Performance Analytics Division

The Performance Analytics Division(PAD) works to establish an enterprise-wide scalable data analytics capability that enables both analysts and their leaders to anticipate and solve problems, optimize resourcing decisions, and deliver enhanced readiness.

Army Data Science Ecosystem

About Performance Analytics

The PAD further coordinates and synchronizes Army Business Mission Area (BMA) strategic leader priority analytics projects and accelerates solutions in order to deliver optimized solutions to analytical challenges. PAD additionally works towards integration of the analytics federation by creating cross-functional analytics project teams matrixed from across the Army Analytics Federation to execute cross-domain coordination and synchronization for enterprise analytics objectives, requirements, resources and activities.

PAD also serves to create an enduring cultural shift to a point where Army Leaders understand and leverage data analytics as a key enabler of the Information Age Army. In addition, PAD contributes to shaping and developing the analytics workforce through the spirit of competition, leveraging the Deep Green professional development challenge and competition to that end.

Community of Interest Data Science & Intelligent Automation

The Army's Community of Interest (COI) for Data Science and Intelligent Automation is a collaboration and learning forum for DOD Data Scientists and Operations Research/System Analysts(ORSAs) to address Data Science & Intelligent Automation-related issues and share knowledge with respect to analytics platforms, tools, methods, etc.

Learn More
Headquarters Analytics Lab (HAL) Use Cases

A Use Case is the justification for initiating or undertaking a project or task. Use Cases evaluate the benefits of a solution to a defined problem as well as the benefits and cost in both budget and manpower for various possible solutions.

Learn More

Deep Green

The Deep Green initiative run by the Army Office of Enterprise Management is an ongoing series of professional development computer science- and data science-based competitions. Deep Green will leverage crowdsourcing environments to identify data-driven solutions for the Army’s most challenging problems. Each challenge will be a world-class event building on the lessons learned from previous competitions, allowing commands, domains, and other Army and Defense communities to better see themselves through the lenses of “big data” and Enterprise Resource Architecture (ERA). By seeing themselves better, communities will be better equipped to reshape their information systems for post-ERP implementation strategies.

The Deep Green challenges will be defined and directed by one or multiple Sponsors that face technological gaps in current data realizations and system implementations. To the extent that such gaps are primarily or at least largely data-based, artificial intelligence, machine learning, big data, analytics, and/or computer science-based solutions should be applicable. The Deep Green initiative will provide the Sponsor(s) an instant force multiplier at little or no cost compared to traditional approaches such as grants or in-house research and development initiatives.

Challenge participants will be given the opportunity to learn state-of-the-art techniques in the stimulating context of an expert-monitored, cooperative-competitive environment. A small number of the top-performing competition teams will be invited to give presentations of their methodologies, results, and future recommendations to Sponsor representatives, subject matter experts, and OBT initiative overseers. The first place award will be given out to the team/individual that performs best in both the modeling and final presentation. At the Sponsor’s discretion, a runner-up award may also be presented. The first place awardee will also be invited to present findings to a larger Community of Interest of fellow data analytics professionals. Challenges may comprise either a single competition or several, and may be expected to take from several months up to two years to complete.

Learn More