The data set needs to be gathered from the “producers”, such as the Internet – click-stream data, social media activity reports, mobile-phone call detail records, and information captured by sensors. The data need to be classified according to their sources, images, videos, and texts, and then stored using technologies supporting data storage. The next process (data integration) involves combining data residing in different sources, and providing users with a unified view of these data. The analytics supports the mining of the data, the determination of what is relevant, and the discovery of patterns and relationships to help decision making. More importantly, data analytics are predictive analytics. They give the probability of different outcomes, and are future-oriented. Big Data analytics can be done with the software tools commonly used as part of advanced analytics disciplines, such as predictive analytics and data mining. However, the unstructured data sources used for Big Data analytics may not fit in traditional data warehouses. Furthermore, traditional data warehouses may not be able to handle the processing demands posed by Big Data. The technologies associated with Big Data analytics include among others NoSQL, Hadoop, and MapReduce. These technologies form the core of an open source software framework that supports the processing of large data sets across clustered systems. Consumers and citizens often through applications are the ultimate beneficiaries of Big Data, but businesses’ processes could also be the beneficiaries. ‎[33]