Data quality engineering platform Bigeye today announced that it closed a $45 million series B round led by Coatue, with participation from existing investors Sequoia Capital and Costanoa Ventures. The company plans to put the funding, which brings its total raised to $66 million, toward scaling its team and platform with a particular focus on creating collaborative data reliability workflows.

Companies often struggle to manage vast pools of data stored across disparate systems on-premises and in private and public clouds. One study by PricewaterhouseCoopers and Iron Mountain found that while 75% of business leaders feel they’re “making the most of their information assets,” in reality, only 4% are set up for success. As the pandemic accelerates digital transformation and the data management stakes rise, data observability and monitoring tools have come into vogue. Eighty percent of teams within organizations are practicing, or intend to practice, observability within two year, according to a 2020 Honeycomb report.

Bigeye was founded in 2019 by Kyle Kirwan and Egor Gryaznov, who managed Uber’s first data warehouse for reporting and data analysis. The San Francisco, California-based platform augments instruments data with monitoring and anomaly detection tools, enabling stakeholders to know the health of the data via APIs and visual dashboards.

“With Bigeye, [we’ve] created a data observability platform that lets any company prevent customer-facing data outages, save expensive engineering hours, and build greater trust in the data,” Kirwan told VentureBeat via email. “The tools [we] developed helped Uber rapidly scale its data platform while ensuring reliability. Now, [we’re] applying those lessons and making them available to all companies, even those without Uber’s resources.”

Anomaly detection

As processes around data remain a hurdle in adopting technologies like AI, observability solutions like Bigeye are attracting investments. There’s Aporia, Monte Carlo, and WhyLabs, a startup developing a solution for model monitoring and troubleshooting. Another rival is Domino Data Lab, which claims to prevent AI models from mistakenly exhibiting bias or degrading.

As for Bigeye, it can proactively detect and resolve data issues — automatically recommending and monitoring key data quality metrics. Under the hood, anomaly detection algorithms adapt to changes in businesses without requiring manual tuning.

“In our mission to be the deepest and most accurate observability platform, Bigeye trains independent anomaly detection models for each data attribute tracked on the platform. Tens of thousands of unique models detect anomalies and learn from user feedback without requiring hand-tuning or guesswork. These models are the result of years of research and continue to be a key area of investment,” Kirwan added.

In each of the last four quarters, Bigeye, which has a 23-employee workforce that it plans to roughly double to 40 by 2022, says it added to its existing roster of customers across ecommerce, education, and telecommunications. Instacart, Crux, and SignalFire, and Udacity are using Bigeye to monitor data behind their analytics tools, while Clubhouse and Rev.com are using it to prevent disruptive data pipeline problems.

“We started our journey with Bigeye as a customer. 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,” Caryn Marooney, general partner at Coatue, said in a statement. “We are looking forward to partnering with Kyle, Egor, and the entire team as they continue to scale.”