Today’s organizations are awash in data. Just a decade ago, a gigabyte of data still seemed like a large quantity.

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SwissCognitive, AI, Artificial Intelligence, Bots, CDO, CIO, CI, Cognitive Computing, Deep Learning, IoT, Machine Learning, NLP, Robot, Virtual reality, learningNowadays, however, some large organizations are managing upward of a zettabyte. To get a sense of how much data that is, if your typical laptop or desktop computer has a 1 TB hard drive inside it, a zettabyte is equal to one billion of those hard drives.

How can organizations even hope to get any business value from so much data? They need to be able to analyze it and identify needles of valuable knowledge in an almost infinite haystack. That’s where the combination of data science, machine learning and AI has become remarkably useful — but you don’t need anywhere near a zettabyte of data for those three things to be relevant.

Once relegated to esoteric corners of academia and research or the wonky side of IT and data management, they’ve collectively emerged as crucial technology topics for organizations of all types and sizes in various industries. However, there’s often still confusion about data science vs. machine learning vs. AI and what each involves. Understanding the nature and purpose of these transformative concepts will point the way toward how to best apply them to meet pressing business needs.

Let’s look at each one, plus the differences between them and how they can be used together. Data science

While data has been central to computing since its inception, a separate field dealing specifically with data analytics didn’t emerge until many decades later. Rather than the technical aspects of data management, data science focuses on statistical approaches, scientific methods and advanced analytics techniques that treat data as a discrete resource, regardless of how it’s stored or manipulated. […]

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