Data that the company owns is one of the most valuable assets available. Specifically, these data and the ability to generate useful business information for decision making is, in the end, the true value.
The Big Data has shaken the panorama of the databases. Big data implies having to work with distributed databases and with this scenario, obtaining a complete and reliable copy of several petabytes of data does not seem like something simple.
Multidimensional databases are generally used to create OLAP applications. They are made up of several tables of facts and dimensions. In this way, each dimension table contains a simple primary key that in turn composes the primary key of the fact table.
Cubes, dimensions and hierarchies are the essence of OLAP’s multidimensional navigation. By describing and representing information in this way, users can intuitively navigate a complex set of data.
OLAP is the acronym of Online Analytical Processing. It is a solution used in the field of the so-called business intelligence whose objective is to speed up the consultation of large amounts of data. It uses multidimensional structures (or OLAP cubes) that contain summarized data from large databases or Transactional Systems (OLTP).
The processes’ improvement of protection of personal data has re-filled the pages of business forums, blogs and professional social networks after the approval of the Data Protection Law by the EU in 2016.
The financial sector has experienced in recent years important growth and integration processes, a continuous and accelerated incorporation of new technologies to the business, and the universal deployment of the concept of multichannel.
The characteristics of the retail sector make it an ideal terrain in which the use of Big Data combined with predictive analysis techniques make an excellent match for the business.
“Big Data is like teenage sex: everyone talks about it, nobody really knows how to do it, everyone thinks everyone else is doing it, so everyone claims they are doing it.”