Existing data analytics systems (e.g. dashboard and reporting systems) typically require input data to be prepared before it can be visualized or manipulated. For example, data is first read from input sources and then the read data is cleansed, joined, or otherwise transformed before it can be manipulated and visualized. The tools used for preparation of data are often termed extract-transform-load (ETL) tools. These typically involve significant manual operation and technical expertise to set up and configure properly. Once the data has been prepared using ETL tools it is then stored in a data warehouse for use by visualization systems.
Existing analytic applications and systems allow users to select measures and dimensions of interest and perform analytical operations such as consolidation (roll-up), drill-down, and complex filtering across an arbitrary number of hierarchies or dimensions (i.e. “slicing-and-dicing”). Such systems not only require input data to be prepared but also loaded into a multidimensional dataset or OLAP cube before any data analysis can occur.
Similar reference numerals may have been used in different figures to denote similar components.