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How to Maintain Data Quality for Real-time Data Flows

Data quality is the basis of smooth operations and reliable outcomes of analytical algorithms, AI, and machine learning models. Traditionally, data quality monitoring is focused more on maintaining data quality when data is at rest than on ensuring that data is of good quality when it is in use. Handling data only when it is at rest is a difficult way to to maintain in today’s fast-paced data environments, and implementing a data-quality solution into an existing data solution can be daunting.

As the world becomes more and more digitized, data movers need to rapidly exchange and integrate data, creating a demand for quality assurance of data in motion, not just at rest.

Depending on the user and use case, specific rules might be applied differently as data flows into various destinations and is used for different purposes. When data is being streamed in real-time, high-quality data needs to feed into operations or analytics systems without interruptions instead of waiting to be examined. This necessitates an in-motion, user/use case-specific way of monitoring data that ensures quality while maintaining efficiency and data relevancy and timeliness.

The Challenge

Most data-monitoring software on the market can follow quality parameters and compare all data to established rules, but many existing tools isolate data-quality capabilities and disconnect data governance from other data needs. During the journey of translating data into business value, users have to fulfill data needs on different platforms, making traditional data solutions less efficient.

The most time-consuming steps in this process are inspecting data quality, waiting for humans (data stewards or data engineering) to respond, and fixing identified problems. Running the data-quality system outside of the integration process would be extremely time-consuming—detecting data quality problems would require digging through a  long and complex log, in addition to requiring time to remediate errors. By the time data reaches its destination in this scenario, operations, analytics and decision making will be significantly delayed.

Choosing the right data quality validation tool for a custom data solution can require a compromise and often creates gaps in integration. Additionally, because data needs to be validated and checked before it is injected into any warehouses, applications, or analytical pipelines, slowdowns and delays are now built into every data process. Data quality management, therefore, should not be a stand-alone program but needs to instead be closely aligned with data integration to avoid downtimes and optimize efficiency.

The Solution

Nexla blends data-quality processes into the integration process by automatically monitoring data  as it flows in real time. As new data is ingested, Nexla constantly integrates metadata intelligence to the data. Nexla learns your data, then automatically applies smart validations and enriches the data. A user can also very easily create their own validations using our data validation engine; mark attributes as required; check for values, patterns, types; etc. All of these tasks are done during the data flow.

Instead of waiting for your customers to tell you about  incorrect order contents, you can use Nexla to detect errors as soon as they are observed and send  in notifications and alerts that  can also be customized based on your schedule and needs. This way, your data engineering team can easily trace back errors without digging through log files and fix them without disrupting your operations. With Nexla, you do not have to worry about bad data blocking your data pipelines. Nexla automatically quarantines bad data so that your data flows do not stop running.

When you transfer data from one place to another, you can set specific rules for output validation. In this example, the customer data needs to adhere to  two rules: the output record must contain CUSTOMER_EMAIL, and the IP_ADDRESS must be of type IP v4. You can add, edit, and delete any validation rules without coding. Validation is automatically applied as data is flowing into the destination.

As data is checked for errors and inconsistencies, Nexla sends notifications detailing the exact processes and steps in which errors occurred and quarantines affected data without interrupting the data flow. Depending on your quality criteria, you can customize the notifications sent.

Under the “flows” overview tab, you can check for error labels in any dataset and make annotations. The clear view of data lineage allows you to pinpoint where the errors occurred without tracing through all branches of the data flow.

In the “dashboard,” you can click on data flow summaries to view detailed lists of errors. Each error report will direct you to the specific corresponding location so that you can easily inspect and fix errors with a few clicks.

Conclusion

Data quality is an essential part of a unified data solution; data delivered quickly to the right place is useless if it is incorrect or outdated. Speed and convenience are not useful without data quality, and any validation process must be able to keep up with the rest of the data solution.

Implementing a unified solution with quality validation built in at multiple points is the simplest, most effective approach, and Nexla’s seamless approach to making data ready-to-use in any format includes maintaining data quality throughout the process. By keeping everything in a single platform with a low/no-code interface, Nexla offers a significant reduction in engineering costs as well as efficient operations and analytics built upon high-quality data that you can trust.

During this past year, billions of records of robust, quality data passed through Nexla to inform dinner orders, tutor availability, grocery inventories, and many more. If you’re ready to trust your data, get a demo or book your free unified data solution strategy session today. For more on data and data solutions, check out the other articles on Nexla’s blog.

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