The Elastic Cloud
The increased number of data sources and high data velocity resulting from the sensoring of business value chains generate unprecedented data volumes, resulting in data sets that were previously unmanageably large and effectively un-processable due to the cost of data storage and the limitations of computer processing power. With the advent of elastic cloud computing from Amazon Web Services (AWS) and others, offering virtually unlimited data storage and processing capacity at increasingly low cost, processing these data becomes both instantaneous and cost effective.
Moore's Law: The Cost of Computing and Storage Continues to Plummet
Combining IoT, the Cloud, and AI offers a new computing paradigm that can address challenges that were heretofore unsolvable.
The rate of IoT proliferation across value chains is staggering. In the past 20 years, nearly 20 billion sensors have been installed,1 and in the next 10 years more than 50 billion additional sensors will be installed.2 McKinsey estimated that by 2026, over one trillion objects will be connected to the Internet.3 All value chains will be disrupted: oil & gas, manufacturing, financial services, healthcare, government services, defense, telecommunications, retail, travel and transportation, aerospace, education, scientific research, consumer products, etc.4
IoT Device Proliferation Across Business Value Chains
The growth rates of IoT,5 the Cloud,6 Big Data, and AI are unprecedented in the history of information technology. Within the next five years virtually all value chains will be fully sensored; more than 90% of corporate data will be processed in the cloud;7 accumulated data will increase from 2017 by a factor of 10 to more than 163 zettabytes (or 163 trillion gigabytes) by 2025;8 and AI will grow exponentially.9
IoT software alone is expected to become a $250 billion industry by 2020;10 unprecedented growth in the history of computer software.
IoT: $250 Billion Software Market by 2020
Big Data and the AI Renaissance
The concept of “Big Data” is a commonly misunderstood consequence of these technology trends. Yes, these data sets become staggeringly large, but that is not what big data is all about. Historically – again due to the costs of storage and limitations of computational power – computation was performed on data samples and statistical methods were employed to draw inferences from those samples that were in turn used to inform business decisions. With big data, however, we perform calculations on all the data; there is no sampling error. This enables AI, a class of computations previously unattainable, using machine learning and deep learning to develop self-learning algorithms to perform precise predictive analytics.11 The combination of Big Data and AI replaces traditional hard-coded, rule-based algorithms with continuously self-learning machine learning algorithms. That is the promise of Big Data.
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1“The Zettabyte Era: Trends and Analysis,” Cisco Visual Networking Index, Cisco, June 2017.
2“Tech Companies Creating Strategic Platforms to Support the Internet of Things, IHS Says,” IHS Technology, April 2016.
3“The Four Global Forces Breaking All the Trends,” McKinsey Global Institute book excerpt and slideshow, April 2015.
4“The Internet of Things: Mapping the Value Beyond the Hype,” McKinsey Global Institute, June 2015.
5“Winning in IoT: It’s All About the Business Processes,” BCG, January 2017.
6“Gartner Says Worldwide Public Cloud Services Market to Grow 18 Percent in 2017,” Gartner press release, February 2017.
7“Cisco Global Cloud Index: Forecast and Methodology, 2015-2020,” Cisco, 2016.
8“Data Age 2025: The Evolution of Data to Life-Critical,” an IDC White Paper, April 2017.
9“Why Artificial Intelligence is the Future of Growth,” Accenture, September 2016.
10“Winning in IoT: It’s All About the Business Processes,” BCG Perspectives, The Boston Consulting Group, January 2017.
11The Master Algorithm: How the Quest for the Ultimate Learning Machine Will Remake Our World, by Pedro Domingos, Basic Books, 2015.