The amount of online data has become so massive that it’s no longer data anymore. Rather it has been given a new name: Big Data. The ‘big’ in big data stands for its sheer volume and complexity.

“If you want an idea of the amount of data that is being generated in just a single day, then here’s a startling statistic: Every day, we produce about 2.5 quintillion bytes of data. That’s 18 zeros after 2.5!”

Although analysing big data alone can be valuable, integrating low-density big data with structured data can provide even greater business insights. By capturing various forms of big data, such as customer, product, equipment, or environmental data, and by adding relevant data points to your core master and analytical summaries, one can draw better conclusions.

For instance, analysing customer sentiment, as opposed to just top customers, can lead to different conclusions. Therefore, many view big data as an essential expansion of their existing business intelligence capabilities, data warehousing platform, and information architecture.

It’s important to note that both human- and machine-based big data analytical processes and models exist. Big data analytical capabilities encompass a range of techniques such as statistics, spatial analysis, semantics, interactive discovery, and visualisation. By utilising analytical models, you can identify correlations among different data types and sources, leading to significant findings.

For organisations across various sectors to get the most out of their data, they need to leverage advanced analytics techniques. This is where big data analytics comes in. Analytics, as the term suggests, is the process of analysing data to gain insights and intelligence.