Big Data analytics partitions a large data set into an ensemble of smaller work parcels, with each individual parcel paired to an individual computer, within a network of computers. The parcels are then all processed at the same time, with all computers running simultaneously, using an efficient parallel processing strategy that offers several advantages:
- When work parcels are distributed across multiple computers, it is very likely that all relevant data can be loaded directly into computer memory, which facilitates greater processing efficiency. This leads to significantly shorter training intervals, and thus shorter wait times before algorithms are deployed operationally.
- The system can easily scale upwards in order to further reduce training times, by simply adding more computers to the network, and therefore increasing overall memory capacity.
- Distributed file systems and distributed databases for big data are also designed to reside on multiple computers in a parallel arrangement. These are designed to integrate individual pools of data that are disparate in terms of format, and storage location, and which are too large for accommodation by a single computer.
- The parallel arrangement also ensures just‐in‐time data discovery in very large and continually refreshed streaming data sets, thereby allowing the analytic model to quickly learn, adapt, and maintain predictive accuracy as new data is added.
In business, relevance and agility to adapt are key success drivers. Parallel computing and on-demand processing power enable an organization to easily derive data-driven insights, which can then be integrated into the business workflow to build a successful competitive strategy.