By Chitra Sivanandam, VP Analytics
When the average person on the street hears “data analytics,” their mind probably goes to some scene from Moneyball with Brad Pitt making an amazing choice for his next utility infielder based on esoteric stats handed to him by data wizard Jonah Hill. The process of how those numbers were generated, tabulated, and analyzed is left to the imagination.
In reality, data analytics sit at the tactical edge for our front-line warfighters, in the command center of battlefield leaders, and in the offices of defense policymakers. Data analytics are neither confined to a single type of decision maker nor to a single point in the life cycle of an event, battle, or decision. There is no clean line on the collection of data and its analysis…we don’t get to stop the mission while we examine what we have so far. As intelligence is collected, the analysis is creating even more data, revealing unknown correlations and insights.
Across the board (space, time, mission, etc.), we need to connect the dots. The way we do that is by becoming a broker of efficiency, which means some difficult prep work.
For a baseball general manager, he or she is presented with a vast array of data that is all measured and captured in the same way. Every baseball team, every sports reporter, every fan, and every data analyst has access to a library of statistics that are commonly understood and accessible. All they have to do is decide how to analyze that high-quality data. Those of us supporting warfighters at the edge do not have that luxury.
Quality is in the details
For really good use of data, we need common nomenclature and ontology that normally doesn’t exist. Imagine a grand old house with a several layers of decades-old paint sluffing off. You’re hired to fix it. You could simply open a can of paint and start applying a fresh coat. This will look passingly good for a bit, but the underlying dirt and previous paint jobs will soon have the house looking a mess again.
A good painter is going to do the really hard prep work--scraping, sanding, and priming--before applying a finish coat. A really, really good painter might even take the time to tape off the windows and shore up non-plumb walls, allowing the use of a sprayer instead of a brush. Really good data analytics demands the same approach.
We spend the majority of our time creating solutions that bring our intelligence streams into a common syntax for analysis. This is hard because 1) we are always making judgment calls because various streams are not of the same quality, 2) there are now interactions between data streams that never comingled previously, and 3) we have to ask if we are simply layering analyses or truly taking advantage of data interaction. As you can see, the work flow is as important as the analysis.
If we do the proper prep work, we can see correlations and insights that were previously obscured by the layers of differing nomenclature. The comingling of data streams now creates whole new data sets for explorations and exploitation. Applying human ingenuity, technologies like machine learning and artificial intelligence, and mission understanding, we can bring the right information to the right decision maker at the right time. We declutter the noise and become a broker of efficiency.
About the author: Chitra Sivanandam is an executive-level subject matter expert and strategist with deep expertise in the development, operations, and execution of technologies and business models. Her varied background ranges from traditional aerospace/intelligence community/Dept. of Defense to working at the crossroads of public and private interest. She earned her bachelor of science in imaging science from Rochester Institute of Technology and MBA in finance from Wharton School of the Univ. of Pennsylvania.