BI Data Warehouse dictionary

Business Intelligence

Business Intelligence (BI) is a broad category of application programs and technologies for gathering, storing, analyzing, and providing access to data to help enterprise users make better business decisions. BI applications include the activities of decision support, query and reporting, online analytical processing (OLAP), statistical analysis, forecasting, and data mining.

It can be described as the process of enhancing data into information and then into knowledge. Business intelligence is carried out to gain sustainable competitive advantage, and is a valuable core competence in some instances.

Data warehouses

A data warehouse is a database geared towards the business intelligence requirements of an organization. The data warehouse integrates data from the various operational systems and is typically loaded from these systems at regular intervals. Data warehouses contain historical information that enables analysis of business performance over time.


Measures are the numbers of interest from the OLAP database. Examples are Revenue, Budget, Contribution, Number of phone call and, Number of requests etc. To be defined as a measure, the data type of the column in the relational OLAP database must be a numerical data type.


Dimensions are used to categorize or to filter the measures from an OLAP database. Examples are Period, Customers, Vendors, Accounts, and Products etc. The data type for a dimension in the relational OLAP database could be any data type, but are often strings.


Measures and Dimensions are used to define objects in TARGIT. The basic object type is the cross table where measures are selected to be displayed in the cells of the cross table, while dimension values of single or multiple dimensions make up the columns and rows. Graphical object types include objects like Bar charts, Pie charts, Maps etc., but they can all be perceived as a graphical presentation of data in a cross table.

Document (analyses, reports and dashboards)

One document can be based on just one object, but often a document is made up of multiple objects. Multiple objects are by default dynamically linked to each other, meaning that selecting a dimension value in one object will automatically be applied as a filter on the other objects. In a multi-object document it is often recommended that all objects are based upon the same measure(s); however from different angles, i.e. with different dimensions.


Criteria’ is the term used when applying filters or when limiting data in TARGIT. Criteria may be applied in four different ways:

  • Global criteria. Criteria that affect all objects in the current analysis.
  • Local criteria. Criteria that affect only the current
  • Drill Down criteria. Criteria that are applied when clicking a dimension value in one object. The selected dimension value will apply as a criterion on all other objects.
  • Comparison elements criteria. These are criteria that are defined per element of a comparison – effectively criteria that could affect data of a single column in a crosstab.
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