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.
- What is data governance?
- Why is data governance important?
- What is data quality?
- Why is data quality important?
- What are the main differences between data governance and data quality?
- How data governance and data quality overlap
- How to integrate data quality and data governance for your organization
What is data governance?
Data governance is the 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 the 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.
SEE: For more information, check out our in-depth data governance overview.
There are various data governance software that give you control over data availability, usability, integrity and security. We reviewed the top data governance tools, their features, strengths and weaknesses and pricing so that you can select the best option for you.
Why is data governance important?
Data governance is important for various reasons:
- Compliance: It ensures companies are adhering to laws and regulations, such as GDPR, which can help them avoid hefty fines and penalties.
- Consistency: It provides a consistent approach to handling data across an organization.
- Security: It plays a key role in protecting sensitive data from potential breaches, reducing the risk of exposure.
- Trust: It builds trust in data as stakeholders can be confident the data is properly managed, up to date and accurate.
- Increased efficiency: It boosts operational efficiency by eliminating unnecessary duplication of data and streamlining data-related processes.
- Better decision-making: High-quality, reliable data makes for better strategic planning, decision-making and overall performance metrics across every sector of a business.
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, what strategies they want to implement and what data they have at their disposal for success.
SEE: Learn how to measure data quality.
To ensure data quality, it is necessary to use the best data quality software because any flaws in data quality can lead to poor decision-making. The higher the quality of your data, the more valuable it becomes.
Why is data quality important?
Ensuring data quality is not just a nice thing to have but a crucial aspect of any data-driven approach or business. Managing data quality can lead to:
- Accurate decision-making: High-quality data leads to better decision processes as it often involves tracking performance, predicting future outcomes and identifying potential issues.
- Resource optimization: By ensuring data quality, companies can avoid the waste of resources on incorrect data and help leverage resources efficiently.
- Customer experience: Accurate and up-to-date data helps companies understand customers and their preferences.
- Cost reduction: Poor data quality can lead to costly mistakes and rework, so by investing in data quality, organizations can minimize errors and associated expenses.
Data quality is not just a short-term concern; it impacts an organization’s long-term success and growth. Organizations can ensure they are well-prepared for future challenges and opportunities by maintaining high data quality standards.
What are the main differences between data governance and data quality?
Data governance focuses on overarching data management activities for people, processes and technology. Its applications include designing a sound approach to storing information, managing its life cycle, 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.
On the other hand, data quality focuses on addressing information accuracy 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 data is accurate, consistent, relevant and complete.
|Data approach||Data governance||Data quality|
|Focus||Policies, processes and procedures for managing data assets||Assessing and ensuring the accuracy, consistency and reliability of data|
|Objective||Ensure data is appropriately used, protected and compliant with regulations||Ensure data meets predefined standards and requirements|
|Scope||Broad in scope; organization-wide||Narrower in scope; primarily focuses on datasets or specific projects|
|Activities||Policy development, defining data ownership and accountability, data classification, data access controls, data retention policies and regulatory compliance||Data profiling, data cleansing, data validation, data standardization, data monitoring and establishing data quality metrics and benchmarks|
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 governance is symbiotic; they are necessary to achieve sound enterprise data management.
SEE: Explore the top data management strategies for small businesses.
Without good data quality practices, organizations will struggle to maintain complete, 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 in reporting tools. Furthermore, poor data quality makes extracting insights from raw data difficult.
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. Implementing data quality and data governance strategies often involves the following:
- 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 datasets to work on first.
- Improve the most critical datasets by defining key performance indicators that will measure improvement.
- Identify opportunities for automation or efficiency by creating an action plan based on those KPIs.
- Determine if governance policies are enforced and if they should be updated or created.
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.
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