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Seg24 Had Enough Hype? Some Straight Talk about AI, Machine Learning, and Big Data

  • December 2015
  • -
  • Frost & Sullivan
  • -
  • 9 pages


Table of Contents

By 2025, the combined value of IoT technologies could reach more than $6 trillion. So, it would seem imperative that the market understand what Big Data, AI, and machine learning are, and how they can work together. This Stratecast report briefly discusses exactly that, and presents results that a growing number of organizations are achieving, and are gearing up to achieve, by harnessing their combined power.

“What does Big Data really mean? We hate that term and refuse to use it.” The author of this report
heard that directly from a company just the other day, and it is a familiar refrain in many such
conversations. It is true that the market for Big Data and analytics (BDA) solutions has seen more
than its share of hyperbole. For example, a burning question in the industry seems to be: How many
V’s does it take to define a technology? In the BDA market, many believe it takes three:
• Volume – the vast amount of data being generated today.
• Velocity - the speed at which new data is generated and distributed.
• Variety – the different types of data that now exist: structured data that fits neatly into tables
and relational databases (RDBs), such as customer records; unstructured data, which
emanates from online and mobile sources, and cannot be managed by an RDB; and semistructured,
which includes things such as email, corporate documents, and XML files.
Others see a fourth “V”: Veracity, which is about the accuracy of the data, and how confident users
are that they can trust and base decisions on it. Still others see a fifth V: leveraging data to drive
It is hoped that those who loathe the relentless pace of technology industry hype figuratively
buckled up their intellectual seatbelts over the past months, because the emergence of two
other technologies has only served to add to and accelerate the hype around Big Data. These two
accelerators of both technology and hyperbole are artificial intelligence (AI) and machine
learning. To those seeking kernels of truth that define Big Data, the picture is only clouded further
by assertions such as these:
1. “Big Data is the new AI.” The notion is that the idea behind AI was to figure out how
people do what they do, program computers to do it, and thereby replace humans with
computers. The notion says: the movement failed; and Big Data will now succeed where AI
failed by (at long last) enabling computers to do what humans cannot.
2. “Machine learning is the next generation of Big Data.” The idea here is that Big Data
solutions analyze past experiences to deal with unfamiliar situations and predict future
events—but that machine learning will duplicate this on a massive scale, superseding the
impact of Big Data; and that machine learning will become core to every application.

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