Exsilon Analytics
Discovery Through Data

It is evident that today's businesses are in short supply of enough skilled analytical resources to analyze and interpret the enormous amount of data at their disposal. According to research by McKinsey Global Institute (MGI), by 2018 the United States will experience a shortage of up to 190,000 skilled data scientists, and 1.5 million managers and analysts capable of extracting actionable insights from the Big Data deluge.

That is where Exsilon, a San Francisco based statistical consulting and training firm comes in. The company blends traditional consulting services with a highly customized approach to education and training. "Our business model is to compliment our data analysis efforts with hands-on workshops and detailed technical documentation to train a company’s internal resources so they can learn how to apply analytical tools to solve other problems," says Kevin Potcner, CEO of Exsilon.

The Exsilon teams consists of statisticians, data scientists, and instructional designers with a strong passion for education within corporate environments in addition to their technical experience applying statistical techniques to solve problems across a wide range of functional areas including R&D, operations, marketing, quality, and product development.

The Exsilon team has supported myriad industries that leverage analytics including automotive, biotech, consumer goods, energy, financial services, healthcare, medical devices, pharmaceutical, and retail. "These industries are experiencing not only a shortage of data scientists able to use highly advanced techniques but a sufficient level of analytical literacy across their entire organization. To build a solid analytics culture, companies will need to invest not only in technology and employing highly skilled data scientists, but in developing a base level of understanding about analytics across all those who consume and use the results," Kevin shares.

One of Exsilon's core principles to their approach is to provide an authentic description of the benefits and short- comings behind every analysis without conveying a false-level of precision in the results. "For companies to effectively use data, we believe they should be aware of the specific conclusions an analysis does and doesn’t support. That includes an understanding of the uncertainty, risk, and error as well as a honest recognition of where an analysis falls short. We think this helps decision-makers accurately incorporate the results of a statistical analysis into their decisions, better compare and integrate results from different analyses, and cultivates thoughtful ideas for next steps," explains Kevin.