Technical data

Bigeye tuna, which provides data quality automation, closes second round this year with $ 45 million – TechCrunch


Bigeye announced $ 45 million in Series B funding on Thursday, just six months after securing a $ 17 million Series A round.

Coatue led the new investment which included existing investors Sequoia Capital and Costanoa Ventures. Together, the San Francisco-based company grossed a total of $ 66 million, which also includes a $ 4 million seed harvested last May.

Company technology automatically recommends and monitors data quality, such as telling customers what type of data metrics to collect and alerting customers if there is a problem, such as when one of their ordering systems is in service. failure before it becomes a bigger problem.

Usage of the platform has doubled in each of the past four quarters, and the company has also attracted new customers, such as Clubhouse, Recharge and Udacity, prompting co-founders Kyle Kirwan and Egor Gryaznov to consider another round of funding. The co-founders met at Uber and worked on similar data quality issues.

“We really started out wanting to solve the people problem for us, but we didn’t anticipate the silent demand,” CEO Kirwan told TechCrunch. “Even in Series A, there was a demand, and to meet that demand, we had to grow our engineering team even faster. There is so much going on about the product – we have the nugget of the product today, but we want to go deeper like exploring when we detect data failures, how to avoid them next time, and how to best communicate them to the right person . “

In addition to engineering, the new investment will also fund the growth of the product and go-to-market teams. Bigeye currently has 25 employees and Kirwan would like to see 40 by the end of the year.

Having started by automating a way to pay attention to the right signal coming from data, Bigeye is now focused on helping data teams connect with the rest of their business when something isn’t working or isn’t working.

Kirwan plans to invest in how to speed up access to the root cause in order to avoid data failures in the future. Additionally, the company also examines repetitive tasks in a customer’s workflow to see if there is an opportunity for machine learning to automate it.

As part of the investment, Caryn Marooney, general partner at Coatue, joins the company’s board of directors. Coatue is one of Bigeye’s first clients; she got to see firsthand what the platform could do.

Marooney said she was drawn to the team’s experience with large-scale data quality monitoring at Uber, its approach to helping data teams measure and improve the quality of their data, and the high profile clients that the company serves.

Looking to the future, she sees data monitoring and observability as a key part of the modern data stack. Rather than looking at the data quarterly, companies use it every day to make business decisions and therefore need a reliable method to collect and use the data.

“Before, if you had bad data, your dashboards would break,” Marooney added. “Bad data today can disrupt your business. Bigeye was created by Data Teams for Data Teams, and we believe they solved this reliability issue for the most data-centric businesses.


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