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Research Synopsis

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Research Questions

  1. What is a simple and effective way to overcome the Army's challenge of having siloed and proprietary databases?
  2. How can the Army provide its data scientists access to common analytical tools?

The U.S. Army faces two analytical and management challenges because its data are locked away in siloed and proprietary databases and it lacks access to modern, commonplace analytical tools. To solve these two problems, the authors developed a case study with Army Contracting Command (ACC) to determine if there is a simple and effective way to overcome these challenges and found an effective, efficient, and quick path forward.

The authors conducted a proof of concept for data sharing and analytics with ACC, which has high volume and value of annually awarded contracts. They migrated large contracting data sets from ACC, built a robust querying and analytics platform for exploring that data, piloted a method for accessing heretofore inaccessible unstructured text data from contracts, and conducted a pilot machine-learning analysis highlighting how a cloud-based contract analysis system for ACC could lead to cost savings.

The team found that the Army can achieve immediate cost savings and efficiencies through advanced data analytics and the use of currently available commercial off-the-shelf technology. The Army should immediately conduct multiple similar proofs of concept that take siloed and inaccessible data to the cloud to be analyzed using modern analytical tools to validate the methodology from this report across multiple commands.

Key Findings

  • The U.S. Army can achieve immediate cost savings and efficiencies through advanced data analytics and the use of currently available commercial off-the-shelf technology.
  • The Army does not need to wait for a complete system to reap efficiencies and cost savings. The Army can build off the proof of concept developed for this study.
  • The Army can leverage commercial cloud infrastructure and software to immediately begin robust data sharing, querying, and analytics.
  • Going to the cloud would provide infrastructure efficiently without large initial capital expenditures. Maintenance, upgrades, and hardware availability would be baked in.
  • As a matter of policy, ACC data scientists lack access to common data-science tools and lack permissions for remote access to computing infrastructure that allows for robust data-processing pipelines and analytic interfaces.

Recommendations

  • The Army should immediately conduct multiple similar proofs of concept that take siloed and inaccessible data to the cloud to be analyzed. This analysis would be conducted using modern analytical tools to validate the methodology from this report across multiple commands.
  • The Army should develop a policy on the use of open-source analytical products and create cloud-storage requirements to ensure that multiple ongoing data efforts are interoperable.
  • The Army should set a goal, perhaps not more than one year out, to have access to a scalable analytics environment for all of its key operational and business data.

Research conducted by

This research was sponsored by U.S. Army Materiel Command and conducted by Forces and Logistics Program within the RAND Arroyo Center.

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