How to automate data quality processes

How to create bulk rules that you can use to streamline and automate data quality processes in your organization.

From reducing costs to improving efficiency, maintaining data quality improves the accuracy of analytics and improves business decision-making capabilities. However, for organizations looking to scale their data operations, simply having a data quality management strategy may not be enough.

SEE: Hiring Kit: Automation Specialist (TechRepublic Premium)

Manual data quality management approaches in particular can sabotage data quality, especially with the potential for data entry and other human errors. Aside from this potential issue, manual data quality management also requires hands-on tactical work from data professionals who might otherwise be working on more strategic business tasks. The simple answer to both of these problems? Find ways to automate your data quality processes.

Why data quality processes should be automated

Processes like manual data entry are tedious enough to easily introduce human error. Errors, ranging from a simple undetected typo to typing in the wrong field or a complete mistyping, can severely impact data quality.

SEE: Best practices for improving data quality (TechRepublic)

The solution to this common mistake lies in automating data quality processes, thereby accelerating and increasing both the efficiency and accuracy of data quality management. Because automation doesn’t suffer from fatigue or lack of focus, it’s not prone to the same data entry errors that humans struggle with. Proper configuration of automated data quality processes—using the right rules and integrations—ensures that data quality automation improves overall data quality.

Steps to automate data quality processes

Set data quality standards

A data quality automation strategy starts with understanding and establishing the importance of data quality to the business. Data quality indicators to be examined include accuracy, relevance, completeness, timeliness, and consistency.

However, how you approach these indicators depends on the organization’s goals and the nature of its data. For example, an organization could create software-based rules based on its business needs that govern operations and analytics.

Implement strict controls on incoming data

Using third-party data sources can result in working with large amounts of incorrect data. Incorporating such data into a company’s pipelines can be time-consuming and expensive. To avoid this, companies should consider implementing strict controls over all incoming data to verify data quality earlier in the process. Still, verifying data quality from these sources can prove challenging.

Automation can simplify these data quality checks for third-party data. Consider setting up automated data quality alerts that can flag anomalies, incomplete entries, and unusual data formats. With this approach to data quality automation, organizations can proactively address data issues before they enter their pipeline.

Define troubleshooting based on organizational use cases

Once bad data is discovered, troubleshooting comes into play to ensure bad data is handled correctly. In order to automate troubleshooting, it is first necessary to determine what can be automated and what requires the oversight of a data steward. This helps clarify who or what should solve each data problem, what can be done in specific use cases, and when problems should be escalated to a trained data expert.

Choose the right automation tools for your business needs

Automated tools save time, improve efficiency in flagging inaccuracies in data, and ensure data meets required quality metrics. However, choosing the right automation tools requires an understanding of the limitations of data quality tools. Data quality tools cannot repair completely corrupted data; They cannot cover the deficiencies of an organization’s data framework.

SEE: How do I become a data steward? (TechRepublic)

To get the most value from automation, organizations should conduct a thorough analysis of the right tools and platforms based on their business needs and data framework. They should test potential tools extensively to ensure they meet business needs while ensuring their employees have the technical skills they need to use those tools.

SEE: Top Data Quality Tools (TechRepublic)

Using such tools and platforms encourages a culture of collaboration by making it easier to move and replicate the processes of people, from business analysts to data scientists to automation specialists. These tools help organizations automate mission-critical tasks such as data discovery, data cleansing and transformation, and especially data monitoring and reporting.