Data quality vs data governance: How they impact your business

Learn about the key differences between data quality and data governance and how they can work together to improve your data strategy.

Data quality and data governance describe different parts of enterprise data management strategies but are not mutually exclusive. Together, they can help your business improve its bottom line by providing better visibility into enterprise assets, all while driving efficiency and operational improvements that lead to greater business agility. This comparison defines both terms, explains their differences, and covers how data quality and data governance best practices can be used in tandem.

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What is data governance?

Data governance is a process of establishing, aligning and securing data within an organization. It aims to ensure that data is collected, stored, processed and disposed of consistently.

Data governance covers the strategies and processes needed to manage enterprise data effectively to leverage it for business decision-making. It also provides a framework for managing risk associated with businesses in an uncertain regulatory environment.

In short, data governance is about managing all organizational information assets — not just data, but also documents, applications, networks, configurations and metadata.

What is data quality?

Data quality is the measure of how complete, accurate, relevant, timely, consistent and trustworthy data is. If data has all these qualities, then it is considered high quality. Businesses with high-quality data can make better decisions about which direction they want to take their company in, what strategies they want to implement and what data they have at their disposal for success.

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Any flaws in data quality can lead to poor decision-making. The higher the quality of your data, the more valuable it becomes.

What are the main differences between data governance and data quality?

The main difference between data governance and data quality is that data governance focuses on overarching data management activities for people, processes and technology. Data governance applications include designing a sound approach to storing information, managing its lifecycle, identifying information that needs to be corrected or deleted, appointing someone as the accountable data steward and investing in technology to help maintain data governance.

Data governance governs who accesses data, how data is accessed, who analyzes the data and who reports on the data. On the other hand, data quality focuses on addressing these issues more granularly by identifying data problems or inconsistencies within individual pieces of information, such as names or addresses. It also covers the design and execution of specific processes to ensure that data is accurate, consistent, relevant and complete.

The most important distinction is that data quality is about data accuracy, whereas data governance is more concerned with how enterprises use data.

How data governance and data quality overlap

Data quality is an important component of data governance but should not be considered a substitute for governance. The relationship between data quality and data governance is symbiotic; they are both necessary to achieve sound enterprise data management.

Without good data quality practices, organizations will struggle to maintain complete and accurate information that can be trusted to provide input for other corporate processes. Poorly managed metadata will also undermine business intelligence initiatives by introducing inaccuracies into reporting tools. Furthermore, poor data quality makes extracting insights from raw data difficult.

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As such, companies must find an appropriate balance between these two important components of data management. It is not enough to have one without the other; organizations must have strong governance practices while implementing robust data quality strategies.

How to integrate data quality and data governance for your organization

Data quality and governance goals are achieved through strategic decisions, operational efforts, ongoing oversight and a willingness to innovate. Take inventory of your organization’s data to understand what you have, where it resides, how it gets there, who uses it in which business process, how often they use it and why they need it.

Use this information to determine the most critical data sets to work on first. Next, improve the most critical data sets by defining key performance indicators that will measure improvement. Then identify opportunities for automation or efficiency by creating an action plan based on those KPIs. Finally, determine if governance policies are enforced and if they should be updated or created.

Continue leveraging machine learning and artificial intelligence tools to improve data accuracy and empower employees to take responsibility for their data. In addition, monitor any regulations that may affect your organization, such as GDPR, to ensure compliance. And don’t forget about security: Security measures can help protect against human error and malicious behavior — without them, all other efforts would fail.

If data governance is ineffective, it may not be possible to reach a high level of data quality. Conversely, organizations cannot achieve effective data governance if data quality is low or non-existent. Both need to be in place to get your desired results.