How To Plan a Successful Big Data Pilot

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Big data initiatives often begin with a pilot project. This approach can generate internal support to invest in larger big data initiatives by demonstrating the positive impact of data science and data-driven decisions on desired business outcomes (e.g. profits, margins, etc.) and business processes (e.g. creating rubrics for making decisions and metrics to measure the impact)

Nevertheless, executing pilot projects can be difficult, and many pilots don’t convert into larger big data projects. In this session we’ll explore the challenges of big data pilots farsite has encountered and suggest ways to plan and execute a success pilot.

Photo of Michael Gold

Michael Gold


Michael, co-founder of farsite, directs farsite’s overall strategy and manages business development.

Michael has a bachelor’s degree in classics from the University of Michigan and a master’s degree in history from the University of Minnesota. Michael also has an MBA from Fisher College of Business at The Ohio State University. He has worked as a White House Intern in the Office of Public Liaison Business Affairs Desk, and also for SmartBrief, a Washington-DC based start-up that publishes daily B2B news briefs.

Ryan McClarren


Ryan’s role with farsite is to take the totality of data science and statistics and guide our analysts to create insight for your high-consequence decisions. Part of this job requires in-depth expertise, both to know how to produce results as well as to know what is possible. The other part is listening to you to understand how our analysis will have the most impact.

Originally in high-performance computation for nuclear engineering application, he earned his undergraduate degree and PhD from the University of Michigan. Due to the stochastic nature of many nuclear processes, Ryan naturally began working in statistics, at first in Monte Carlo simulations. While at Los Alamos National Laboratory and as a faculty member at Texas A&M University, Ryan became a recognized expert in statistical methods for quantifying and reducing uncertainty in computer simulations.


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