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Available On-Demand - This webinar has been recorded and is now available for download.

Data Architecture for IoT Communications and Analytics

TDWI Speaker: David Loshin, President of Knowledge Integrity

Date: Wednesday, June 6, 2018

Time: 9:00 a.m. PT, 12:00 p.m. ET

Webinar Abstract

The Internet of Things (IoT) is an architectural paradigm combining an exploding number of different types of connected sensors and devices continuously generating and broadcasting data. The data can be processed to create integrated analytics models that can enhance and optimize new business initiatives.

Yet IoT networks are constantly morphing, with numerous types of devices producing massive numbers of real-time data streams employing a multitude of communications protocols in the absence of well-defined standards. This introduces some difficult challenges in data capture, ingestion, and filtering as well as the analysis processes used to generate those models that will create key insights and actionable intelligence.

A technical architecture for a successful IoT implementation must enable a connected data platform for capturing, ingesting, and analyzing real-time streaming data as well as for leveraging historical insights and building machine learning models from data at rest. This means analyzing the data in real time, creating analytical models, and pushing those models back to the components in the hierarchy of the IoT network.

This webinar explores some fundamental aspects of IoT data architecture that will continuously adapt to the dynamic nature of massive numbers of connected sensors and other end-point devices. We will examine IoT communication, data streaming, ingestion and analysis, and deployment of developed analytical models for automated and predictive decision making.

Attendees will learn about:

  • Understanding the dynamic nature of IoT data flows
  • The complexity of ingesting and analyzing IoT data
  • Aspects of intelligent stream processing
  • The value of a metadata and services catalog
  • Adaptive analytical modeling
  • Pushing analytics to the edge
  • Continuous business process monitoring

David Loshin

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