FirstEigen’s Autonomous Big Data Quality Validation Tool Wins Gartner Cool Vendor Award for Information Innovation and Governance

Share Article

FirstEigen recognized for innovative use of machine learning for data validation autonomously with DataBuck

FirstEigen

The tremendous growth of data volumes, coupled with the proliferation of data sources, have complicated the task of validating the elements of big data quality. We’re helping solve those problems by leveraging the latest in machine learning.

FirstEigen, provider of the autonomous big data validation and data matching tool DataBuck, today announced it was shortlisted by Gartner Inc. as one of three “Cool Vendors in Information Innovation and Governance, 2017.”

“The tremendous growth of data volumes, coupled with the proliferation of data sources, have complicated the task of validating the elements of big data quality,” said FirstEigen CEO Seth Rao. “As data moves between different systems or accumulates in data lakes, companies face challenges when trying to ensure the completeness, timeliness, consistency and validity of information. We’re helping solve those problems by leveraging the latest in machine learning and are proud to be recognized by Gartner as a disruptive and innovative service provider.”

According to Gartner, untrustworthy, low quality data not only reduces an organization’s ROI on its information investments, but also exposes them to increased business and regulatory risks.

FirstEigen leverages artificial intelligence and machine learning to make the data quality validation process seamless and intuitive with DataBuck, its self-learning and autonomous tool. DataBuck creates and constantly updates thousands of data validation checks without manual intervention. Built on the Spark platform with specialized algorithms, it is 10 times faster than any other tool or home-grown approach. Errors can be autonomously filtered in just three clicks.

“Data quality issues are always expected, and they are traditionally mitigated by hiring an army of programmers to trap them,” said Rao. “But it’s the unexpected data quality issues that pose a more serious business risk. Current tools use extensive coding to monitor for the expected issues, but only DataBuck uses machine learning to comprehensively identify risks from all types of data quality threats, including the unseen risks lurking in the shadows, and with absolutely no code to write.”

For more information about DataBuck, visit http://FirstEigen.com/DataBuck/ or reach out to FirstEigen at Contact(at)FirstEigen(dot)com.

[1] Gartner “Cool Vendors in Information Innovation and Governance, 2017” by Andrew White, Svetlana Sicular, Saul Judah, May 22, 2017

ABOUT FIRSTEIGEN
Greater Chicago-based FirstEigen was founded in 2015 with a focus to dramatically ease the efforts needed to validate Big Data Quality and Data Matching. With over 15 years of experience in the Data Validation space the team has leveraged Expert Learning algorithms to provide good data quality with minimal manual intervention and no coding. For more information visit us at http://www.FirstEigen.com and follow @FirstEigen on Twitter.

Share article on social media or email:

View article via:

Pdf Print

Contact Author

Jen Holmes
FirstEigen
+1 (385) 393-4436
Email >
Visit website