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S3 Value Proposition

Data science is about realizing the value of data. Data may be the most expensive thing an organization has to manage. It's not just the the cost of IT staff, the tech refresh, licenses, etc. It's about the whole business process. One of the main things business process does is produce data. Take an assembly line for example. It's probably the most expensive part of a flow shop business and while it produces goods, it also produces enterprise data that ultimately drives executive decisions. If the data isn't properly refined and interpreted, those decisions are not as good as they should be. 

If you have come to this site, you probably feel that you are not making the best use of your data and that your organization needs better data science and analytics capabilities. But a recent Gartner study found that in 2015, two-thirds of US data science jobs will go unfilled due to skill deficits. So in-house data science and analytics teams are expensive to build and hard to staff. 

We offer an alternative; we can build a data science capability for you. We can work with you to identify places where data science can help; we can model the issues, identify the data, and supply the algorithms. In some cases, our deliverable might be a single report to management. In others, we might come back monthly or quarterly, producing periodic reports and convening with decision makers. In many cases the data science needs to be online and continuously operating. We can build and configure the tools, and help integrate them into your enterprise. We can teach your staff to use the tools and interpret the results and we can support them when difficult questions come up.

Rajdeep and Jamie have been doing that since meeting at Lockheed in 2004. They have streamlined medical appointment management processes, optimized supply chains, identified new drug targets, etc. In most of those problems the data already existed. Data is gold! And like real gold, it can be expensive. We've seen this in military problems where “real” data may require a field exercise costing hundreds of millions of dollars, or a rocket launch taking years to plan. Also like real gold, once you get the data, it's heavy and hard to carry around and can sometimes seem like an anchor. Data science helps turn that anchor into an anvil, allowing you to hammer out more value from the data and make more sense of it.

Data science used to be done by big companies on big, centralized computers with big infrastructures that produced big reports with big tables requiring big advanced degrees to read and understand and big budgets to pay for. Rajdeep and Jamie met in an environment like that, and did some very complex and interesting data science. But computation has changed, and the people and organizations that do data science need to change too. Today, data science is done on small, portable laptop computers with almost no infrastructure. And while the big tables (or even bigger ones) are still available, the primary products are compact visualizations that a busy executive can understand at a glance. 

S3 mirrors this evolution in computation. Like a laptop computer, we're small, lean, adaptive, surprisingly well networked, and respond to stakeholder culture. This makes S3 a very good match to the data science problems many organizations face. And we have a very good skill mix. Some people can do math. Some people can program computers. Few people are good a both. Fewer still can use data as a canvas for their creativity. We think we've got all three and we think that gives us unique value. We'd like to show you how.


S3 Data Science
S3 Data Science, copyright 2015.