The Language of Business Intelligence

Business Intelligence has a language all its own. That’s why we created a glossary of Business Intelligence terminology. Having trouble understanding certain BI concepts? Check out this glossary.

Browse Alphabetically

A-D, E-H, I-L, M-P, Q-T, U-Z

A – D

Ad-Hoc
In Latin ad-hoc means, "for this purpose only" and thus ad-hoc reporting is a way to create dynamic, often temporary queries to handle specific questions that no existing report answers.

Analytical Based Pricing
Pricing techniques associated with adopting a structure based on focused analytics that maximizes the supply chain for an organization or business.

Analytics
Continuous and iterative analysis and research data from past performance to gain insight and improve business planning to generate a competitive advantage.

ANSI
American National Standards Institute, the recognized standards publishing body for a range of businesses, professions, and industries.

Aggregation
A powerful performance tool, aggregations pre-summarize detail data into smaller tables along a specific line of analysis or dimension (such as time). This allows report queries to process against smaller data sets.

Atomic Level Data
The lowest granularity or level of data available, also referred to as "detail" data. (Example: Individual sales transaction line items.)

Attributes
A Logical Data model of your business first breaks down by Dimension (Example: Time), which then breaks down to Attributes (Examples: Year, Month, Day). Attributes will then typically relate to one or more columns in a database.

Balanced scorecard
A performance management tool that summarizes an organization's performance from multiple perspectives on a single page.

Base Tables
Fact Tables with data stored at the lowest level of detail.

Benchmarking
A set standard that allows performance to be measured against. In business intelligence, this can be certain performance measurements understood only through statistical analytics of large data collections past performance for an organization.

Boyce–Codd normal form
"A relation or table is in BCNF if it is already in the third normal form and no key attribute is functionally dependent on any non-key attribute."

Business intelligence
Information systems that assist managers in decision making by allowing extensive, user driven data analysis via a variety of modeling techniques, or by providing easy, intuitive access to structured information.

Business Performance Management (BPM)
A framework that optimizes the execution of an organization’s strategy and consists of a set of integrated processes, supported by technology (such as performance dashboards, data warehousing, analysis and reporting) that enables organizations to communicate, monitor, measure and manage performance against goals

Candidate Key
A single attribute or a set of attributes that uniquely identifies an instance of an object set or entity type and can be a candidate to be chosen as the primary key.

Cardinality
Refers to the relationships among data tables in a Schema. These relationships can be defined as: one-to-one, one-to-many or many-to-many. For example, a small database containing Store data and Employee data may be defined so that there is a one-to-many relationship between Stores and Employees (i.e. one store has many employees, but only one employee has one store).

Cloud computing
Cloud computing is the delivery of computing as a service rather than a product, whereby shared resources, software and information are provided to computers and other devices as a utility (like the electricity grid) over a network (typically the Internet).

Composite Key
Primary key made up of more than one attribute.

Conformed Dimension
Conformed dimensions have consistent definitions, regardless of where they are used. This allows a single query to be run across multiple tables, Data Marts and Data Warehouses.

Consolidation
Group of attribute elements. It serves as „virtual“ attributes and enable performance of a row-level math.

Cube
A powerful analysis tool that is used for viewing and analyzing data in a multi-dimensional format, from any angle, in any combination, through a ‘slice and dice’ and drilldown approach

Dashboard
A dashboard is a visual display of the most important information needed to achieve one or more objectives; consolidated and arranged on a single screen so the information can be monitored at a glance. They provide intuitive indicators, such as gauges and stoplights, show the state of the business at the exact instant the dashboard is viewed or refreshed.

Data cleansing
"Data cleansing or data scrubbing is the process of detecting incomplete, incorrect, inaccurate part of data and correcting (or removing) corrupt or inaccurate records."

Data dictionary
Repository holding the definitions of the data structures in a database. In a relational database, the data dictionary contains the definitions of all the tables, columns, and so on.

Data governance
Data governance is a set of processes that ensures that important data assets are formally managed throughout the enterprise. Data governance ensures that data can be trusted and that people can be made accountable for any adverse event that happens because of low data quality.

Data Mart
Similar in structure and purpose to a full Data Warehouse, data marts are smaller sets of data focused on one particular business subject area. Properly designed with conformed dimensions, data marts can work with, or even as, your enterprise Data Warehouse.

Data profiling
Data profiling is an analysis of the candidate data sources for a data warehouse to clarify the structure, content, relationships and derivation rules of the data. Profiling helps to understand anomalies and to assess data quality, but also to discover, register, and assess enterprise metadata

Data Warehouse
A Data Warehouse is a central repository for all or significant parts of the data collected by the various business systems of an enterprise. The term was coined by W.H. Inmon. IBM sometimes uses the term "information warehouse."

Decision Support System (DSS)
Decision Support systems and tools leverage stored historical data (typically in a Data Warehouse) to help business users make informed decisions. In recent years this term has been slowly replaced with the more encompassing idea of "Business Intelligence."

Denormalize
Process of taking normalized data and converting into an unnormalized form. (Example: Collapsing three tables containing reference information on Years, Months and Days into a single table.) Denormalization brings a Data Warehouse Data Model closer to a Star Schema and is often recommended for performance improvements.

Detail Data
The lowest granularity or level of data available, also referred to as "atomic" data. (Example: Individual sales transaction line items.)

Dimension
Logical grouping of related business attributes that typically form at least one hierarchy or drill-path for analysis. (Examples: Time Dimension = Year > Month > Day; Geography Dimension = Country > State > City).

Drill-Down
Process of finding more detailed data by displaying data at a lower level than was previously shown.

E – H

Element
A unique value of an attribute or metric. (Examples: "Atlanta" is an element of the attribute "City." "July" is an element of the attribute "Month").

Entity Relationship Diagram (ERD)
Graphical representation of your data model that visually identifies objects and the relationships between data elements.

ETL
Acronym for “extraction, transformation and loading.” Process used to populate a Data Warehouse with data from other sources.

Fact Table
A table containing numeric data grouped along one or more dimensions. This numeric data may then be used within calculations for reporting and analysis. Fact tables can contain either atomic or aggregated data and serve as the center of a star or snowflake Data Warehouse schema.

Filter
Objects used to limit data returned in a report by applying specific criteria. It is usually found in the SQL WHERE clause.

Hierarchy
A set of attributes with a defined path for elemental browsing and drilling. Typically (but not always) hierarchies exist within specific dimensions.

HOLAP
Hybrid OLAP, combination of ROLAP and MOLAP. The source data is stored using ROLAP, and the aggregations are stored using MOLAP.

I – L

Index
A data structure used in Relation Databases to help quickly locate rows of data using certain criteria. Indexes provide significant performance enhancement for queries that can leverage them.

Logical Data Model
Logical Data Models graphically represent your business dimensions, attributes and relationships.

M – P

Market Basket
A specific type of analysis that is focused on how multiple items within a single purchase experience (Market Basket) relate to one another. (Example: How often do people who purchase diapers also purchase beer?).

Metadata
Data used to describe the properties of other data.

Metric
A numeric expression that is displayed on a report and is composed of facts and mathematical functions.

MOLAP
Multi-dimensional Online Analytical Programming is an OLAP strategy that stores pre-summarized data in a proprietary file or "cube" structure instead of a relational database.

Natural Key
A strategy of using meaningful codes as your primary database table keys as opposed to using artificial or surrogate keys. (Example: Defining primary key for day attribute using date/time data types.)

Normalize
Breaking out a data structure into multiple tables by removing redundancy. (Example: Separating a single denormalized time reference table that includes Year, Month and Days into three tables.)

Operational Data Store (ODS)
Typically a copy of a transactional or operational database structure that contains only current or near-term data. Often used to stage data prior to processing into a Data Warehouse.

Partition
Strategy of breaking a single relational database table along a specific attribute into multiple smaller tables to reduce the size of the data set that queries must process against. (Example: Splitting a single table of year sales data across twelve tables of monthly sales data.)

Physical Data
Physical Data models graphically represent your relational database structure, including all tables, columns, relationships, keys and indexes.

Primary Key
One or more columns in a table that contains values used to uniquely identify each row or record in that table.

Project
Highest level of intersection between Data Warehouse, metadata repository and user community. It contains reports, metrics, filters, and functions.

Q –T

ROLAP
Relational Online Analytical Processing, An OLAP that stores the data and aggregations in a relational database.

Schema
The set of tables in a Data Warehouse associated with a data model.

Slowly Changing Dimension (SCD)
Dimensional data that occasionally changes and affects how elements in your Data Warehouse relate. (Example: Reorganization of business results in changes to which areas and regions stores belong to.) Slowly changing dimensions require a strategy of how best to represent this changed relationship.

Surrogate Key
An artificial key that contains no encoded information or derived meaning that is created and used as an identifier (ID) or primary key (PK) field within database tables.

Template
Reusable object that defines the layout of a report, including the set and placement of attributes and metrics to be displayed.

U – Z

Warehouse Catalog
A list of available tables that exist in the relational database and are used in your Data Warehouse.