S3 Home‎ > ‎

Our Company

S3 Data Science began operations in 2015. S3 is small. We like being small. Bigger isn't necessarily better. Just ask the next dinosaur you see. S3's founders had worked together in a large engineering company for a decade. During that decade, we learned the advantages and disadvantages of being big. At the same time, we watched computation undergo remarkable transformations. While the data got big, nearly everything else got small. Mainframe computers virtually disappeared and the big calculations they did were often performed on laptops. Big reports were replaced by small visualizations that conveyed the information in a way that a busy executive could grasp instantly. Sensors the size of airplanes were replaced by microsensors the size of a rice grain. Even the company we worked for was feeling the pressure. They got a lot smaller, some of it through market forces and some of it deliberately. 

But small by itself is not enough. We studied these changes carefully and realized that small things were effective when they embraced Minimalist principles: those visualizations were more effective than the big reports because every pen stroke in a good chart carries information useful to the receiver. The laptops were effective because they delivered only the hardware that was useful for computing results, while avoiding the liabilities of large size, heavy weight, and excessive power consumption. Small companies were effective when they didn't have lots of bureaucracy that their customers didn't need to get the job done. 

Minimalism was an important guidepost when we decided to make S3. That Minimalism is manifest in lots of ways. Our logo, the pentakis dodecahedron, is a minimal surface (smallest polyhedron of 32 vertices containing the inscribed sphere). More materially, if an open source package will do the specific job we have in mind as well as a commercial package, we favor the open source package. If we have to choose between outsourcing and doing a job ourselves, we favor the latter because we might learn something in the process. We even make our own T-shirts and have gotten quite good at it. It's taught us to respect the guys up the street who make T-shirts for a living. We know a lot more now about the problems they face and how careful they have to be with their process and materials. Since our role is to help enterprises be more effective, learning how other kinds of businesses work is good experience.

Minimal doesn't mean easy. Minimal can often be very hard. Anyone who has studied Optimization Theory knows that finding minimal solutions is, in general, one of the hardest problems in math and engineering. But if you want to bring in a team to optimize your business, then all else equal, you're probably better off with a team that takes optimization seriously enough to minimize their own team's footprint.


Simple can be harder than complex: You have to work hard to get your thinking clean to make it simple. But it’s worth it in the end because once you get there, you can move mountains.”

Steve Jobs, BusinessWeek, May 25, 1998
S3 Data Science Logo
S3 Data Science, copyright 2016.