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Exposing Engineering Data As a Strategic Asset with the U.S. Air Force

The Air Force SEEK EAGLE Office (AFSEO) has developed a new data lake architecture and applications that combine historical data, machine learning, and modern big data technology to accelerate complicated engineering analyses. Before a new store (weapon, fuel tank, etc.) is mounted on an aircraft, AFSEO is responsible for setting safe limits for flight for that store and determining the unsafe ways in which different stores can interact. Doing so requires complicated engineering analyses and tests (flight, wind tunnel, etc.) led by Ph.D. level engineers.

During this session, Donna Cotton, CDO the U.S. Air Force SEEK EAGLE Office (AFSEO) discusses how the Air Force leveraged 50+ years of accumulated data in its data lake to synthesize previously approved analyses rather than building new analysis from scratch.

Watch now to learn how both efforts helped engineers certify new stores and capabilities with the agility required to support the modern-day US Air Force.

New case study: Air Force SEEK EAGLE Office and Tamr Expedite The Air Force’s Mission Capabilities 

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During this session, Donna Cotton, CDO the U.S. Air Force SEEK EAGLE Office (AFSEO) discusses how the Air Force leveraged 50+ years of accumulated data in its data lake to synthesize previously approved analyses rather than building new analysis from scratch.

First, a custom data catalog has been built that tags all files with relevant metadata and then makes them searchable and filterable through a web-based UI. This is enabled by a Dell EMC multiprotocol Isilon storage solution that simultaneously serves files to all operating systems based on users’ roles through SMB/NFS protocols and to the Cloudera based data lake as HDFS. The custom data catalog UI is hosted and served directly by Solr, offering a number of advantages.

Second, a custom application built on Tamr machine learning searches historical documents for relevant antecedents, and in some cases, can create a “by analogy” certification without human intervention.

Watch now to learn how both efforts helped engineers certify new stores and capabilities with the agility required to support the modern-day US Air Force.

***As seen at the 2020 MIT Chief Data Officer and Information Quality (CDOIQ) Symposium ***

New case study: Air Force SEEK EAGLE Office and Tamr Expedite The Air Force’s Mission Capabilities 

About the Speaker

Donna Cotton Chief Data Officer at the U-S- Air Force SEEK EAGLE Office (AFSEO)

Donna Cotton
Chief Data Officer at the U.S. Air Force SEEK EAGLE Office (AFSEO)

Donna Cotton is the Chief Data Officer for the Air Force SEEK EAGLE Office (AFSEO), providing leadership and oversight of architecture development, data management and cybersecurity for an enclave supporting over 300 users. She leads data transformation initiatives, specifically implementation of Advanced Analytics and Artificial Intelligence/Machine Learning capabilities to manage over 100,000,000 files of critical aircraft store compatibility data. Her leadership keeps AFSEO at the leading edge of information management practices across the DoD, with her expertise sought by numerous DoD Agencies and Industry Partners.

Ted Gudmundsen Public Sector Technical Lead at Tamr

Ted Gudmundsen
Public Sector Technical Lead at Tamr

Ted is the technical lead at Tamr Government Solutions, the federal arm of Tamr Inc. Ted was an early hire at Tamr, an MIT spin off that is commercializing research on the application of machine learning to data integration problems. He has designed and implemented data integration solutions for many government and commercial customers, including the Air Force, NavAir, CBP, GE, Carnival Cruise Lines, and Air Liquide. He has been involved in advanced data processing for over 10 years, previously as a researcher at MIT Lincoln Laboratory and with Tamr since 2015. Ted graduated with a M.S. in physics from Cornell University and a B.A. with high honors in physics from Princeton University.