In June 2020, the U.S. Geospatial Intelligence Foundation held one of their weekly forums on data management, featuring data and analytic speakers from the National Geospatial-Intelligence Agency (NGA), a combat support agency under the United States Department of Defense and a member of the United States Intelligence Community with the primary mission of collecting, analyzing, and distributing geospatial intelligence in support of national security.
Many of the themes discussed — democratization of data, automation of processes, and the upskilling of people — serve as key pillars of Analytic Process Automation (APA).
For NGA, and many government organizations, the imperative is clear that any technology that is implemented needs to integrate within operational realities, support the cultural goals of infusing data to support missions and improve the user experience, reduce mundane tasks, and accelerate insight.
Instead of six to eight different point solutions for data preparation, analytics, reporting, and business process automation, which results in a disconnected experience with slow or misaligned outcomes, APA platforms converge all automation capabilities into one.
At NGA, there is no lack of data. As one speaker explained, “there is an onslaught of all kinds of data, including textual, rational, streaming, file-based and relational, but at NGA there is a need to bring it together and find correlations in space and time.”
With all this data and its related complexities, the first mile challenge is understanding what data is available, its structure, quality, how much it has been used, and whether it’s trusted.
As one panelist described “we have to have exceptional search, translation and understanding of what data is available, how it can be used, and how it can be aggregated with other sources to conduct analysis to support mission outcomes.”
When looking at this issue of access and availability of data, agencies like NGA are actively looking to industry leaders like Alteryx that can provide platforms to aggregate data, connect many disparate databases and data sources, and bring these data sources together so that analytics can be done to find the “interesting, important insight from an intelligence perspective.”
This ability to democratize data by making it available, discoverable, and accessible is a key pillar of APA, and analytic platforms need to enable this ability, reduce complexity, and automate the mundane and manual efforts that are the big time-sucks in many legacy analytics efforts.
Another key point discussed was the need to better leverage automation in not only making data available, but also providing the enterprise best practices for usage, governance, and overall management of assets. If the platform can automate the mundane tasks associated with data, it will enable a large group of skilled analysts and domain experts to get to the important stuff much more effectively.