Big Data is a set of technologies that allow you to overcome the challenges of storage and the difficulty of dealing with heterogeneous data. The three important parameters are scope, variety and speed of data analysis. Database management systems are no longer based on relational database architectures, but on NoSQL databases (not just SQL) originally developed by Internet players. In marketing, megadata focuses on predicting when trends will occur and analyzing consumer behavior. Thus, data collection helps improve the relevance of marketing offers while monitoring reception through real-time social network analysis. By mining the data, you can improve the segmentation of your consumer profile. Data that is classified and analyzed in this way is called intelligent data.
Machine learning is a scientific discipline, more precisely a sub-category of artificial intelligence. It consists in allowing the algorithm to detect “patterns”, or repetitive patterns, in a set of data. This data includes numbers, words, images, statistics, etc. Anything that can be stored digitally can be used as machine learning data. By recognizing patterns in this data, the algorithm learns and improves performance when performing certain tasks. In short, machine learning algorithms autonomously learn task executions and predictions from the data, improving performance over time. With this learning, the algorithm will be able to find patterns in a selection of new data.