By Peter Bailis - July 30, 2018
More data is recorded today than ever before, offering hyper-resolution into the environments and behaviors that define us. Despite this increasing potential, our tools haven’t kept pace. Manual analysis via spreadsheets, charts, and dashboards remain our primary tools but, when applied to today’s complex data, extracting value is slow, painful, and error-prone. When these tools fail, we turn to people, in the form of machine learning and data science teams. But these teams are scarce, even within the largest organizations.
To close the gap between recording data and acting on it, my research group at Stanford builds new interfaces, algorithms, and systems for making advanced analytics and machine learning usable. Over the past several years, we’ve worked with domain experts to make data-informed advances in the sciences, and with some of the most advanced companies to improve efficiency and reliability. These experiences have proven there’s an opportunity for a new kind of analytics that’s both more usable and more efficient.
After years of sitting on the sidelines at Stanford, I’m putting skin in the game. I’m taking a leave of absence from my tenure-track position at Stanford to found Sisu, a new company headquartered in San Francisco. At Sisu, we’re developing and applying cutting-edge technology to help people use data to make better decisions. We’re building a new analytics stack.
To maximize our impact, we’ve raised a $14.2 million Series A round of financing led by Andreessen Horowitz. In addition, Ben Horowitz, co-founder and general partner at Andreessen Horowitz, has joined Sisu’s board of directors.
While we’re currently in stealth, our team at Sisu is quickly growing, with deep expertise spanning machine learning, databases, and distributed systems. If you want to help build the future of data analytics, join us.