Bigeye Observes $45 Million in Funding

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Data observability startup Bigeye, which uses statistics and machine learning to detect inconsistencies and other problems in data, has netted $45 million in a Series B round that it will use to accelerate the company’s growth. Bigeye co-founder and CEO Kyle Kirwan says data observability’s hook for enterprises is the growing need for better data quality.

“There is a lot of momentum around data observability–and data platform tools overall–right now, and it’s only going to increase,” Kirwan tells Datanami. “We see data democratization and data mesh efforts continuing to add pressure to data platform teams to move fast while keeping the data reliable. As a result, observability will be an industry-wide need, as it has been at companies like Uber, Airbnb, and Netflix for several years now.”

Kirwan, his Bigeye co-founder and CTO Egor Gryaznov, and Bigeye’s Chief Data Science Henry Li helped build Uber’s data observability platform, called Data Quality Monitor (DQM), which was responsible for flagging data problems in Uber’s massive data warehouse. Now it’s hoping to take the learnings from building DQM to build a data observability platform for the masses.

“Observability isn’t a new concept in the world of data,” Kirwan says. “Internal teams at innovative data companies like LinkedIn, Netflix, and Uber quietly built observability tools for their internal data platforms because they were ahead of the tooling. We were one of those teams at Uber and the only one that has gone on to create a company to address this problem for the wider industry.”

Bigeye helps customers automatically monitor data quality in real time  (Image source: Bigeye)

Bigeye monitors nine categories of data quality issues using more than 50 pre-built metrics, as well as custom definitions, Kirwan says. It also pioneered an approach called autometrics to automatically track a unique selection of observability metrics for each customer, without requiring a bunch of manual effort.

Bigeye feeds this collected data into a model to train anomaly detection algorithms, which automatically flag sudden changes in data quality attributes. All of this enables its customers to adopt the service level agreement (SLA) approach to ensuring consistent data quality among their stakeholders (internal data scientists, analysts, and the like).

The company’s approach is resonating with data-savvy customers, such as Instacart, Udacity, and Clubhouse, along with companies in the financial service arena. It’s also resonating with Caryn Marooney, general partner at Coatue, the venture capitalist leading the $45 million round.

“We started our journey with Bigeye as a customer,” says Marooney, who also now sits on Bigeye’s board. “We were impressed by the strength of the platform, their unique approach, and how that approach directly related to the potential size of Bigeye’s opportunity.”

Kirwan says the funding (which also included participation from Sequoia Capital and Costanoa Ventures) will allow Bigeye to continue building out the platform. There are three specific areas of interest he’s eyeing, including:

• Enabling end-to-end development and release engineering (DRE) workflow, which will give engineers “more powerful tools to not only detect but communicate, resolve, and prevent issues proactively,” Kirwan says.
• Doubling down on intelligence. “We already have the best automation when it comes to automatically instrumenting the data with metrics and detecting anomalies, and we intend to keep pushing our lead in this area,” he says;
• More integrations.

The funding comes less than six months after Bigeye’s $17-million Series A, which was led by Sequoia. According to Kirwan, the company has doubled usage each quarter for four straight quarters. The San Francisco-based company has 25 employees now, and Kirwan expects that to grow by 40 by the end of the year.

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