Data Quality Framework: 9 Key Components & Best Practices for 2025

Updated December 03rd, 2024

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A Data Quality Framework defines the standards, processes, and tools that ensure data accuracy, consistency, completeness, and reliability. It establishes guidelines for data governance, enabling organizations to maintain high-quality data for better decision-making and compliance.
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Key components include data validation, automated quality checks, and continuous improvement. Implementing a robust framework helps businesses optimize data management, reduce errors, and enhance trust in analytics.

By integrating scalable and actionable metrics, organizations can monitor and improve data quality effectively. A well-structured framework is essential for aligning data practices with business goals and regulatory requirements.

In this blog, we delve into the key components, creation process, and available resources for mastering data quality frameworks.

Let’s dive in!

Table of contents #

  1. Key components of a data quality framework
  2. What is a data quality framework composed of?
  3. Best data quality frameworks
  4. How can you create a data quality framework
  5. Template for crafting an effective data quality framework
  6. Books and resources for mastering data quality framework
  7. Summarizing all the points
  8. How organizations making the most out of their data using Atlan
  9. FAQs about Data Quality Framework
  10. Data quality framework: Related reads

What are the key components of a data quality framework? #

Today, organizations must establish robust frameworks to ensure the quality of their data assets. So, let us take a look at the key components of a data quality framework:

  1. Data governance
  2. Data profiling
  3. Data quality rules
  4. Data quality assessment
  5. Data cleaning
  6. Data monitoring
  7. Data issue management
  8. Data reporting
  9. Continuous improvement

Now, let us look into each of the above key components in brief:

1. Data governance #


Data governance includes policies, standards, and guidelines that provide a direction on how data should be collected, stored, managed, and used within an organization. This is the foundation of any data quality framework.

2. Data profiling #


Data profiling involves examining the data available in an organization and collecting statistics or informative summaries about that data. It helps in identifying anomalies, inconsistencies, or inaccuracies in the data.

3. Data quality rules #


Data quality rules are sets of predefined rules or constraints that help in checking the accuracy, validity, consistency, and completeness of data. They can be business rule checks, cross-dataset checks, or checks against external data sets or services.

4. Data quality assessment #


This involves a regular audit or review of data quality performance using the data quality rules. It’s usually done using data quality scorecards that are tailored to meet the organization’s data quality needs.

A data quality survey conducted by Monte Carlo in March 2023 revealed that over half of the respondents reported that 25% or more of their revenue was affected by data quality issues. The same survey noted a rise in monthly data incidents, from 59 in 2022 to 67 in 2023, indicating a growing challenge in maintaining data quality.

5. Data cleaning #


This involves detecting and correcting (or removing) corrupt, inaccurate, or erroneous records from a dataset or database.

6. Data monitoring #


This involves the continuous tracking and monitoring of data quality metrics to ensure ongoing compliance with the data quality standards.

7. Data issue management #


Involves resolving data quality issues that are found during data profiling, data quality assessment, and data monitoring.

8. Data quality reporting #


This provides a report on the state of data quality within an organization. It’s an important tool for communicating data quality status and progress to stakeholders.

9. Continuous improvement #


As the needs of an organization change, the data quality framework should adapt to meet these new requirements. It involves continuous analysis, measurement, improvement, and control of data quality efforts.

A successful data quality framework combines these elements with technology (like data cataloging tools, data quality software, etc.), processes (like defining data quality metrics, setting up data stewardship roles, etc.), and people (like data stewards, data owners, etc.). It helps ensure that high-quality data is available, thereby supporting decision-making and operational efficiency.

According to the TDWI 2024 State of Data Quality Report, Organizations are more mature in managing the quality of structured data, with 41% supporting only structured data, even though they utilize various data types.

What is a data quality framework composed of? #

A data quality framework can be thought of as a structure that encompasses strategies, procedures, standards, technologies, and measures to ensure and improve data quality. The composition of such a framework includes:

  1. Data quality standards
  2. Data profiling
  3. Data quality assessment
  4. Data quality reporting
  5. Data cleaning
  6. Monitoring and control
  7. Data governance

Let us look into each of them in brief:

1. Data quality standards #


These are the criteria that your data needs to meet to be considered of ‘high quality’. The specific standards used can vary greatly depending on the nature of the data and the use case.

For example, in a financial institution, data quality standards might include an accurate representation of transactional amounts and correct customer details.

2. Data profiling #


This is the process of examining the data available in an existing database and collecting statistics and information about that data.

For instance, data profiling could identify the average age of customers in a retail company’s database, or the number of missing or null values in a certain column.

3. Data quality assessment #


This is the process of evaluating the quality of your data against the predefined standards. Data quality issues like duplicates, inconsistencies, and inaccuracies are identified during this step.

For instance, in a dataset of employee records, an assessment might find that some records have been duplicated. In other words, the ‘Salary’ column contains a non-numeric value.

4. Data quality reporting #


After data is assessed, a report is generated that includes the findings of the data quality check. This might involve:

  • Generating a data quality score
  • Highlighting specific data quality issues
  • Producing other summaries
  • Visualizations of data quality

5. Data cleaning #


This step involves resolving the issues found during the assessment stage. Data cleaning could involve tasks such as:

  • Removing duplicates
  • filling in missing values
  • Correcting inaccurate data

6. Data quality improvement #


This refers to ongoing efforts to improve data quality, such as refining data collection procedures. This also involves implementing new technology to prevent data quality issues from occurring in the first place.

For instance, a business might implement a new data entry system with built-in data validation rules to prevent incorrect data from being entered.

7. Monitoring and control #


This is a continuous process of checking the data quality at regular intervals to ensure the standards are being maintained.

8. Data governance #


This is the overarching strategy that aligns all the data quality efforts to ensure they’re in line with business objectives. It involves setting up policies, procedures, and responsibilities related to data quality.

Now, let us take an example of a retail company implementing a data quality framework. The company may define its data quality requirements in the below manner:

  • Data quality standards: The company decides that all customer records should have a valid email address, a non-null purchase history, and accurate demographic information.
  • Data profiling: The company examines its current customer database to identify patterns, anomalies, and areas that need attention.
  • Data quality assessment: The company finds that 10% of their customer records have an invalid or missing email address.
  • Data quality reporting: The company generates a report detailing the issues with their data, including the specific problem with email addresses.
  • Data cleaning: The company undertakes an effort to correct or fill in missing/invalid email addresses, possibly by reaching out to customers directly or using other available data.
  • Data quality improvement: The company decides to implement a new customer onboarding form that validates email addresses at the point of entry, preventing this issue from occurring in the future.
  • Monitoring and control: The company sets up regular checks of its customer data to quickly identify and address any future data quality issues.
  • Data governance: The company establishes a data governance team responsible for overseeing and guiding these efforts, setting policies for data collection and management, and ensuring alignment with business goals.

These components work together in a data quality framework, with each one building upon and informing the others.

Also, read → USDA DATA Act - Data Quality Plan | McKinsey on Real-world data quality

Choosing the best data quality frameworks for your next project #

There are several established data quality frameworks available that provide guidelines and best practices for maintaining and improving data quality. Here are a few examples:

1. The DAMA guide to the data management body of knowledge (DAMA-DMBOK): #


The DAMA-DMBOK is a comprehensive guide to data management that includes a chapter on data quality. It’s a valuable resource for anyone looking to develop a data quality framework.

2. The data quality framework from the Australian Institute of Health and Welfare (AIHW): #


The data quality framework by the Australian Institute of Health and Welfare is a framework specifically designed for the health sector. It covers six dimensions of data quality:

  • Institutional environment
  • Relevance
  • Timeliness
  • Accuracy
  • Coherence
  • Interpretability

3. TDWI’s data quality management framework #


This framework is described in the TDWI report “Data Quality and the Bottom Line”. It’s a comprehensive guide that covers everything from data governance to data profiling, and it provides actionable advice for businesses looking to improve their data quality.

4. The MIT Information Quality (MITIQ) program #


This program has developed a data quality framework that emphasizes the business impacts of data quality. The framework covers data quality dimensions, business processes, and the role of data governance.

5. ISO 8000 data quality model #


The ISO 8000 data quality model is an international standard for data and information quality management. It provides a model for describing, measuring, and managing data quality.

6. The data quality assessment framework (DQAF) by IMF #


The International Monetary Fund has developed a framework specifically for the assessment of data quality, especially in the realm of macroeconomic and financial statistics. The framework focuses on five dimensions: assurances of integrity, methodological soundness, accuracy and reliability, serviceability, and accessibility.

These frameworks all approach data quality from slightly different perspectives, but they each provide valuable guidelines and insights for ensuring and improving the quality of data in an organization.

It’s worth noting that each organization will likely need to customize any chosen framework to best suit their unique needs and circumstances.

How can you create a data quality framework: A step-by-step guide #

Here is a general step-by-step process for creating a data quality framework for an organization:

  1. Understand the business needs
  2. Define data quality goals
  3. Data quality assessment
  4. Establish data governance
  5. Implement data quality rules
  6. Automate data quality processes
  7. Data cleansing
  8. Monitor, control, and report
  9. Implement continuous improvement practices
  10. Training and culture
  11. Review and update the framework regularly

Let us look into each step in detail:

1. Understand the business needs #


Identify the critical data elements that are used to drive business decisions. These are typically data elements that appear in reports, dashboards, and other decision-making tools. Also, understand the current data-related pain points, such as reporting inaccuracies, slow processing times, etc.

2. Define data quality goals #


Define what data quality means for your organization. This typically involves identifying the key dimensions of data quality (e.g., accuracy, completeness, timeliness, consistency, and relevance). Each dimension should have a specific goal associated with it that aligns with business needs.

3. Data quality assessment #


Profile your data to understand its current quality status. Data profiling involves statistical analysis and review of data to understand patterns, anomalies, and errors. This step is critical to understand the extent and nature of your data quality issues.

4. Establish data governance #


Create a data governance committee or designate data stewards who will own the data quality process. The governance structure should be accountable for meeting the data quality goals.

5. Implement data quality rules #


Based on your goals, create data quality rules that can be applied to validate and clean data. These rules should cover all critical data elements identified earlier.

6. Automate data quality processes #


Automation is key to maintaining high data quality over time. Implement data quality tools that can automate the process of checking and cleaning data.

7. Data cleansing #


Correct the current issues identified in your data assessment. This may involve standardizing data, removing duplicates, correcting errors, and filling gaps in data.

8. Monitor, control, and report #


Establish ongoing monitoring and reporting of data quality metrics. These metrics should be regularly reviewed by the data governance committee to ensure you’re meeting your data quality goals.

9. Implement continuous improvement practices #


Data quality isn’t a one-time initiative. It’s an ongoing practice that should continuously be reviewed and improved. Use feedback from the data quality reports to refine your processes, and consider using techniques like Six Sigma. In other words lean to facilitate continuous improvement.

10. Training and culture #


Train staff on the importance of data quality and the processes you’ve implemented. Creating a culture that values data quality is key to ensuring these initiatives are successful.

11. Review and update the framework regularly #


The data quality framework should be a living document. Review it regularly and make updates as needed to ensure it continues to align with your business needs and goals.

Remember, the data quality framework should be customized to fit the specific needs and capabilities of the organization. It may take time to establish, but once in place, it can greatly improve the reliability and usability of your data.

A template for crafting an effective data quality framework #

Here’s a basic template for a Data Quality Framework document along with a few elements to keep in mind:

1. Executive summary

  • Write a brief description of the purpose and goal of the Data Quality Framework.

2. Introduction

  • A deeper explanation of the Data Quality Framework’s role and the business needs that it will serve.

3. Business needs and objectives

  • A detailed account of the business needs and objectives that necessitate the framework.
  • Identification of critical data elements.

4. Data quality goals

  • Explanation of the data quality goals and the key dimensions of data quality relevant to your organization (e.g., accuracy, completeness, timeliness, consistency, and relevance).

5. Data governance

  • Outline of the data governance structure including roles and responsibilities.

6. Data quality rules

  • Details of the data quality rules for validation and cleaning of data.

7. Data quality assessment

  • Procedure and frequency of data quality assessments.
  • Statistical methods or tools used for data profiling.

8. Data quality monitoring, control, and reporting

  • Description of the procedures for monitoring, control, and reporting.
  • Explanation of the metrics used for data quality evaluation.
  • The frequency at which these metrics will be reported and reviewed.

9. Data quality improvement

  • Explanation of the continuous improvement process.
  • Methods for implementing improvements based on data quality reports.

10. Data cleansing

  • The procedure for correcting the issues identified in your data assessment.

11. Training and culture

  • Description of the staff training plan.
  • Measures to be taken to build a data-centric culture.

12. Framework review and update procedure

  • Explanation of how and when the Data Quality Framework will be reviewed and updated.

13. Conclusion

  • Final thoughts and a brief recap of the Data Quality Framework.

Appendix

  • Include any supporting documentation or materials like a glossary of terms, reference materials, etc.

This template provides an overarching structure for creating a Data Quality Framework, but remember to tailor it to the specifics of your organization. It’s essential to regularly review and update the document to reflect any changes in the organization’s objectives or the data environment.

Books and resources for mastering data quality framework #

Here are some books and resources where you can learn more about data quality frameworks:

Books #


  1. “Data Quality: The Accuracy Dimension” by Jack E. Olson - This book focuses on the dimension of accuracy within data quality, and gives a detailed account of how to ensure data is accurate.
  2. "Data Quality Assessment" by Arkady Maydanchik - This book provides a comprehensive resource for understanding and implementing data quality assessment in your organization.
  3. “Executing Data Quality Projects: Ten Steps to Quality Data and Trusted Information” by Danette McGilvray - This book presents a systematic, proven approach to improving and creating data and information quality within the enterprise.
  4. “Data Governance: How to Design, Deploy and Sustain an Effective Data Governance Program” by John Ladley - This book provides a comprehensive overview of data governance, including the necessary components of a successful program.
  5. "The Data Warehouse ETL Toolkit: Practical Techniques for Extracting, Cleaning, Conforming, and Delivering Data" by Ralph Kimball and Joe Caserta - This book provides a comprehensive guide to the entire ETL (Extract, Transform, Load) process, and includes valuable advice on ensuring data quality throughout.

Online Resources #


  1. DAMA International’s Data Management Body of Knowledge (DMBOK): DAMA International’s Guide to the Data Management Body of Knowledge is a comprehensive, rigorous reference that outlines the scope and understanding necessary to create and manage a data management program.
  2. Data Governance Institute (DGI): The DGI provides in-depth resources on data governance and data quality, including a useful model for a data governance framework.
  3. Data Quality Pro: This is a free online resource that offers a wealth of articles, webinars, and tutorials on various data quality topics.
  4. Online Courses: Websites like Coursera, edX, and Udemy offer courses on data quality management and data governance that can help deepen your understanding of these topics.
  5. IBM’s Information Governance Catalog: While not a learning resource in the traditional sense, this catalog provides a real-world example of a comprehensive, well-documented information governance framework.

Remember that the process of establishing a data quality framework in your organization is iterative and will involve learning from both successes and failures. Therefore, take advantage of the collective knowledge shared through these resources, but don’t be afraid to adapt and create a framework that best fits your organization’s unique needs.

How organizations making the most out of their data using Atlan #

The recently published Forrester Wave report compared all the major enterprise data catalogs and positioned Atlan as the market leader ahead of all others. The comparison was based on 24 different aspects of cataloging, broadly across the following three criteria:

  1. Automatic cataloging of the entire technology, data, and AI ecosystem
  2. Enabling the data ecosystem AI and automation first
  3. Prioritizing data democratization and self-service

These criteria made Atlan the ideal choice for a major audio content platform, where the data ecosystem was centered around Snowflake. The platform sought a “one-stop shop for governance and discovery,” and Atlan played a crucial role in ensuring their data was “understandable, reliable, high-quality, and discoverable.”

For another organization, Aliaxis, which also uses Snowflake as their core data platform, Atlan served as “a bridge” between various tools and technologies across the data ecosystem. With its organization-wide business glossary, Atlan became the go-to platform for finding, accessing, and using data. It also significantly reduced the time spent by data engineers and analysts on pipeline debugging and troubleshooting.

A key goal of Atlan is to help organizations maximize the use of their data for AI use cases. As generative AI capabilities have advanced in recent years, organizations can now do more with both structured and unstructured data—provided it is discoverable and trustworthy, or in other words, AI-ready.

Tide’s Story of GDPR Compliance: Embedding Privacy into Automated Processes #


  • Tide, a UK-based digital bank with nearly 500,000 small business customers, sought to improve their compliance with GDPR’s Right to Erasure, commonly known as the “Right to be forgotten”.
  • After adopting Atlan as their metadata platform, Tide’s data and legal teams collaborated to define personally identifiable information in order to propagate those definitions and tags across their data estate.
  • Tide used Atlan Playbooks (rule-based bulk automations) to automatically identify, tag, and secure personal data, turning a 50-day manual process into mere hours of work.

Book your personalized demo today to find out how Atlan can help your organization in establishing and scaling data governance programs.

Summarizing all the points #

A data quality framework is a structured plan to ensure and manage the quality, reliability, and integrity of data in an organization. It involves different dimensions of data quality including accuracy, completeness, consistency, timeliness, validity, and uniqueness.

The essential components of a data quality framework, provide insights into its composition and practical application. The data quality framework should be customized to fit the specific needs and capabilities of the organization. It may take time to establish, but once in place, it can greatly improve the reliability and usability of your data.

FAQs about Data Quality Framework #

1. What is a data quality framework? #


A data quality framework is a structured set of guidelines, processes, and tools designed to ensure data accuracy, consistency, and reliability. It helps organizations manage and improve their data quality to support decision-making and operational efficiency.

2. Why is data quality important for businesses? #


High-quality data ensures accurate insights, supports compliance with regulations, and enhances decision-making. Poor data quality can lead to financial losses, inefficiencies, and reputational damage.

3. How do I implement a data quality framework? #


Implementing a data quality framework involves defining data quality goals, assessing current data quality, establishing governance structures, applying quality rules, and continuously monitoring and improving data processes.

4. What are the key components of a data quality framework? #


Key components include data quality standards, data profiling, assessment, governance, monitoring, and data cleaning processes. These elements work together to ensure ongoing data integrity and usability.

5. How can data quality frameworks improve business decision-making? #


Data quality frameworks provide reliable data, reducing risks associated with faulty analyses. They enhance trust in data, leading to better strategic decisions and improved operational performance.

6. What role does governance play in data quality frameworks? #


Governance ensures that data quality efforts align with organizational goals. It involves setting policies, assigning responsibilities, and creating accountability structures to maintain high data quality standards.

  1. How to Improve Data Quality in 12 Actionable Steps?
  2. Data Quality Measures: Best Practices to Implement
  3. Data Quality in Data Governance: The Crucial Link That Ensures Data Accuracy and Integrity
  4. 6 Popular Open Source Data Quality Tools in 2025
  5. Data Quality Metrics: How To Measure Them?
  6. Forrester on Data Quality: Approach, Challenges, Best Practices
  7. Gartner Magic Quadrant for Data Quality: Overview & Evaluation
  8. 6 Reasons Why Data Quality Needs a Data Catalog
  9. The Evolution of Data Quality: From the Archives to the New Age

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