Bigeye Raises $17 Million Series A to Build Trust in Data

Bigeye, the leading data quality engineering platform formerly known as Toro, today announced $17 million in funding to help organizations build trust in their data. Led by Sequoia Capital with participation from existing investor Costanoa Ventures, the latest round will be used to improve the platform and make it available to more data teams.

Data is increasingly core to modern business, woven into the products and services that directly affect customers and the health of the business. To keep pace, data engineering has increased in scale, complexity, and automation. As a result, human fail-safes no longer exist and operational issues like delayed, missing, duplicated, or damaged data cannot be detected manually. Software engineers address these issues with Site Reliability Engineering techniques, but data teams have lagged behind. Now, Bigeye is applying an engineering approach to data, making it effortless for data teams to measure, improve, and communicate data quality for their organizations.

“Data quality is hands down the biggest challenge most data teams face today, and they’re under enormous pressure to deliver high-quality data. But the tooling hasn’t caught up, demanding heroic levels of effort to keep things running smoothly,” said Kyle Kirwan, CEO and co-founder of Bigeye. “Our mission is to make it effortless for data teams to build and maintain world-class quality data for their organizations.”

Bigeye re-envisions data quality by applying proven engineering concepts from DevOps and Site Reliability Engineering (SRE). Unlike current solutions that rely on hand-written rules or armies of brittle test cases, Bigeye automatically instruments datasets and pipelines with data quality metrics, creating actionable alerts driven by anomaly detection techniques that enable data teams to prevent incidents from impacting the business.

Customers like Instacart, Crux Informatics, and Lambda School are already building greater trust in their data by leveraging Bigeye to measure, improve, and communicate data quality on hundreds of datasets with thousands of data quality metrics. The platform will continue to improve by deepening support for the data engineering workflow, doubling down on intelligence, and accelerating go-to-market to bring data quality to more data teams.

“Data quality monitoring will be as necessary for the emerging data stack as monitoring has been for every software application,” said Bogomil Balkansky, partner at Sequoia. “Having spearheaded the data quality team at Uber, Kyle and Egor have a clear vision to provide always-on insight into the quality of data to all businesses. We’re thrilled to partner with this exceptional team as they continue to build a product customers love and a market-leading company.”

“Bigeye enables customers to start monitoring hundreds of data quality metrics in a matter of minutes with minimal impact on warehouse load. There’s no solution on the market that can match the speed and scale of Bigeye,” said Egor Gryaznov, CTO and co-founder of Bigeye. “Our customers instrument metrics and discover unexpected data quality issues within 15 minutes of connecting their data warehouse.”

In addition to the funding, Bigeye is expanding its advisory board to include notable industry figures, like Olivier Pomel, CEO of Datadog; Brad Menezes, who headed the Application Performance Monitoring (APM) product at Datadog; Jai Ranganathan, who led the data platform team at Uber, and Ritesh Agrawal, who led infrastructural data science at Uber.

Customers use Bigeye both in the cloud and on-premises. Bigeye’s SaaS platform is SOC2 compliant for customers that prefer the fully-managed SOC2-compliant SaaS workspaces while other teams, like Instacart, run Bigeye on-premises, giving them control with the same level of capabilities and anomaly detection.

As part of the continued improvement of the platform, Bigeye has also increased support for Service Level Agreements (SLAs), which help data engineers build trust through transparency with their data users. Read our blog for a deeper dive into the importance of service-level agreements for data teams.

To learn more about Bigeye and the latest round of funding, including a message from the founders and customers visit www.bigeye.com/series-a

About Bigeye

Bigeye is the leading data quality engineering platform designed to help data teams build trust in data. Bigeye combines automatic instrumentation, robust anomaly detection, and extensive customization to provide always-on insight into the quality of every table in every data pipeline. Bigeye can be deployed in as little as 15 minutes and enables teams to continuously monitor all aspects of their data quality, proactively detect and resolve issues, and ensure that every user can trust the data. www.bigeye.com

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