On Demand Webinar

Changing the Game With Predictive Maintenance

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Event Description

This webinar will focus on deploying predictive maintenance applications for government agencies and commercial organizations.

Hear how federal agencies, state and local governments, and transportation and logistics organizations can create performance analytics to save money on fleet, asset, or system maintenance by proactively identifying and managing asset reliability risks that could adversely affect plant or business operations.

We're excited to show a demo of Predictive Maintenance in action (inspired by a mission-critical military scenario) designed to:

  • Reduce downtime, gain huge cost savings and efficiencies, and keep fleets and operations running smoothly
  • Apply predictive models on top of a wide array of integrated data including historical logs, IoT sensors, NOAA weather, and more
  • Predict before something breaks so you can replace a part and avoid unnecessary downtime or missed missions

All registrants will receive a collection of resources and case studies on Predictive Maintenance.

Don't miss it! If you can't make it, register to receive the recording.

Deep Uppal, Vice President of Public Sector Technology Innovation, Information Builders
Deep Uppal
Vice President of Public Sector Technology Innovation,
Information Builders

Deep Uppal is an innovator, technical problem solver, and change agent with a proven track record of defining the technical vision; communicating complex processes; and successfully creating, integrating, and deploying next-generation enhanced analytics, network architecture, and all associated facets of technology related to state and federal government.

Jeff Dodson
Jeff Dodson
Pre-Sales Sr. Systems Engineer,
Information Builders

Jeff Dodson is a pre-sales senior systems engineer at Information Builders. His main role involves demoing our product capabilities and developing proof-of-concepts using our technology solutions. He specializes in Predictive Analytics, and he has previously published research in the cyber security realm about using a mathematical approach for Steganography detection.

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