While the
OLAP cube can display quantitative information at the intersection of a row and a column, the ability to further filter data makes it multi-dimensional.
The following step is construction of
OLAP cubes with proper dimensions and measures.
Thus, predictable column does not necessarily have to be a "module", as shown in the results, but some other dimension of the
OLAP cube. With the application of DMX queries for the predictable column, the probability of appearance of a selected dimension is obtained, which provides the possibility of adapting the structure of electronic courses.
The
OLAP cube allows the managers of an organization to visualize a series of reports having a dynamic and multidimensional structure.
OLAP cube slicing & dicing interactive reports are an integral part of the training.
The curriculum covers all the new features such as Management Studio, Business Intelligence Development Studio,
OLAP cube browser, dashboard reports, interactive reports and corporate scorecards.
Features include data extraction, validation, and transformation,
OLAP cube generation, SQL code generation, incremental and full data load, and reverse engineering of data warehouses and
OLAP cube definitions.
Quantitative marketing scientists store corporate data in multidimensional
OLAP cubes. In order to build an
OLAP cube, these trained analysts must know in advance the sorts of relationships (and data) that will be considered: products sold by month, products sold by contact center agent, agent performance by region, agent performance by time of day, agent performance by product and so on.
One of the most common of these analytical tools is On-Line Analytic Processing (OLAP), which presents the data in the form of a multidimensional cube (an
OLAP cube) instead of a vast table of discrete entries representing each transaction.
Unlike TM1, and most other OLAP tools, which are primarily used for viewing data, PowerOLAP enables managers to use the spreadsheet to feed data back into the
OLAP cube for financial modeling, "what if" analysis and ad hoc reporting.
Once you selected the suitable database scheme and compiled fact and dimension tables in the form of an
OLAP cube; you can start exploring the multidimensional data (Lachev, 2005).