<p><strong>SAN FRANCISCO</strong> &#8212; Bigeye, the provider of a leading data observability platform, has snagged $45 million in Series B funding. Bigeye says it has doubled usage each of the last four quarters and added to its existing roster of customers, like Instacart, with new customers, including Clubhouse, Recharge, and Udacity. Led by Coatue and with participation from existing investors, Sequoia Capital and Costanoa Ventures, the latest round follows on a $17 million Series A from just 6 months earlier and brings total funding to $66 million.</p>
<p>With the funding, Bigeye will scale its team to address the rising demand for data observability, continue building on its product leadership, and help more data teams prevent customer-facing data outages, save expensive engineering hours, and build greater trust in the data.</p>
<p>“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&#8217;s opportunity,” said Caryn Marooney, General Partner at Coatue, who is joining the Board. “We are looking forward to partnering with Kyle, Egor, and the entire team as they continue to scale.”</p>
<p><b>Helping modern data teams move faster with confidence</b></p>
<p>Bigeye’s customer roster covers a growing range of industries, including food delivery, financial services, machine learning providers, ed-tech, and more. Data teams use the platform to quantify and improve data quality and ensure reliable data pipelines for business-critical applications, including:</p>
<ul class="bwlistdisc">
<li><b>Self-service analytics: </b>Bigeye customers like Instacart and Udacity make data-driven decisions an integral part of the way they grow their customer bases. Their data teams leverage Bigeye to monitor the data behind crucial analytics tools and ensure that strategic growth decisions are made on high-quality data.</li>
<li><b>ML and AI initiatives: </b>Bigeye customers like Clubhouse and Rev are innovating with ML and AI to improve service and better engage their users. With Bigeye, data engineers can proactively prevent disruptive data pipeline problems from reaching their data scientists, who can then spend more time on high-value modeling activities.</li>
<li><b>Third-party data: </b>Bigeye customers like Coatue and SignalFire ingest data from a huge variety of sources. They need to know that the data arrives on schedule and meets their quality standards at all times. With Bigeye, customers are able to automate that monitoring, giving them broader and more comprehensive visibility and ensuring that their data team workflows are never disrupted.</li>
</ul>
<p>“Ensuring data quality doesn’t mean you have to go slow. In fact, if you address data quality with Bigeye, your team can actually move faster because they have trust in the data,” said Dustin Pearce, VP of Infrastructure at Instacart.</p>
<p>&#8220;We&#8217;re a small team, and we serve a massive community. Bigeye&#8217;s monitoring tools help us know that our data is accurate and up to date, no matter how fast we&#8217;re shipping,&#8221; said Kenny D&#8217;Amica, head of data science at Clubhouse.</p>
<p>“We have a strong data-driven culture. Our business analysts, data scientists, and data-savvy business users rely on key data sets on a daily basis to better serve our millions of students. It’s imperative that we prevent anomalies from slipping through and negatively affecting their analysis. With Bigeye, we have an integrated one-stop solution for monitoring the health of our data — the ultimate answer as to whether our data is good or not,” said Simon Dong, head of data engineering at Udacity.</p>
<p>“With the complexity of our data and the rate of change our business is undergoing, we needed a different approach to data quality. Bigeye provides an accessible, powerful, and agile data observability platform that benefits our entire organization. On day two of using Bigeye, we were putting checks in place to prevent issues that could have otherwise negatively impacted our business. By week three, we had elevated trust in our critical datasets and empowered the SMEs on our analytics teams to measure new context about what good data quality is,” said Yuda Borochov, CDO of Zip Co.</p>

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