Data quality framework: What it is and how to implement it

Most organizations value data quality, but what principles and plans do they use to implement it? Your plan surrounding data quality, how to improve it, and how to address it in your organization is called your "data quality framework."

Data quality frameworks can come in many forms. What might work for a larger corporation may not be feasible for a smaller operation, and vice versa.

In this blog, we'd like to address the concept of a data quality framework, what it means, and why it's important to have one in your organization. Let's get started.

What is a data quality framework?

Data quality frameworks include the tools and processes surrounding an organization's initiative to improve data quality. Typically, they include the system and tooling for tasks such as:

• Data profiling. Categorizing and assessing data and what the records contain.
• Assessing data quality dimensions. Tools to evaluate data quality dimensions: completeness, accuracy, consistency, uniqueness, timeliness, and validity.
• Writing data quality rules. Rules for how data should be handled and to determine its quality.
• Creating and enforcing data governance. Processes, roles, and responsibilities surrounding your data landscape.
• Data cleansing and enrichment. Going into records to clean up dirty data and improve the assets.
• Continuous monitoring. The ability to continuously and automatically monitor your system for data issues.

Ultimately, your organization's plan for addressing all these tasks will become your data quality framework.

What is the purpose of data quality frameworks?

Data quality isn't something you can address in an ad-hoc manner. You need a strategic and measured approach to ensure your data's accuracy, consistency, and reliability. Data quality frameworks are important because they allow you to:

• Define and enforce data quality standards. Describe what your organization views as high-quality data and ensure everyone adheres to it.
• Establish data quality processes and procedures. Clearly define the way your organization handles data quality and processes data.
• Facilitate data quality assessments and monitoring. Encourage more people to take data quality seriously by continuously assessing it and monitoring for unexpected issues.
• Promote data quality awareness and accountability. Improve your data culture, encourage users to take ownership, and have everyone contribute to issue resolution.
• Support compliance with regulatory requirements. Be more prepared for data regulations and what they mean for your business.

The importance of data quality cannot be overlooked, and it all begins with your data quality assessment framework. Approach the initiative in the most informed and measured way possible.

8 types of data quality frameworks

Now that we know what a data quality framework is and its purpose, let's explore the different types of data quality frameworks so you can find one that fits your unique business needs.

1. Total data quality management (TDQM)

Using this approach means you will holistically address data quality throughout the entire data lifecycle. People responsible for executing the framework will continuously define, measure, analyze, and improve data quality.

2. Data quality maturity model (DQMM)

This framework uses the concept of "data maturity" to gauge your organization's data quality capabilities. You will examine the various stages of data maturity and use your current level of maturity as a roadmap to define goals and processes for your framework.

3. Data management body of knowledge (DAMA DMBOK)

DAMA International, a nonprofit data management solution and education organization, originally developed this framework. It provides a "best practices" approach to data management, defining key concepts and offering guidelines for implementing them into your data systems.

4. Data downtime (DDT)

Data downtime measures the time data is unusable, unavailable, or low quality. The data downtime data quality management framework uses this metric as a basis for the necessary processes surrounding data quality, all of which aim to reduce DDT as much as possible.

5. ISO 8000

Is a universal language for data quality that provides guidelines and requirements for maintaining high data quality at an organization. It emphasizes several principles, such as "data is an asset" and "data quality is essential," to help businesses define and execute the processes that go into their framework.

6. Six sigma

Six Sigma is a data-driven methodology that uses a five-step process (DMAIC: Define, Measure, Analyze, Improve, Control) to improve business processes by identifying and eliminating defects, reducing variation, and optimizing efficiency.

Unlike some frameworks that focus on overall data management or specific data quality dimensions, Six Sigma provides a structured, project-based approach with a strong emphasis on statistical analysis and quantifiable results. This makes it particularly well-suited for tackling complex problems and achieving significant, measurable improvements in process performance and data quality.

7. Data Quality Scorecard (DQS)

A data quality scorecard is a report card for your data. It is an easy-to-access dashboard for information on the health of your data based on predefined metrics. While most data quality metrics offer improvement capabilities, data quality scorecards focus on measuring and assessing data quality to identify issues and track progress over time.

8. Custom frameworks

If none of these frameworks works for your business, consider creating a custom data quality management framework. Custom data quality frameworks allow enterprises to tailor their processes to their unique needs and capabilities. However, they can cost more upfront than pre-built/defined frameworks.

How to implement a data quality framework in your organization

Once you choose a data quality framework, you might ask yourself, "How do I get started?" Below, you'll find the seven steps you should follow when starting a data quality framework.

1. Assess your current data quality efforts

Starting from scratch is always harder than using what you have available. Before starting with a data quality assessment framework, assess what tools, processes, standards, and policies you have surrounding data. This way, you can evaluate strengths and weaknesses, define areas for improvement, and make the best use of your current resources.

2. Define your goals and objectives

Once you have a clear image of your capabilities, it's time to set goals and objectives for your data quality framework initiative. Is there a specific type of data you want to improve first? Do you have a data quality score you'd like to reach in a given timeframe?

Set these objectives, along with the processes and tasks that will get you there, and then begin incrementally checking off boxes until you reach your goal.

3. Determine the right framework for your organization

Using the list above, measure the different types of data quality management frameworks against your organization's budget, capabilities, and internal policies. Find a solution that works to your strengths but also addresses weaknesses effectively.

4. Develop policies and procedures

A data quality framework is only practical if the business has already set clear policies and procedures surrounding its use of data. This is called "data governance and management" and involves the creation of internal policies that deal with data access, user responsibilities, lifespan of data, and much more.

5. Invest in the right technology and support

No one can manage data quality without the correct tooling. Choose a data quality solution that caters to your business's needs in an automated and user-friendly interface that your users will enjoy using. Not all data quality tools are created equal. Do your research into price, processing times, setup, etc.

It's even better if your data quality software has AI-powered capabilities.

6. Roll this out throughout the organization

Keeping your data quality framework exclusive to your data teams won't help much. You will need enterprise-wide buy-in for it to be executed effectively. Staff training and a data-driven culture are two obstacles every data quality management framework must overcome to begin operating.

7. Monitor and adjust as needed

Data quality is a never-ending process. Data quality monitoring needs to be done continuously and automatically so issues can be identified and resolved constantly. As you improve, issue volumes will decrease, but they will never disappear completely. By being proactive, you can prevent them from reaching downstream systems and causing real harm.

Optimize your daily operations with a data quality framework

Choosing the right data quality framework can differentiate between a successful data quality initiative and one that falls short. By carefully considering your organization's specific needs and objectives, you can select a framework that provides the necessary structure, guidance, and tools to achieve your data quality goals.

Remember that data quality is an ongoing journey, not a destination. Continuous monitoring, evaluation, and improvement are essential to maintain high-quality data and reap its benefits. Invest in robust data quality software and foster a data-driven culture to ensure your organization's data remains valuable.

Download our end-to-end data quality framework to learn more about data quality solutions and best practices.