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Business Intelligence Glossary – Common Jargons

Business Intelligence Glossary

Business intelligence and data warehousing technologies are heavy on jargons. There are specific terms to define specific method and processes. For a beginner, it may be confusing to go through a technical paper to grasp clear understanding.

This article gives quick definition of some of the most common terms that you will come across in BI & DW domain.


One way of speeding up query performance. Facts are summed up for selected dimensions from the original fact table. The resulting aggregate table will have fewer rows, thus making queries that can use them go faster.


Attributes represent a single type of information in a dimension. For example, year is an attribute in the Time dimension.

Balanced Scorecard

A performance management tool that summarizes an organization’s performance through multiple perspectives on a single page. It is used to integrate performance measures into the basic management structure of the organization.

Conformed Dimension

A dimension that has exactly the same meaning and content when being referred to from different fact tables.


A reporting tool that presents an array of important indicators on a single screen. The information presented can include measurements, metrics, and scorecards. Dashboards often present a combination of metrics from across different areas, or in a mixed set of numeric and visual formats.

Data Mart

Data marts have the same definition as the data warehouse (see below), but data marts have a more limited audience and/or data content.

Data Staging

The data staging area is a system where all the data extraction, transformation and loading (ETL) operations are performed. This is the work area where data warehouse developers clean, summarize, filter, decode and prepare data.

Data Warehouse

A warehouse is a subject-oriented, integrated, time-variant and non-volatile collection of data in support of management’s decision making process (as defined by Bill Inmon).

Data Warehousing

The process of designing, building, and maintaining a data warehouse system.


The same category of information. For example, year, month, day, and week are all part of the Time Dimension.

Dimensional Model

A type of data modeling suited for data warehousing. In a dimensional model, there are two types of tables: dimensional tables and fact tables. Dimensional table records information on each dimension, and fact table records all the “fact”, or measures.

Dimensional Table

Dimension tables store records related to this particular dimension. No facts are stored in a dimensional table.

Drill Across

Data analysis across dimensions.

Drill Down

Data analysis to a child attribute.

Drill Through

Data analysis that goes from an OLAP cube into the relational database.

Drill Up

Data analysis to a parent attribute.

EDW – Enterprise Data Warehouse

A database environment designed for, and dedicated to, providing a single, comprehensive view of an enterprise. It is a reliable source of consistent, contextual, and controlled information for tactical and strategic decision-making.


Stands for Extraction, Transformation, and Loading. The movement of data from one area to another.

Fact Table

A type of table in the dimensional model. A fact table typically includes two types of columns: fact columns and foreign keys to the dimensions.


Granularity refers to the level of detail or summarization of data in the data warehouse.


A hierarchy defines the navigating path for drilling up and drilling down. All attributes in a hierarchy belong to the same dimension.


Data about data. For example, the number of tables in the database is a type of metadata.


A measured value. For example, “Total Sales” is a metric.


Multidimensional OLAP. MOLAP systems store data in the multidimensional cubes.


On-Line Analytical Processing. OLAP should be designed to provide end users a quick way of slicing and dicing the data.


Relational OLAP. ROLAP systems store data in the relational database.

Snowflake Schema

A common form of dimensional model. In a snowflake schema, different hierarchies in a dimension can be extended into their own dimensional tables. Therefore, a dimension can have more than a single dimension table.

Star Schema

A common form of dimensional model. In a star schema, each dimension is represented by a single dimension table.


A preserved view of a data set at a particular instant in time. Ex: A snapshot of certain Banner tables is used to produce GDEU reports.

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