A data quality framework is the systematic processes and protocols that continually monitor and profile data to determine its quality. The framework is used over the lifecycle of data to ensure the quality meets the standard necessary for your organization’s use cases.

Leveraging a data quality framework is essential to maintain the accuracy, timeliness, and usefulness of your data. Yet with more data coming into your organization from a growing number of sources, and more use cases requiring trustworthy data, you need to make sure your data quality framework stays up to date to meet your business needs.

If you’re noticing data quality issues, such as duplicate data sets, inaccurate data, or data sets that are missing information, then it’s time to revisit your data quality framework and make updates.

Establish the Data Quality Standard You Need

The purpose of the framework is to ensure your data meets a minimum quality threshold. This threshold may have changed since you first launched your framework. If that’s the case, you will need to determine the standard you now need, then update the framework’s policies and procedures to ensure it provides the data quality required for your use cases. The update ensures your framework reflects your current data needs and data environment.

Evaluate Your Current Data Quality

You’ll want to understand the current state of your data. You can profile and assess your data to gauge its quality, and then identify any gaps between your current data quality and the quality needed for usage. If gaps exist, you will need to determine what needs to be improved, such as data accuracy, structure, or integrity.

Reevaluate Your Data Quality Strategy

Like your data quality framework, your data quality strategy needs to be reviewed from time to time to ensure it meets your current requirements. The strategy should align with business requirements for your data, and your framework should support the strategy. This is also an opportunity to assess your data quality tools and processes to make sure they still fit your strategy; and make updates as needed. Likewise, this is an ideal time to review your data sources and make sure you are bringing in data from all the sources you need—new sources are constantly emerging and may be beneficial to your business.

Bring Modern Processes into Your Framework

Data quality processes, such as data profiling and data governance, should support your strategy and be part of your framework. These processes, which continuously monitor data quality and identify issues, can be automated to make them faster and scalable. If your data processing tools are cumbersome and require manual intervention, consider modernizing them with easy-to-use tools.

Review the Framework on an Ongoing Basis

Regularly reviewing your data quality framework ensures it is maintaining data at the quality standard you need. As data quality needs or business needs change, you will want to make sure the framework meets your evolving requirements. This includes keeping your data quality metrics up to date, which could entail adding or changing your metrics for data quality.

Ensuring 7 Critical Data Quality Dimensions

Having an up-to-date framework helps maintain quality across these seven attributes:

1. Completeness: The data is not missing fields or other needed information and has all the details you need.
2. Validity: The data matches its intended need and usage.
3. Uniqueness: The data set is unique in the database and not duplicated.
4. Consistency: Data sets are consistent with other data in the database, rather than being outliers.
5. Timeliness: The data set offers the most accurate information that’s available at the time the data is used.
6. Accuracy: The data has values you expect and are correct.
7. Integrity: The data set meets your data quality and governance standards.

Your data quality framework should have the ability to cleanse, transform, and monitor data to meet these attributes. When it does, this gives you the confidence to make data-driven decisions.

What Problems Do Data Quality Frameworks Solve?

An effective framework can address a range of data quality issues. For example, the framework can identify inaccurate, incomplete, and inconsistent data to prevent poor-quality data from negatively impacting the business. A modern, up-to-date framework can improve decision-making, enable reliable insights, and potentially save money, by preventing incorrect conclusions or unintended outcomes caused by poor-quality data. A framework that ensures data meets a minimum quality standard also supports business initiatives and improves overall business operations. For instance, the data can be used for campaigns, such as improving customer experiences, or predicting supply chain delays.

 Make Your Quality Data Easy to Use for Everyone

Maintaining data quality is a constant challenge. A current data quality framework mitigates the risk that poor quality data poses to your organization by keeping data accurate, complete, and timely for its intended use cases. When your framework is used in conjunction with the Actian Data Platform, you can have complete confidence in your data. The platform makes accurate data easy to access, share, and analyze to reach your business goals faster.

Related resources you may find useful:

• 7 Ways to Stop Data Quality Issues in Their Tracks
• Discover the Top 5 Data Quality Issues – And How to Fix Them!
• What is Data Lifecycle Management?

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