In this section we offer a useful glossary of the most common terms around our solutions:

(Alphabetic Order)

Cube: The physical data structure containing the indicators in the different levels or dimensions based on time periods.

Data Mart: This term is used to identify a subset of data warehouse for a certain group of users or business functions.

Data Mining: Process that focuses on identifying understandable, useful and reliable patterns of the data included in a Data Warehouse in order to understand its behavior.

Data Warehouse: An organizational information deposit that concentrates data from different databases in order to make easier consultations and reduce response times.

Dimensions: The approach under which the indicator is being evaluated.

Drill Across: It is the action of breaking down a metric by a dimension that belongs to another cube.

Drill Down: It is the action of breaking down a metric in its various dimensions. It is known as "navigate" within an indicator.

Drill Up: It is the action of collapsing an indicator that has been broken down by one dimension.

ETL: For Extract, Transform and Load. It is the process that allows organizations to move, reformat and clean data from multiple sources, and then load it into another database, data mart or data warehouse for analysis, or another operating system to support a business process.
ETL processes can also be used for integration with legacy systems.

Indicators: The value representing the mathematical measurement of one aspect of the business. The  monitoring o the indicators allows executives to make operational or strategic decisions.

Base Indicators: Included within the cube and can change their value in different time periods. Some examples are: Banking, Accounting, Sales, Inventories, etc.

Composite Indicators: Formulas created from Base Indicators that may or may not belong to the same cube.

Indicators Online: These values provide a better and detailed understanding of simple indicators and can be viewed at first hand in operational systems.

Business Intelligence: Skills, strategies and technologies used to help a business in its management and creation of knowledge through the analysis of existing data. Its role is to support the strategies and business operations with timely and relevant information that can significantly reduce total operating costs, dramatically improve productivity and / or enable strategic capabilities.

Metadata: Non-operational data used to document the way a Data Warehouse is built.

OLAP: Online Analytical Processing defined as technology that helps streamline data operation in different organizational levels (or dimensions) and time periods.

Periods: The time dimension, which is always implicit in any indicator.

Repository: It is the database where the metadata and the information cubes are stored.

ROLAP: Relational Online Analytical Processing defined as OLAP systems and tools built on a relational database.

Sources:
Participant Manual - Artus Basic Administrator, Bitam.
Wikipedia, www.wikipedia.com