Data Quality in Data Governance: The Crucial Link that Ensures Data Accuracy and Integrity
Last Updated on: May 17th, 2023, Published on: May 17th, 2023
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At a high level, data quality is the degree to which data is accurate, consistent, and reliable. It’s about ensuring that the data your organization collects, stores, and uses is of high integrity and can be trusted for decision-making. Various aspects of data quality include accuracy, validity, reliability, timeliness, relevance, and completeness.
Data governance, on the other hand, is the overall management of the availability, usability, integrity, and security of the data employed in an enterprise. It’s a set of principles and practices that ensure high quality throughout the complete lifecycle of your data.
Table of contents
- Data quality and data governance: How they’re interconnected?
- The interconnection between data quality and data governance: A tabular view
- Deciphering the interrelationship between data governance and data quality: A detailed view
- Implementing data quality tools for effective data governance: A step-by-step guide
- Bringing it all together
- Data quality in data governance: Related reads
Data quality and data governance: How they’re interconnected?
In other words, data governance is the strategy and data quality is a key tactical goal of that strategy. They’re deeply interconnected in several ways:
- Establishing standards
- Processes and procedures
- Monitoring and improvement
- Data literacy
- Technology and tools
Let us look into each of the above interconnected ways in brief:
1. Establishing standards
A good data governance program sets the standards for data quality within the organization. It defines what ‘good’ data quality looks like, which metrics to use, and how to measure them.
Data governance assigns roles and responsibilities for maintaining data quality. This includes data owners, stewards, and custodians, who are responsible for various aspects of data quality in their respective domains.
3. Processes and procedures
Data governance lays down the processes and procedures for maintaining data quality. This could include data cleansing, de-duplication, integration, and validation routines.
4. Monitoring and improvement
Data governance also involves monitoring data quality levels, auditing for compliance with standards, and driving continuous improvement initiatives.
5. Data literacy
Data governance ensures that the organization understands the importance of data quality and is capable of maintaining it. This includes training and education, communication, and advocacy efforts.
6. Technology and tools
Data governance involves selecting and implementing the right technology and tools to support data quality. This includes database management systems, data integration tools, data quality tools, and business intelligence tools.
In summary, without a strong data governance program, maintaining high data quality becomes a challenge. And without high-quality data, the insights and decisions derived from that data may be flawed, potentially leading to suboptimal or even harmful outcomes. Therefore, your initiative to develop a data governance strategy is a crucial step toward ensuring data quality in your organization.
The interconnection between data quality and data governance: A tabular view
In this section of the blog, we’ll explore the relationship between data governance and data quality in a simple table:
|Data Governance||Data Quality|
|Sets the overall strategy for managing data within the organization||A key tactical goal within that strategy|
|Establishes standards for data quality||The degree to which data meets those standards|
|Assigns roles and responsibilities for managing data||Managed by the roles defined in the governance plan|
|Defines processes and procedures for data management||Maintained through those processes and procedures|
|Involves monitoring, auditing, and improvement of data management practices||Quality levels are monitored and improved as part of these practices|
|Ensures data literacy within the organization||The importance of high-quality data is understood as part of this literacy|
|Selects and implements technology and tools to support data management||The quality of data is supported and improved by these tools and technologies|
Remember that while the table provides a simplified view, data governance and data quality are complex, interrelated concepts that require careful planning, execution, and ongoing management.
As you develop your data governance strategy, it’s important to keep data quality in mind as a key goal and to ensure that your governance practices effectively support and enhance the quality of your data.
Deciphering the interrelationship between data governance and data quality: A detailed view
Now, let look into each of these interrelationships in detail:
1. Setting the overall strategy for managing data vs. A key tactical goal
Data governance is like the constitution of a country - it sets the broad framework and principles of how data should be managed within an organization. For example, a part of your governance strategy might be to centralize data management for better control and consistency.
Data quality, then, becomes a key goal within that strategy, a tactical objective that needs to be achieved - for instance, ensuring that patient records are accurate and up-to-date in your centralized database.
2. Establishing standards for data quality vs. Degree to which data meets those standards
Data governance defines what ‘good’ data quality looks like. For instance, it might define a standard that no more than 0.5% of patient records should contain errors. Data quality is then the degree to which your data meets those standards - for example, if only 0.2% of records contain errors, your data quality is high and surpasses the standard.
3. Assigning roles and responsibilities for managing data vs. Managed by the roles defined in the governance plan
Data governance involves defining who is responsible for what data. For instance, a governance plan might assign a ‘data steward’ role to certain employees who are responsible for maintaining the accuracy of patient records.
Data quality is then managed by those data stewards - for instance, they might regularly review and correct any errors in the patient records they are responsible for.
4. Defining processes and procedures for data management vs. Maintained through those processes and procedures
Data governance sets out the processes for managing data - for example, how new patient records should be entered into the system, how changes should be tracked, and how errors should be corrected.
Data quality is maintained through these processes - for instance, by following the defined process, employees can ensure that new records are accurate, changes are correctly recorded, and errors are promptly fixed.
4. Monitoring, auditing, and improvement of data management practices vs. Quality levels are monitored and improved
Data governance involves regularly checking that data management practices are being followed and looking for ways to improve them - for example, you might conduct audits to ensure that patient records are being correctly maintained and look for any common issues that need to be addressed.
Data quality is what you monitor and aim to improve through these activities - for instance, if an audit finds that a significant number of records contain errors, you would look to improve your data quality by addressing the underlying issues.
5. Ensuring data literacy vs. Understanding the importance of high-quality data
Data governance includes promoting data literacy throughout the organization - for example, by providing training on how to handle data correctly and why it’s important.
Data quality is a key part of what employees need to understand. For instance, employees in healthcare organizations need to know why accurate patient records are critical for providing good healthcare and how their actions impact data quality.
6. Selecting and implementing technology and tools vs. Quality of data supported and improved by these tools
Data governance involves choosing the right systems and tools for managing data - for example, a database management system that provides robust data validation features.
Data quality is then supported and enhanced by these tools - for example, the validation features of the database system could automatically check that new patient records meet certain criteria, helping to prevent errors and improve data quality.
Implementing data quality tools for effective data governance: A step-by-step guide
Creating a comprehensive data governance program with the goal of quality data can be quite challenging. That’s why we put together a step-by-step guide on what to consider, what to implement, and how to make a business case for it:
- Step 1: Define the problem and goals
- Step 2: Identify the required tools and technologies
- Step 3: Evaluate and select tools and technologies
- Step 4: Make a business case
- Step 5: Implement the tools and technologies
- Step 6: Monitor and improve
Now, let us look into each of the above steps in detail:
Step 1: Define the problem and goals
The first step in any data governance initiative is to clearly define what problems you’re trying to solve and what your goals are. In your case, the goal is to improve data quality. It’s also helpful to quantify the problem and the goal as much as possible.
For instance, you might say, “We have identified that 5% of our patient records contain errors. Our goal is to reduce this to less than 1%.”
Step 2: Identify the required tools and technologies
Based on the problems and goals you’ve identified, you can start researching the tools and technologies that can help. You’ll likely need a combination of data quality tools (for data cleansing, validation, etc.), data integration tools (for combining data from different sources), and possibly also data governance tools (for managing metadata, tracking data lineage, etc.).
Step 3: Evaluate and select tools and technologies
Now you’ll need to evaluate the different options based on their features, ease of use, compatibility with your existing systems, cost, and other factors. This may involve requesting demos or trial versions, reading reviews, and speaking with other users.
Step 4: Make a business case
Once you’ve identified the best tools and technologies for your needs, you’ll need to make a business case for implementing them. Here are some points to include:
- Cost of poor data quality
Start by quantifying the cost of poor data quality in terms of wasted time, inaccurate reporting, poor decision-making, compliance risks, etc. This will help decision-makers understand why the status quo is not acceptable.
- Benefits of improved data quality
Then, articulate the benefits of improved data quality. This could include more accurate reporting, better decision-making, reduced compliance risk, improved patient care, and others.
- ROI of the proposed tools and technologies
Now present the return on investment (ROI) of the proposed tools and technologies. This should include both the costs (purchase price, implementation costs, ongoing costs) and the expected benefits (time saved, improved decision-making, reduced risk, etc.). Make sure to show how the benefits outweigh the costs.
- Risk mitigation
Discuss how the proposed tools and technologies can mitigate the risk of poor data quality, such as compliance risks, reputational risks, and operational risks. This can be especially persuasive for decision-makers in healthcare, where data errors can have serious consequences.
Step 5: Implement the tools and technologies
Once you’ve got approval, you can start implementing the chosen tools and technologies. This will likely involve a project plan, resource allocation, and a roll-out strategy.
Step 6: Monitor and improve
Finally, once the tools and technologies are in place, you’ll need to monitor their effectiveness and make improvements as needed. This could involve tracking key metrics, conducting regular audits, and seeking feedback from users.
Remember, data governance and data quality are ongoing efforts, not one-time projects. You’ll need to keep monitoring and improving over time to maintain high data quality and adjust to changing needs and circumstances.
Bringing it all together
We hope this guide helped you gain a comprehensive understanding of the significance of data quality within the framework of data governance.
By following these steps and principles, your organization can ensure that its data governance program effectively supports and enhances data quality.
This, in turn, can lead to more accurate reporting, better decision-making, reduced compliance risk, improved patient care, and other benefits.
Data quality in data governance: Related reads
- What is Data Governance? Its Importance, Principles & How to Get Started?
- How to Improve Data Quality: Strategies and Techniques to Make Your Organization’s Data Pipeline Effective
- Data Quality Measures: A Step-by-Step Implementation Guide
- The Benefits of GDPR Compliance and Data Governance: Protecting Your Data and Your Business
- Key Objectives of Data Governance: How Should You Think About Them?
- Data Governance Framework — Examples, Templates, Standards, Best Practices & How to Create One?
- Data Governance and Compliance: Act of Checks & Balances
- How to implement data governance? Steps, Prerequisites, Essential Factors & Business Case
- How to Improve Data Governance? Steps, Tips & Template
- 7 Steps to Simplify Data Governance for Your Entire Organization
- Snowflake Data Governance — Features, Frameworks & Best Practices
- Automated Data Governance: How Does It Help You Manage Access, Security & More at Scale?
- Enterprise Data Governance — Basics, Strategy, Key Challenges, Benefits & Best Practices
- Data Governance in Manufacturing: Steps, Challenges, and Practical Examples
- Data Governance in Retail: Best Practices, Challenges, and Viable Solutions
- Data Governance in Insurance: Why is it Important and How it Drives Positive Business Outcomes
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