Big data refers to extremely large sets of structured and unstructured data that cannot be handled with traditional methods. Big data analytics can make sense of the data by uncovering trends and patterns. Machine-learning capabilities programmed into analytic software can accelerate the process of making sense of the data, with the help of decision-making algorithms. It can categorise the incoming data, recognise patterns and translate the data into insights helpful for business operations.

The current usage of the term big data tends to refer to the use of predictive analytics, user behaviour analytics or certain other data analytics methods that extract value from big data. Analysis of data sets can find new correlations to spot business trends, prevent problems and identify market opportunities.

Scientists, business executives, medical practitioners, advertising and governments are increasingly met with difficulties dealing with large datasets in areas including fintech, internet searches, urban informatics and so on. The sheer size and number of available data sets have grown rapidly as data collected by everyday devices in the marketplace such as mobile devices, and other information-sending internet-of-sensing devices, cameras, microphones and wireless sensor networks have become ubiquitous. Machine learning and a robust database management system are now critical in analysing big data; big data relies on the capabilities of those analysing and their tools.

In 2021, the world’s most valuable resource is no longer oil, but data. Data is a key economic commodity that can make existing businesses more efficient and is driving new business models and industries. The spread of the Covid-19 global pandemic has generated an exponentially mounting and extraordinary volume of data that can be harnessed to improve our understanding of big data management.