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.
Introduction “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 Value. 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.
Table Of Contents
Seg24 Had Enough Hype? Some Straight Talk about AI, Machine Learning, and Big Data Big Data: What it Is and What it Does How AI and Machine Learning Work with Big Data to Deliver Value Big Data, AI, and Machine Learning Combine to Deliver Business Results Big Data-driven AI and Machine Learning: Huge Potential - and Downsides Stratecast - The Last Word About Stratecast