Home / Perspectives / TechTarget: Data Quality for Big Data: Why it’s a Must and How to Improve It Want to learn more? CONTACT US Contact Us First Name*Last Name*Company*Work Email* What can we help you with?*How did you hear about us?I agree to receive marketing communications from Orion Innovation.* I agree to receive marketing communications from Orion Innovation. We are committed to protecting and respecting your privacy. Please review our privacy policy for more information. If you consent to us contacting you for this purpose, please tick above. By clicking Register below, you consent to allow Orion Innovation to store and process the personal information submitted above to provide you the content requested.PhoneThis field is for validation purposes and should be left unchanged. Home / Perspectives / TechTarget: Data Quality for Big Data: Why it’s a Must and How to Improve It 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. Industries Healthcare & Life Sciences COIs Data & Analytics