6 Reasons Why the DoD Needs Babel Street Analytics

What type of data and analytics technologies does your organization need if you want to meet the data strategy goals expressed by the Department of Defense?

To meet these goals, you’ll need advanced data and analytics technologies that can effectively process and analyze large volumes of data, including unstructured and semi-structured data across multiple languages. These technologies should also provide multilingual data triage and enrichment capabilities, enable data governance at a granular level, and support object-based production, enabling organizations to view data holistically and share it among authorized users while limiting access to sensitive information.

In addition, these technologies should employ natural language processing, machine learning, and statistical modeling techniques to extract valuable information and actionable data from the data sets. They should also be easily deployable across a broad range of enterprise data systems, with a modular architecture that allows for containerized delivery and scalability.

Finally, these technologies should empower analysts to improve insights by customizing machine learning models and incorporating model annotation, training, customization, and adaptation into their workflows, often bypassing the need for software engineers' involvement in these tasks. Overall, the technologies should be robust, reliable, and secure, with the ability to meet the stringent data security and privacy requirements of the Department of Defense.

Babel Street Analytics (formerly Rosette) aligns with the DoD needs, as follows:

1. Data intelligence technology that can be easily deployed across a broad range of enterprise data systems. As a vendor-agnostic API, Babel Street Analytics employs a modular architecture ready for containerized delivery via Docker and other containerization platforms.
2. Multilingual data triage and enrichment capabilities that can be applied to data as it is being created and collected in the Joint Worldwide Intelligence Communication System. Babel Street Analytics embeds multilingual natural language processing capabilities into the ETL pipeline. This helps intelligence professionals more efficiently and effectively search data.
3. A holistic, rather than siloed, view of data. Babel Street Analytics aids in the move to object-based production Object-based production reaches through data silos to provide organizations with a holistic view of data. This data can be viewed by authorized users across the enterprise.
4. The ability to limit data access to authorized users and enable sharing among the largest possible pool of authorized users. By statelessly extracting knowledge as structured metadata from unstructured input, Babel Street Analytics enables role-based access to key elements of information.
5. The means to improve intelligence sharing with its Five Eye intelligence alliance partners. Rather than classifying information document by document, Babel Street Analytics divides documents into their constituent data elements, enabling governance at a granular level. (This process is sometimes called the “atomization” of data.) This process can assist in Five Eye sharing.
6. Ways to empower analysts to improve insight by annotating and customizing machine learning models. Babel Street tools enable analysts to incorporate model annotation, training, customization, and adaptation into their workflows — often bypassing the need for software engineers’ involvement in these tasks.

The Department of Defense has clearly expressed its data strategy goals. The Babel Street data and analytics platform can enable the DoD to achieve these objectives.

For more information, download our ebook: Improving DoD Data Strategy: 6 Ways Rosette by Babel Street Can Help.