Fresh and high quality data is a core requirement for any organization using data for self service analytics, marketing or operations automation, in-product machine learning, financial planning, and other mission critical use cases. Data teams at companies like Instacart, Udacity, Docker, Scale, and Clubhouse are using Bigeye to detect and resolve data freshness and quality issues before their stakeholders are impacted. They 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.
We believe that in the long term, more and more SaaS products will be highly data-driven, as mentioned in our Cube investment announcement. In order to get there, engineering and data teams will need to have a lot more confidence in their underlying data, which led us to being very interested in the data quality space.
Kyle and Egor, the founders of Bigeye, spent a few years at Uber building a meaningful part of the internal data stack, solving these problems at very large scale. While there are a few companies in this space, we believed they had the best product on the market, as well as deep domain knowledge to iterate it.