually expand governance policies organization-wide.
2) Data stewardship
Data stewards are the frontline people who maintain data quality. They resolve quality issues and ensure governance policies are followed. But to do so, they must have strong analytical skills and a deep understanding of technical and business requirements to succeed in this role.
Their key responsibilities are:
Tracking data quality metrics and fixing inconsistencies.
Working with different teams and business units to align governance policies with business needs.
Educating teams on best practices to prevent data errors before they occur.
Metadata maintains both governance and quality by providing context and transparency about existing data. It documents details like:
Data origins (where data comes from).
Transformations (how data has changed over time).
Usage history (who has accessed or modified data).
Measuring the impact of data governance on data quality
Implementing data governance is the first step — measuring its impact is what proves its value. To measure the impact and see if your governance efforts are improving data quality, track the following KPIs:
Accuracy: Percentage of error-free records.
Completeness: Number of missing or incomplete fields in datasets.
Consistency: Uniformity of data across multiple systems.
Timeliness: The time it takes for data to be updated and available.
Compliance rate: Percentage of data that meets regulatory and governance standards.
User trust score: Feedback from business teams on data usability and reliability.
Tracking and reporting on governance metrics
Once you have tracked these KPIs, you must report them to the necessary teams. For this, you can use dashboards and automated reporting tools. For example, platforms like data.world provide interactive dashboards to track metrics and report them in real-time. This helps teams:
Use real-time dashboards to visualize trends in data quality.
Set automated alerts to track data quality issues for quick resolution before they impact operations.
Generate compliance reports that highlight governance adherence and data integrity status.
Regular audits and assessments
However, governance is not a one-time project — it’s an ongoing effort. That’s why you must conduct regular audits to consistently check if policies are followed to improve data quality. This way, you can catch issues and adjust your data governance frameworks based on audit insights to align with business needs.
The role of technology in data governance and quality
Technology is the backbone of modern data governance and quality initiatives. Without the right data governance tools, data management can be overwhelming as there’s so much involved in the entire process. To make this easier, you can use data catalogs and data management systems.
Data catalogs create a central place where teams can track and manage their data assets. These catalogs connect with data management platforms to give complete visibility into your data assets. This way, you can use your data appropriately to make the right decisions.
In fact, these tools are backed by agile data governance which makes data management more flexible and collaborative. It automates manual workflows and lets teams respond quickly to new data requirements. This collaborative environment allows stewards and business users to work together and track the effectiveness of governance initiatives.
Best practices for integrating data governance and quality initiatives
Now that you know how data governance and quality go hand-in-hand to make the right business decisions, let’s look at some best practices to integrate them:
Define shared goals: Make sure governance and data quality efforts align with business objectives.
Establish cross-functional collaboration: Bring IT and business teams together to create governance policies that reflect real-world data usage.
Create clear roles and responsibilities: Assign data owners and stewards accountable for maintaining data quality within governance frameworks.
Use governance as an enabler: Encourage teams to view governance as a tool for better data-driven decisions, not only a compliance requirement.
Implement continuous data profiling: Regularly scan for duplicates and missing values to maintain high-quality data.
Apart from these practices, embed governance policies at every stage of the data lifecycle to sustain high-quality data over time. To do so, add validation rules that prevent incorrect information from entering your systems.
As data moves through your systems, add automated checks at key points. These checks will verify data completeness and consistency. Then, conduct monitoring checks to catch any issues that slip through. You can even create dashboards to track error rates and data quality metrics. This will help spot problems and your team can fix them before they affect business decisions.
How data.world supports data governance and data quality
As a leading data catalog and governance platform, data.world uses a knowledge graph to connect all data assets in a way that makes relationships apparent and discoverable. This foundation enables automatic data lineage tracking and sensitive information discovery while flexible workflows keep governance simple and practical.
We provide a central hub where teams can collaborate to manage metadata and enforce policies while maintaining a clear view of how data flows through your organization. To speed this up, we also provide AI and automation features so your teams can focus on valuable analysis without compromising compliance.
Schedule a demo today and see how data.world can help your organization streamline governance and improve data quality.
