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Data science vs. machine learning vs. AI: How they work together Posted on : Jan 08 - 2021

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

At its core, data science aims to extract useful insights from data given the specific requirements of business executives and other prospective users of those insights. What are customers interested in purchasing? How is the business doing with a particular product or in a geographic region? Is the COVID-19 pandemic straining or growing resources? These are questions that can be answered using the mathematics, statistics and data analytics that are part of the data science process.

Traditionally, organizations have depended on business intelligence systems to derive insights from their growing pools of data. However, BI systems depend partly on humans to spot trends in spreadsheets, dashboards, charts or graphs. They're also challenged by at least four of the Vs of big data: volume, velocity, variety and veracity. As organizations store data in increasing quantities and collect it at increasing speed from a wide variety of data sources, in different formats and with different data quality levels, the conventional data warehousing and business analytics approaches that BI is built on fall short.

By comparison, the experiences of leading-edge companies, such as Amazon, Google, Netflix and Spotify, show how applying the fundamental aspects of data science can help uncover deeper insights that provide significant competitive advantages over business rivals. They and other organizations -- banking and insurance companies, retailers, manufacturers and many more -- use data science to spot patterns in data sets, identify potentially anomalous transactions, uncover missed opportunities with customers and create predictive models of future behavior and events. View More