暗网吃瓜

By Mallory Arnold

It is no secret that predictive analysis helps businesses make informed decisions, avoid risk, enhance security, and improve efficiency.

The Department of Defense (DoD) is no different. The DoD has adopted a Data Strategy to become a more data-centric organization. In the opening of the strategy document, Deputy Secretary of Defense David Norquist called on all defense leaders to “treat data as a weapon system and manage, secure, and use data for operational effect.” The DoD鈥檚 data strategy emphasizes the importance of data for survival on the modern battlefield. When information exists in silos it is unable to be leveraged to our military advantage.

Navigating the data environment is a challenge in three ways:

1. Managing large amounts of data and using it for a competitive advantage is arduous. Data must be categorized, stored, moved and deconflicted. Data must have integrity, confidentiality, and accessibility. The vast amounts of data coming from sensors is overwhelming to the point that humans cannot process it fast enough to recognize patterns.
2. The Army鈥檚 data environment is constantly evolving, both from an overall strategy perspective and down to the unique requirements of each individual organization as to how to execute their data fabrics, data models, and unified data sets.
3. Choosing the right solution is daunting. Organizations struggle with the fundamental decision on whether to make vs. buy. There are hundreds of commercial off-the-shelf and government off-the-shelf tools available. There are several data 鈥淥EMs鈥� who build customized solutions or adapt/adopt 80 percent solutions.

Partnering with a firm who has developed proven approaches can help counter these data challenges.

暗网吃瓜鈥檚 Data Analytics Center of Excellence(CoE) is comprised of people, processes/platforms and tools that coalesce into expert advisory services that help operationalize data. Our Data Analytics CoE acts as a dual-purpose collaborative center for analytics-led transformation across 暗网吃瓜 (internal enterprise) and for our customers (external-facing). Our internal initiatives focus on our corporate processes and corporate data. Our external-facing offerings make use of customer-centered design approaches which include working closely with key customers and end-users to ensure alignment with program objectives and maximize value for end-users. Two ways we leverage predictive analysis includes the following:

1. Data Analysis for Workforce Management (internal enterprise)
2. Predictive Combat Power (external-facing)

Data Analysis for Workforce Management focuses on providing predictive staffing. One of the biggest challenges across the DoD is hiring the right talent. 暗网吃瓜 adopted a data analytics application that aggregates data from all our programs into a single Common Operating Picture that pulls from all of our Programs of Record, i.e., our hiring system, HR system, and requisition system. Our data analytics and visualization tool amalgamates the data inputs, producing simple-to-use data interpretation and visuals that our strategic recruiters and program managers use to conduct turnover forecasting and reduce the impact of programmatic vacancies. The output of the analytics informs sourcing priorities for recruiters to those positions that are at highest risk of a vacancy.

暗网吃瓜鈥檚 time to fill (average) for programs that have not yet moved to this model is 29 days. However, programs now using the combination of our Data Analytics for Workforce Management tool have achieved as low as 10 days, a nearly 65% reduction, both well below the industry average of 47 days.

Predictive Combat Power and Maintenance for battlefield equipment and supplies is incredibly important for forward operating bases (FOBs) or any forward deployed scenario. Our team implemented a Predictive/Prognostic Maintenance (PPMx) solution for an Army Aviation customer. We deployed an artificial intelligence and machine learning-based solution that uses historical data on asset classifications, repairs, vendors, and maintenance plans to predict current asset conditions and failure causes. Our customers will be utilizing this solution to better monitor asset health, optimize maintenance schedules and costs, and gain better insights and understanding of operational risk and planning. They can realize efficiencies by looking at RUL (Remaining Useful Life) charts of assets, minimize time to diagnose asset failures (when an incident does occur), and prevent future failures. Using our PPMx tool, our team was able to predict repair costs and predict future failures. Additionally, we have adapted and adopted this solution for several of our facility maintenance contracts in which we have realized more than $848,000 in cost savings for our customers.

In closing, service providers who have made investments in predictive analysis both internally and externally are well equipped to address complex data challenges for the Army and DoD. Our team has the expertise to cover the gamut of services on behalf of our customers to include assisting them in developing tools that enhance our services offerings.