Information Technology

What is Big Data? A super simple explanation for everyone

The term “Big Data” may have been around for some time now, but there is still quite a lot of confusion about what it actually means. In truth, the concept is continually evolving and being reconsidered, as it remains the driving force behind many ongoing waves of digital transformation, including artificial intelligence, data science and the Internet of Things. But what exactly is Big Data and how is it changing our world?

How does Big Data work?

Big Data works on the principle that the more you know about anything or any situation, the more reliably you can gain new insights and make predictions about what will happen in the future. By comparing more data points, relationships begin to emerge that were previously hidden, and these relationships enable us to learn and make smarter decisions. Most commonly, this is done through a process that involves building models, based on the data we can collect, and then running simulations, tweaking the value of data points each time and monitoring how it impacts our results. This process is automated – today’s advanced analytics technology will run millions of these simulations, tweaking all the possible variables until it finds a pattern – or an insight – that helps solve the problem it is working on.

Until relatively recently, data was limited to spreadsheets or databases – and it was all very ordered and neat. Anything that wasn’t easily organised into rows and columns was simply too difficult to work with and was ignored. Now though, advances in storage and analytics mean that we can capture, store and work with many, many different types of data. As a result, “data” can now mean anything from databases to photos, videos, sound recordings, written text and sensor data.

To make sense of all of this messy data, Big Data projects often use cutting-edge analytics involving artificial intelligence and machine learning. By teaching computers to identify what this data represents– through image recognition or natural language processing, for example – they can learn to spot patterns much more quickly and reliably than humans.

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