Today’s modern warfare landscape is complex, fast-paced and multi-modal – encompassing operations that span air, land, sea, space, and cyberspace. Operating in these environments requires mission data analytics capabilities that allow warfighters to rapidly process information in various forms and formats, analyze it and take decisive action.

Doing this well requires combining rapid, iterative software development with AI-assisted secure software engineering. This combination ensures that warfighters can process and act on complex, high-volume data in near real-time, while also allowing for the emergence of new capabilities to meet evolving mission demands. In our experience, working with customers in this capacity, we have identified four imperatives for successful mission analytics initiatives. They are:

Adopt Open Standards & Industry-Standard Formats and Approaches

To be able to easily and quickly add new data feeds and sensor data into your environment, and then to use the growing number of automated analytic tools, teams must adopt open standards and industry-standard formats and approaches. The power and value of doing this is that it enables flexibility and agility. No one knows what the next game-changing technology will be. No one knows what the next essential dataset will be – or its format or velocity or volume. Teams must have adaptability. They need to be able to allow low-code and no-code users to be able to quickly ingest a new dataset and prepare it for analysis. The ingest step shouldn’t be the bottleneck.

Enable Data Fusion with Fidelity

The future of engagements on a global scale will demand the ability to rapidly derive insights from a wide variety of sources. This could be for predictive logistics in a contested environment, intelligence analysis, or unifying track data for operations. Teams need to be able to ingest, exploit, protect, and disseminate data in the cloud, on-prem, and at the edge.

Create Nimble, Cross-Functional Teams

To quickly deliver new mission analytics capabilities to customers, we have developed an approach that utilizes Product-Oriented Delivery teams, or POD teams. These small, cross-functional teams include, for example, members with DevSecOps capabilities, a developer, a data scientist, an AI developer, a UI/UX person, and a project manager. Together, they engage in an agile user-centered discovery and design process before building, testing and refining a solution, accelerating the delivery of a minimum viable product (MVP) to users. This allows us to iterate quickly and to continually bring an expanded set of capabilities to the warfighter.

Prioritize Accelerated Data Transport

In the mission analytics domain, milliseconds matter. Teams must prioritize enabling the fastest and most unobtrusive ways to move data – again, by using common frameworks, interfaces and control standards. Otherwise, vendor lock-in will hamper the mission at best and ensure its failure at worst. Data bottlenecks or data losses are to be avoided at all costs when making predictions. The surest way to avoid them is through a focus on accelerating data transport via open standards and industry-standard approaches.

These imperatives stem from our work on customer programs, often involving GDIT’s digital accelerators. There, we develop and prove out repeatable processes that enable enhanced mission analytics capabilities. The Defense Operations Grid-Mesh Accelerator solution was developed this way and today it’s a highly customizable tool that customers can adapt for their unique needs. The digital accelerators enabled, for one customer, the rapid development of an open-standards based solution capable of ingesting hundreds of changing data sources and supporting changing analysis needs as well.

This kind of flexibility and rapid, open development work is precisely what today’s mission analytics demands require.