Data Quality for Big Data: Why it’s a Must and How to Improve It

As volumes of collected data increase exponentially, methods to improve and ensure big data quality are critical in making accurate, effective and trusted business decisions.

By George Lawton, April 27, 2021

Data quality can be a major challenge in any data modeling project. Issues can creep in from sources like typos, different naming conventions and integration problems. But data quality for big data projects that involve a much larger volume, variety and velocity of data takes on even greater importance.

And because big data quality issues can create several contextual concerns related to different applications, data types, platforms and use cases, Faisal Alam, emerging technology lead at consultancy EY Americas, suggested adding a fourth V for veracity in big data management projects.

Why Data Quality for Big Data is Important

Big data quality issues can lead not only to inaccurate algorithms, but also serious accidents and injuries as a result of real-world system outcomes. At the very least, business users will be less inclined to trust the data and the applications built on them. In addition, companies may be subject to government regulatory scrutiny if data quality and accuracy play a role in front-line business decisions.

“Data can be a strategic asset only if there are enough processes and support mechanisms in place to govern and manage data quality,” said V. “Bala” Balasubramanian, Senior Vice President of Life Sciences at digital transformation services provider Orion Innovation.

Bala later provides best practices for managing big data quality.

Read the full TechTarget article here.

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