Collaboration implies the ability for information to flow in multiple directions, such as from peer to peer or between a central hub and outlying endpoints. As anyone who has worked in a collaborative environment or on a team project can attest, there are both challenges and benefits to working in such an environment. It’s no different when we consider the role of collaboration in creating a geospatial intelligence (geoint) environment. 

Let’s begin with a review of geoint data workflows. There are many mechanisms currently used to facilitate geoint and knowledge sharing; for example, imagery and geospatial data files can be distributed via email, File Transfer Protocol, websites, shared electronic file systems or other methods. Another mechanism is shared access to databases containing geoint data or to geospatial servers. 

For geospatial data specifically, foundational data sets and any data with a geospatial component must be shareable. Information types include physiographic features and man-made infrastructure, demographics and human terrain, raw and processed/aggregated sensor data, and even analytic results. Sharing imagery and geospatial data is at the core of what geoint analysts do, but the real value comes in the ability to share insight derived from that information. 

Collaboration provides the ability to leverage the work of “the many” and move the discipline forward. We already see this through the use of message boards, blogs and wikis that provide access to “communities of interests” and subject-matter experts for the purpose of discerning quick answers to a plethora of questions, thus enhancing analytic efficiencies as well. 

Moreover, there’s an increasing role for the incorporation of cognitive neuroscience in the development of collaborative analytic process, technology and tradecraft. And when we have these things in place we facilitate the creation of relevant geoint, contextual knowledge and sharing pathways essential to developing deep insight and understanding to support well-informed course of action decision-making processes. 

Indeed, the ability to effectively operationalize these concepts enables us to support novel approaches to multidisciplinary collaboration and address the “I’ll know it when I see it” goal of good analysis. Anchoring analysis through the lens of the geospatial environment enables us to convey — visually — complex statistical results and relationships in an operationally relevant and actionable format, i.e., telling the end user, “Go here now and expect this.” 

The key to enabling a collaborative geoint solution involves creating an accessible computing ecosystem that brings together all the relevant data and exposes them to the community of users irrespective of location or organization. Think cloud-based and in-memory data provisioning and interactive analytics that enable even those working in far-flung environs to interact with others, to visualize and search across a large number of geoint layers and relevant contextual information fused together in a single interface.  

At a tactical level, a collaborative geoint environment is challenged by issues of accuracy and validity of data. When you have inputs from disparate sources across the community — even when collaboration is high — it is difficult to maintain data integrity or data standards, specifically variations in schema and attribution. Further, because geoint comes from many different sources, processing and loading the data so they are usable in a collaborative environment-ecosystem can pose challenges as well. Deconflicting scale is another challenge, as some users will be satisfied with country or small-scale data whereas others will want city- or large-scale resolution data sets. 

From a strategic perspective, organizational culture remains the greatest challenge to creating a collaborative geoint environment as both the technology and government policies necessary to support this are clearly in play. If we are to leverage the true power of geoint, military and intelligence community commands, services and organizations must move past the cultural stovepipes that inhibit the development of a collaborative computing ecosystem and data sharing for this purpose. 

Regardless of the challenges, DigitalGlobe is developing technology solutions designed to work in a collaborative geoint environment. 

First, our products adhere to Open Geospatial Consortium protocols and formats, so data can be shared quickly and easily across most geospatial platforms and tools. Sharing data from an enterprise (or central) data store via Web-enabled services allows for the most efficient storage and dissemination of geospatial data. 

Data formats and standards and communication protocols can help make the processing and ingestion stages of a shared geoint environment simpler, but this should not be a primary goal. There will always be data sources that don’t fit into a predefined structure, and focusing on standard sources typically means that nonstandard sources become a lower priority. A more powerful approach is to focus on the most valuable sources of information and develop the systems and processes to extract data from these sources, transform them as necessary, and load them into the shared environment.

Second, we are moving away from proprietary software applications, one-off systems and highly customized platforms, which will result in a tremendous benefit with regard to data delivery, dissemination and application. By keeping industry standards and or open source systems at the forefront of analytics and platform development, we ensure that customers are left with sustainable geoint solutions able to operate in the cloud architectures being proposed by our military and intelligence community customers. For example, we’ve been investing research-and-development dollars to build out a cloud-based terrain and sensor track analytic platform compatible with specifications suggested for the Intelligence Community Information Technology Enterprise.

Additionally, DigitalGlobe seeks to provide a lens to help our customers make sense of it all. We are swimming in data as a community, and it’s very easy to get lost without the right tools. To that end, we have developed advanced geospatial-based predictive analytic technology and qualitative and quantitative methods to move us beyond the map and image in order to tell the story of “show me where and there,” to rapidly reveal answers hidden in geoint data. Contextually this story is defined at the cross-section of what we see through imagery analysis, the man-made and physical environments, and through indexes we create from other data to geospatially characterize and visualize the human landscape. 

Using this approach can help the military and intelligence community leverage the power of geoint to extract unique information required to produce deep insights that can save resources, time — and even lives.

Ken Campbell is vice president of DigitalGlobe Intelligence Solutions.