ABSTRACT
In large enterprises, it is typical to have hundreds of relational databases of different types and multiple data warehouses. Enterprises must be able to analyze together the data from databases, data warehouses, application servers, machine sensors, social media, and so on. Consequently, data needs be organized into repositories that can store data of all kinds, of different types, and from different sources in data reneries and data lakes. This data can be correlated using more data points for increased business value. By processing data from different sources into a single source, organizations can do a lot more descriptive and predictive analytics. Enterprises are not only wanting to predict with high degrees of accuracy but also to reduce the risk in the predictions. Organizing data pooled into a single data source or just a few data sources allows a richer set of questions to be asked of the data. Being able to add additional correlation sources increases the condence and reduces the risk of the results of the questions.
