With data accumulation rates for most organizations at an all-time high and growing exponentially, it is more important than ever to harness your content and understand the context of your organization’s data holdings in order to get maximum value out of them. Characterizing and understanding your data holdings are the critical first steps, and while traditional analytic applications methods have served most organizational needs in the past, today's data volumes and varieties require organizations to up their game. Fuel's team of senior analysts and data scientists has extensive experience helping critical national security organizations construct advanced analysis shops and data warehouses, combining and analyzing disparate data to solve some of the nation’s most vexing intelligence problems.
Example: Analytic Technique
Fuel senior analysts and data scientists were integral to the design, formulation, and initialization of the advanced analytics data mart of a counterterrorism community client as it was set up shortly after the 9/11 terrorist attacks. While traditional all-source intelligence analytic techniques were the primary tools at the outset, the organization’s vision was to create an advanced analysis cell that would serve as a petri dish for exploring new and disparate data sources, emerging technology, advanced analytic tools, and imaginative methodologies to bring it all together.
As key members of this team, Fuel analysts and data scientists played a critical role in turning this vision into reality over a 10-year period. As project subject matter experts and respected expert sources of knowledge on some of our nation’s most challenging adversaries, the Fuel team was integral to understanding the data sources and their potential applications for the client. Together with government and consultant associates, we continue to apply advanced analytic techniques using big data against terrorist organizations to help keep our country safe.
Example: Modular Algorithms
For another assignment, Fuel senior analysts conducted a major performance analysis of the national technical means SIGINT architecture for a major Intelligence Community organization. The analysis leveraged data that was collected during a large all-service military exercise and resulted in the identification of several sources of inherent error in the overall system of systems. Subsequent to the analysis, these errors were fixed, and the client saw an overall improvement in SIGINT geolocation accuracies. Further, beyond the obvious long-term benefits of improved system performance, the analytic techniques and software scripts developed by the Fuel analysts provided payoff far beyond the initial architecture analysis, since they served as modular algorithms — effectively a library — available for subsequent studies.