Big Data definition
We’ve all heard the term ‘big data’ being thrown around by tech-savvy colleagues, friends, and family, but do you really understand what it is? Here, we provide you with a working definition and take a look at why the concept is important.
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What is big data?
There are numerous definitions of ‘big data’, and the term is always adapting to new technological realities. As the digital landscape is constantly evolving, so too is our understanding of key concepts within modern computing. This means that ‘big data’ is a nebulous idea and can be difficult to pin down. To provide a full understanding of all that big data encompasses, we thought it a good idea to provide two definitions. The first comes from Gartner.
“Big data is data that contains greater variety, arriving in increasing volumes and with ever-higher velocity.” In this definition, big data is any complex data that is in larger quantities, by modern processors. This is often known as the ‘three Vs’ – volume, velocity, and variety.
In other words, big data is data that is processed in high volumes. It is typically unstructured data, with an unknown value. Due to the vast quantities being processed, big data also requires it to be received and acted on in a relatively short period. Finally, big data also encompasses a wide range of data types, including unstructured and semi-structured kinds of data, such as text, audio, and video.
Alternatively, big data can also be defined as those data sets that are of a size that is beyond the capacity of standard software to collect, process, and manage. In this sense, big data is any data that requires special tools to realise its value.
Big data is the new oil
In recent years, experts have also tried to add two more ‘Vs’ to the definition of big data. These are value and veracity. Value is used to represent the fact that all data has an inherent value – even if it’s not immediately apparent how to realise that value. This is one of the most important concepts in modern economics and digital technology development. As businesses and other organisations have concluded that big data, once manageable, is precious, it has been commodified to such an extent that the largest companies in the world are no longer oil producers but those businesses who manage the vast reams of data generated by human action and behaviour. In other words, big data is the new oil.
While big data was defined initially primarily in terms of volume and velocity, recent years have witnessed an attempt to temper the excesses of these two qualities by introducing veracity as a defining quality. Veracity relates to the “biases, noise and abnormality” in data. Essentially, data sets need to be kept clear of these biases and abnormalities if they’re to be useful. This means that volume and velocity are no longer enough on their own; data sets also need to be ‘clean.’ Quantity and speed of processing are no longer all that matter – now we need quality, too.
When did big data start?
Though big data has only recently entered the collective vocabulary, the concept has been around since the 60s and the birth of modern computing. In these early days, the significant developments were the creation of data centres and relational databases, which facilitated the collection of vast quantities of data for the first time. Over the next few decades, the importance of big data steadily grew, and greater resources improved the tools and techniques at the disposal of data analysts.
However, it wasn’t until the widespread adoption of social networks that the general public woke up to the existence and value of the data they were generating. Open-source processing tools, such as Hadoop, also had a significant impact on the sector and lowered the cost of data analysis, ensuring more businesses could leverage the data they harvested for commercial gain. Today, it’s not just humans generating data, but all of their appliances, too. The growth of the Internet of Things (IoT) means that an enormous number of items now generate valuable data relating to our everyday habits and behaviours.
Big data technologies
The tools used to collect, process, analyse, store, and transmit big data are known as ‘big data technologies.’ They come in many different types, and the industry is continually developing new tools, too. Some of the most important technologies currently in development (or only recently being deployed) include predictive analytics software, improved search and extraction tools, and data visualisation software. However, there are also substantial improvements in the hardware required to manage big data. For instance, in-memory data fabric technology is improving the speed with which information from various data sources.
Big data analytics
Being able to collect and store vast amounts of data quickly and efficiently is essential, but it’s also vital for businesses to be able to act on that data if it’s to have any value. To act on it, we need to understand what it means and what it’s telling us. Big data analytics is the discipline through which we interpret what the data is saying.
In other words, it encompasses the processes by which organisations attempt to identify patterns and correlations in a data set. These often offer greater insight into how customers behave and what they want.
Why big data is important for businesses
Big data is thought of as necessary to businesses for at least four reasons. They are:
- Cost reduction – The ability to process large amounts of data makes it easier to identify inefficiencies in your working processes. Greater efficiency reduces costs to a business.
- Informed decision-making – The more evidence you have to back a particular course of action, the more likely it is to have the desired results. Big data allows you to make informed decisions.
- Understand customers – Data generated by customers will enable you to understand their behaviour better and to tailor your services to meet their needs and desires.
- Develop products and services – Big data can help determine what direction your new products and services take by providing you with more in-depth financial and market research.