We’ve shared several blogs about the need for data quality and how to stop data quality issues in their tracks. In this post, we’ll focus on another way to help ensure your data meets your quality standards on an ongoing basis by implementing and utilizing a data quality management framework. Do you have this type of framework in place at your organization? If not, you need to launch one. And if you do have one, there may be opportunities to improve it.

A data quality framework supports the protocols, best practices, and quality measures that monitor the state of your data. This helps ensure your data meets your quality threshold for usage and allows more trust in your data. A data quality framework continuously profiles data using systematic processes to identify and mitigate issues before the data is sent to its destination location.

Now that you know a data quality framework is needed for more confident, data-driven decision-making and data processes, you need to know how to build one.

Establish Quality Standards for Your Use Cases

Not every organization experiences the same data quality problems, but most companies do struggle with some type of data quality issue. Gartner estimated that every year, poor data quality costs organizations an average of $12.9 million.

As data volumes and the number of data sources increase, and data ecosystems become increasingly complex, it’s safe to assume the cost and business impact of poor data quality have only increased. This proves there is a growing need for a robust data quality framework.

The framework allows you to:

• Assess data quality against established metrics for accuracy, completeness, and other criteria
• Build a data pipeline that follows established data quality processes
• Pass data through the pipeline to ensure it meets your quality standard
• Monitor data on an ongoing basis to check for quality issues

The framework should make sure your data quality is fit for purpose, meaning it meets the standard for the intended use case. Various use cases can have different quality standards, yet it’s a best practice to have an established data quality standard for the business as a whole. This ensures your data meets the minimum standard.

Key Components of a Data Quality Framework

While each organization will face its own unique set of data quality challenges, essential components needed for a data quality framework will be the same. They include:

• Data governance: Data governance makes sure that the policies and roles used for data security, integrity, and quality are performed in a controlled and responsible way. This includes governing how data is integrated, handled, used, shared, and stored, making it a vital component of your framework.
• Data profiling: Actian defines data profiling as the process of analyzing data, looking at its structure and content, to better understand how it’s relevant and useful, what it’s missing, and how it can be improved. Profiling helps you identify any problems with the data, such as any inconsistencies or inaccuracies.
• Data quality rules: These rules determine if the data meets your quality standard, or if it needs to be improved or transformed before being integrated or used. Predefining your rules will assist in verifying that your data is accurate, valid, complete, and meets your threshold for usage.
• Data cleansing: Filling in missing information, filtering out unneeded data, formatting data to meet your standard, and ensuring data integrity is essential to achieving and maintaining data quality. Data cleansing helps with these processes.
• Data reporting. This reporting gives you information about the quality of your data. Reports can be documents or dashboards that show data quality metrics, issues, trends, recommendations, or other information.

These components work together to create the framework needed to maintain data quality.

Establish Responsibilities and Metrics

As you move forward with your framework, you’ll need to assign specific roles and responsibilities to employees. These people will manage the data quality framework and make sure the data meets your defined standards and business goals. In addition, they will implement the framework policies and processes, and determine what technologies and tools are needed for success.

Those responsible for the framework will also need to determine which metrics should be used to measure data quality. Using metrics allows you to quantify data quality across attributes such as completeness, timeliness, and accuracy. Likewise, these employees will need to define what good data looks like for your use cases.

Many processes can be automated, making the data quality framework scalable. As your data and business needs change and new data becomes available, you will need to evolve your framework to meet new requirements.

Expert Help to Ensure Quality Data

Your framework can monitor and resolve issues over the lifecycle of your data. The framework can be used for data in data warehouses, data lakes, or other repositories to deliver repeatable strategies, processes, and procedures for data quality.

An effective framework reduces the risk of poor-quality data—and the problems poor quality presents to your entire organization. The framework ensures trusted data is available for operations, decision-making, and other critical business needs. If you need help improving your data quality or building a framework, we’re here to help.

Related resources you may find useful:

·      Mastering Your Data with a Data Quality Management Framework

·      What is Data Lifecycle Management?

·      What is the Future of Data Quality Management

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