Modern Digital Enablement Checklist: How to Close the “Last Mile” Data Gap with a Cloud-Based Approach

Are you prepared for digital transformation? Learn how using cloud software along with data analytics and machine learning technologies can strengthen any organization.

Table of Contents

  1. Summary
  2. Innovative Market Disruption
  3. What Can Traditional Organizations Do to Keep Up With, or Excel, in Their New Digital-Powered Markets?
  4. How Should Organizations Assess Their Current State?
  5. How Can Organizations Arm Themselves For Digital Transformation?
  6. How Can Organizations Gain a True Advantage by Leveraging Technology-as-a-Service?
  7. How Should You Get Started?
  8. The Art of Digital Adoption
  9. Sponsor’s Perspective, “It’s Never Too Late to Join the Digital Revolution”

1. Summary

Organizations choosing not to play in digital modernization are missing out on major force multipliers that allow businesses to protect their existing markets, expand into new market areas, and be more socially and environmentally aware. Indeed, this is about leveraging technology for good, as well as embracing it to drive business value.

This report is designed for those looking to enable their organizations digitally, but who do not know where to start. We will cover what is important around digital adaptation, including what changes need to occur inside business processes, the technology stack, and (most importantly) individuals’ hearts and minds. We will also review what it takes to accelerate your digital posture, including how you can think about approaching new tools and technology that are now on-demand or can be accessed remotely at any time for any reason and at a much lower price than physical assets.

So, what happened to create this opportunity to evolve your business from average to market-leading? To put it simply, innovation and the rise of technology. Let us consider a health care provider embracing the public cloud for data analytics workloads, and powering these analytics with machine learning to determine if symptoms are leading to diagnosis, and then using the proper diagnosis to determine the best treatments. Instead of the symptoms and treatments being monitored by a single doctor, they are monitored by 10,000 virtual doctors, who trained an AI system, with years of treatment and outcome data being considered. To that end, the key benefits of machine learning in this context could be 1) accurate data-driven patient treatment and, 2), accurate diagnostics, 3) predicting and preventing future health issues.

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