Combining big data and machine learning technologies
Valérie Bécaert Valérie Bécaert
October 11 4 min

Combining big data and machine learning technologies

Big data and machine learning are two technological developments that are changing our lives and transforming businesses around the world. If you want to harness them, you first need to know what they represent individually, in order to understand what they can achieve together.

Bigger data than ever before

More data is being created now than at any point in history. It seems like every part of our increasingly connected world is undergoing a digital transformation. We’re creating new words beyond megabytes and gigabytes to describe all this data: Facebook users create 4 petabytes of data per day by one measure, view or uploading 350 million photos and 100 million hours of video. There are an estimated 4.3 billion internet users worldwide, each of those browsing, shopping, swiping, chatting, and posting.

It’s not only about our activity online. Connected devices account for an ever-growing share of digital traffic, especially smart devices in the Internet of Things. And every projection has data creation growing exponentially in the coming years, as it has over the past decade. By one measure, more than 90%of the world’s existing data has been created in the last 2 years, and this has been true for the last 30 years.

The huge datasets being created today offer immense opportunities for insights and new business models, but they also pose challenges. How can a business handle such unprecedented volumes of complex data effectively? That’s the question that animated early and ongoing discussions of big data, harnessing these large datasets for business use.

Big data was first coined as a term in the 1990s, but it wasn’t until the 2000s that technology caught up with the ideas. The advent of superfast networking, cloud-based storage and processing, and digital services all combined to make big data a reality — and give people the opportunity to do something with it.

The smartest machines yet

In the early years of big data, applications taking advantage of it were known as data mining. Now, data mining has been superseded by machine learning and AI.

Machine learning is ideal for handling big data. It’s a fast, precise, and sophisticated data analyst/processor that can work with massive volumes of complex information, sorting data accurately, spotting interesting patterns, and even making predictions of the future.

Machine learning is particularly skilled at categorizing data and performing analytics to find patterns. It can process vast amounts of data, quickly and accurately, using its ever-accumulating knowledge of the subject matter to make judgements that continually improve. And as machine learning becomes more advanced, it can handle more and more complex data, with greater and greater sophistication.

In fact, big data is integral to the advancement of machine learning. The more data you provide it to learn from, the smarter it gets. As machine learning’s knowledge increases, so does its ability to make informed analyses and judgements. You could even look at data as fuel for AI — the more a model is fed, the better it progresses.

Big data and machine learning have a perfectly symbiotic relationship. Big data’s usefulness depends on machine learning’s ability to interpret it, and machine learning’s increasing analytical skill depends on the existence of big data. It’s a relationship that continues to create ground-breaking results.

Making real change

Examples of big data in use are everywhere. In the online retail sector, powerhouse Amazon is leveraging a truly gigantic data store to train its world-class recommendation algorithm, so that it can provide nuanced and appropriate product recommendations to its estimated 103 million Amazon Prime users and other customers.

The treasure trove of purchase records, browsing information and customer details and other data that Amazon collects every millisecond of every day provides an incredibly deep level of customer insight. This could never have been imagined in the era before big data — and accessing it would not be possible without the power of machine learning.

Bigger data, smarter machines

IDG’s Data Age 2025 report forecasts that global data will grow from the current 33 zettabytes to 175 zettabytes (175 trillion gigabytes) by 2025. When we’re talking in terms of figures like that, big data seems like quite an understatement.

Imagine the insights an AI system can learn from such quantities of high quality data, and the levels of sophistication and precision it can reach from such an abundance of training material. For all that’s been accomplished so far, we have only seen the tip of the iceberg for big data and machine learning.