Accelerating GenAI Adoption Through a Data Operations Cloud

I first met Sanjay Agrawal and Shashank Gupta in the summer of 2021. They had built storied careers at companies like Amazon, Google, Meta, Microsoft and Yahoo, solving tough data analytics and infrastructure problems. They were part of the founding team at ThoughtSpot, one of the industry’s pioneering business analytics companies. They had decided to join forces to pursue their passion around data and embark on a new journey as CEO and CTO of Revefi, a next generation data operations cloud company. We led their $10.5 million seed round and partnered with them as they built the team and product.

Today, they have announced the Revefi Data Operations Cloud, which serves as a zero-touch co-pilot to data teams to monitor data quality, spend, usage and performance. By enabling data teams to get the right data into cloud data warehouses reliably, promptly and affordably, their organizations are able to use the data to make critical business decisions. They have already found product-market fit with a no POC/self trial model, and are demonstrating measurable ROI. Within weeks, one customer saw six figure savings, by using Revefi to uncover insights that allowed it to save nearly 30% on its total data warehouse spend while at the same time increasing its usage of the cloud data platform by 35%.

Three reasons I am excited about the next phase of the Revefi journey:

1. Data quality is a mission critical problem that has only become more real in the age of AI: According to Gartner, an enterprise incurs an yearly loss of $13-15M on an average due to data quality issues. According to Forrester, less than 10% of respondents believed data met quality standards. According to HBR, it costs ten times as much to complete a unit of work when the data are flawed in any way as it does when they are perfect.
2. Elevating data ops teams with a zero touch model will unleash their power.
   Data Ops teams spend a significant amount (in one case it reached a high >40%) of time on investigating and root causing SLA violations, and other data quality issues. With the exponential increase in the amount of data in the age of AI, and an ever-increasing number and variety of data sources, this pain is getting worse. On the flip side, if a business user sees a business-critical report where data does looks unexpected, the process of figuring out whether it’s even a problem, and if yes where (whether it’s in data, or semantics), will it resolve on its own (data is delayed) or needs intervention (unexpected nulls), results in more tickets against and thus more load on the Data Ops teams.
3. Building a company, vs just a product, is a superpower of serial entrepreneurs who understand the value of institutionalizing culture and surrounding themselves with excellence.

We look forward to partnering with Sanjay, Shashank and the Revefi team as they accelerate the adoption of GenAI.