Provided here is our redacted investment memorandum, detailing our rationale for investing in Bigeye’s Series A in 2021.
COMPANY OVERVIEW
BigEye is a provider of data quality monitoring software founded by Kyle Kirwan and Egor Graznov, former Uber employees. The data reliability engineering platform detects and resolves data freshness and quality issues before their stakeholders are impacted.
Bigeye’s product supports fresh and high quality data for any organization using data for self service analytics, marketing or operations automation, in-product machine learning and more. They have reduced their mean time to resolution by 60% and replaced painfully manual test-writing and business rules with monitoring and alerting that any data engineer, data scientist, or analyst can configure on their own data in minutes.
KEY HIGHLIGHTS
- Team with first-hand experience: Kyle and Egor previously worked together at Uber, where they built a lot of the data quality infrastructure in-house. They know the problem space very well, and have already built solutions for it at massive scale. Many Uber affiliates are participating in this round, and Sequoia had a pre-existing relationship with their manager, Jennifer Anderson, who confirmed this was one of the biggest problems they had at Uber.
- Early signs of product market fit: Since launching their product and investing in sales, the company has really accelerated growth.
- High impact investor group: Sequoia is leading this round, and took a board seat on the company. They have already leaned in by connecting the founder with their head of talent and will work with them on customer intros. Costanoa has put their head of sales in close touch with the company and is helping them build that team. BigEye is well capitalized and has a strong support cast to go after a competitive market.
COMPETITIVE LANDSCAPE
Monte Carlo
Total Fundraising
- Founded in 2019, $41M from Accel, Redpoint Ventures, GGV Capital
Product
- Monte Carlo’s product is very similar to BigEye. They both offer modules such as freshness monitoring, data lineage, schema discovery, etc. They also allow teams to create troubleshooting playbooks to act on that data. We have never met Monte Carlo during their fundraising process, so we have limited visibility into their long term vision. Based on Sequoia’s feedback and market interviews, BigEye’s product seems to be superior in terms of experience and functionality.
Founding Team
- Barr Moses (CEO): Barr started her career at Bain working on strategy and M&A for tech companies. She then joined Gainsight, where she ended up as a VP Customer Success Ops. She left in December 2018 to start Monte Carlo.
- Lior Gavish (CTO): Lior started his career in the defense space, building radar systems for jets. He then worked at multiple tech companies like MobileSolid, Genieo Innovation, and Barracuda Networks. He joined Barracuda after selling his previous company, Sookasa, to them. That company was backed by Accel, a16z, and First Round. He joined Barr to run Monte Carlo in August 2019.
Superconductive
Total Fundraising
- Founded in 2017, $21M from Index Ventures, Charles River Ventures
Product
- Unlike BigEye and Monte Carlo, Superconductive has an open-source-first approach to the product, using their “great_expectations” product as a wedge. “Great_expectations” is a data testing framework that allows data engineers to confirm certain features of their datasets, create tests from the tests, as well as profiling it automatically. This then funnels into an enterprise version, which hasn’t really been launched at this time.
Founding Team
- Abe Gong (CEO): Abe’s background is in healthcare tech, where he was a data analyst / scientist. Superconductive was initially a healthcare-focused data analytics product that was rebranded in 2019 as an horizontal solution. “Great_expectations” was born out of this experience.
- James C. (CTO): James wasn’t an original founder, but joined as a CTO in 2019 when the company rebranded, so we will include in this analysis. Before joining Superconductive, he spent 13 years with the US Federal Government in various roles, including Chief Data Scientist. He studied Mathematics and Philosophy at Yale.