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2017-06-19 Print

Autonomous Big Data quality validation tool wins Gartner Cool Vendor award

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.


CHICAGO – June 8, 2017 – Machine Learning has enabled the ability to autonomously validate Big Data Quality and Data Matching. FirstEigen’s DataBuck, the leading tool in this space, has now been recognized in Gartner Inc.’s short list of three “Cool Vendors in Information Innovation and Governance, 2017”[1].

The tremendous growth of data volumes and proliferation of data sources have enormously complicated the task of validating the elements of Big Data Quality (completeness, timeliness, uniqueness, reasonableness, consistency, validity, data drift, and data matching) as data moves between different systems or accumulates in Data Lakes. Gartner reports [1] that, 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’s tool, DataBuck, leverages AI and Machine Learning to make Data Quality validation self-learning and autonomous. It creates and constantly updates 1000’s of data validation checks without manual intervention. It is built on Spark platform with specialized algorithms and is 10x faster than any other tool or home-grown approach. Errors can be autonomously filtered in just three Clicks.

Data quality issues are always expected. They are usually mitigated by hiring an army of programmers to trap them. Unexpected data quality issues on the other hand, are not spotted as the programmers are not anticipating them. They pose a more serious business risk. Current tools are only capable of checking for the former with extensive coding, but not the high risk latter. DataBuck uses Machine Learning to comprehensively identify risks from all types of Data Quality threats.

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

Gartner does not endorse any vendor, product or service depicted in their research publications. Gartner research publications consist of the opinions of Gartner’s research organization and should not be construed as statements of fact. Gartner disclaims all warranties, expressed or implied, with respect to this research, including any warranties of merchantability or fitness for a particular purpose.

Contact Info
FirstEigen, Jen Holmes
1212 S. Naper Blvd, Suite 119-220
Naperville, IL 60540

Phone: 385-393 4436