Big data can be explained by understanding the following key aspects:
- Volume. There is no specific quantification that says volume above these many terabytes will be called Big data. What volume one considers as threshold depends on the perspective and the year we are in (big data is a moving target). However, this large volume of data is mostly the cost-free byproduct of digital interaction such as consumers buying stuff off online shops.
- Velocity. The speed at which data is generated and processed. Big data is often available in real time.
- Variety. The type and nature of data. Big data draws from text, images, audio, video; plus it completes missing pieces through data fusion.
- Data must be processed with advanced tools (analytics and algorithms) to reveal meaningful information. Big data is not about asking why, but about detecting patterns. Information generation algorithms must detect and address visible and invisible issues and factors.
- Parallel computing tools are needed to handle data.
- Often “inductive statistics” are used to infer laws (relationships, causal effects) from large sets of data to reveal relationships or dependencies or to perform predictions of outcomes and behaviours.