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How to accelerate data platform integration using IoT and artificial intelligence

Enterprises are moving to the cloud to increase business and IT agility. This helps them to work more effectively and substitute out-of-date data centers and applications. Artificial intelligence, analytics and the Internet of Things (IoT) are some of the major technologies that are driving this development. But this also includes infrastructure migration to the cloud.

It is advisable and fruitful to migrate analytics workloads because the cloud provides scalability, agility, time to market and reduced costs for companies to work more effectively.

“With a five-year compound annual growth rate (CAGR) of 22.3%, public cloud spending will grow from $229 billion in 2019 to nearly $500 billion in 2023.” – -International Data Corporation (IDC) Worldwide Semiannual Public Cloud Services Spending Guide

Azure data platform enables organizations to innovate faster when they migrate data and analytics workloads to Azure. Companies have different needs, but they face similar challenges when they plan and execute data analytics migration to the cloud. It is important to understand the requirements, patterns of migration, decision criteria, best practices and tools that help in its successful deployment.

Document highlights:

  • Readiness for IoT using Azure data
  • Accelerated Data Platform in 30 Days (DPi30) for IoT
    • Identify services and processes
    • Model and define processes
    • Create and enable automation
  • Timeline to Market
    • Envisioning workshop
    • Data platform pilot
    • Transition to automated services

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