Business Intelligence Glossary


For better understanding of all BI terms and related articles we made a business intelligence glossary. On this page you can find all BI terms used in our articles and more. Ease your BI exploration with business intelligence glossary!


Ad Hoc Query: A highly specialized, one-off report solving a one-time problem.

Ad Hoc Reporting: A business intelligence model in which rapid reports are generated for end-users to be able to modify and drill through the data.

Aggregate Data: Data collected, formed or calculated from different sources or measurements.

Analysis: Processing of information in order to identify trends, draw conclusions and reasonable generalizations.

Analytics: Identification, examination, and explanation of meaningful patterns in data.

API (Application Programming Interface): a code that helps two software programs to interact with each other.

Attribute: a field which is a part of data dimension, e.g. a column in a table, a report, or a chart.


Benchmarking: Comparative analysis of a company’s business activities, performance metrics, and best practices of other companies in the same industry.

Balanced scorecard: A performance management tool that holistically captures an organization’s performance from several points such as sales results and inventory levels.

Big data: Highly large data sets that may be analyzed computationally to reveal patterns, trends, and associations, especially relating to human behavior and interactions.

Binning and grouping: a form of data visualization in which individual data values are sorted by class or category and displayed graphically in order to explain the significance of data.

Bootstrap: a framework for developing responsive web applications.

Business Analytics (BA): Repetitious methodical review of the data based on statistical analysis.

Behavioral analytics: Exploitation of the data about people’s behavior to understand the intent and in order to predict future actions.

Business intelligence (BI): A technology-driven process to examine data and display actionable information to support corporate executives, business managers and other end-users in taking business decisions.

Business logic: a program that controls the connection between end user and database.

Business metric: a measure used by businesses in order to track and monitor the success or failure of different business processes.

Business Performance Management (BPM): Platform boosting the execution of a business’s strategy, consisting of multiple integrated processes which involve dashboards, warehouses, visualization analytics, reports, and others.


Case study: a detail research of some subject or project with practical results.

Cloud Analytics: A set of tools and techniques which are intended for analysis, extraction of information from massive databases and its presentation in any readily available form.

Contextual Data: Information that provides the context and comprehensively characterizes an object.

Criteria: A benchmark or standard for evaluating or conducting something.

C#: an object-oriented programming language.

Cube: A multidimensional array of data that is used in OLAP.

Customer Relationship Management (CRM): Application software which is designed for management of relationships and interactions with current and potential customers.


Dashboard: Visual representation of the most important data on a single screen in order to monitor the data at a glance.

Data: Collected information, facts, and statistics for further reference or analysis.

Data Analytics: The processing of raw data with a view to extracting meaningful insights and conclusions and detecting patterns by means of specialized systems or software.

Data cleansing: a process of removing incorrect, duplicated or incomplete information from the database.

Data Cube: A database structure with multiple dimensions which can be stored, combined and manipulated to enable browsing.

Data model: a principle of data organization and its elements relationship organization.

Data point: definable element in data set.

Data visualization: A method of presenting data in a visual format to assist in better understanding of the crucial points.

Demand planning: a process of forecasting the demand for a product or service so that it can be produced and delivered more efficiently and in accordance with customer requirements.

Drilling: The process of navigating through different levels of data in multidimensional sets.


Embedded Analytics: The integration of external Business Intelligence tools and capabilities into existing business software.

End User: The person who a software program or hardware device is designed for. The end purpose of a software or hardware product is to be helpful for the user.

Enterprise content management (ECM): a program or tool which help organization to store, manage and obtain company’s critical data.

Enterprise Data Warehouse (EDW): A database environment created to deliver a single view of an enterprise and is aimed to be a reliable source of controlled information for strategic planning and decision making.

Enterprise Resource Planning (ERP): The process of management of all the information and resources included in a company’s operations by integrating computer system.

Export: Conversion of data in various output formats with the goal of making it suitable for other programs.

Extensibility: software or application capability to be extended or modified.

Extract, Transform, Load (ETL): One of the basic processes in data warehouse administration that includes extraction of the data from external sources, data conversion, and cleansing, loading into the data storage.

Extranet: The corporate network protected from unauthorized access and used for intra-corporate goals.


Fact: An individual record of business activity that is stored in a data warehouse. Each fact contains one or more measures: numbers, amounts, or prices, and a series of dimensions by which the fact can later be analyzed. Facts are the foundation of data warehouse tables and OLAP data cubes.

Fact table: The main table of the data warehouse including the business process measurements, metrics or facts.

Filter: Software that processes text (to remove unwanted spaces; to format it for the exploitation in another application).

Financial data management (FDM): a set of processes and tools companies use to track their financial data.

Financial statements (or financial report) is a formal record of the financial activities and position of a business, person, or other entity.

Forecasting: A prediction or estimation of coming events.


Gap Analysis: The study of whether the available business data fully meets an organization’s end needs. It responds to questions such as: Are the right data sources accessible? Is there enough data to reach meaningful conclusions? Etc.

Gauges: Devices for measuring the magnitude, amount, or contents of something, usually with a visual display of such information.

Granularity: The level of detail or summarization of data in the data warehouse.

Geospatial analysis: Gathering, display, and manipulations of images, GPS, satellite photography and historical data, described explicitly in terms of geographic coordinates or implicitly, in terms of a street address, postal code, or forest stand identifier as they are applied to geographic models.

GUI (Graphical user interface): an interface type that allows users to interact with electronic devices using graphical icons and visual indicators.


Hadoop: A programming framework that supports processing of large data sets in a distributed computing environment.

Heat map: a representation of data in two-dimensional way when values are represented by colors.

Hierarchy: An arrangement of items in order of relative importance.

HOLAP (Hybrid Online Analytical Processing): A combination of Relational OLAP (ROLAP) and Multidimensional OLAP (MOLAP), where one part of the data is stored in a MOLAP storage and another part is in a ROLAP one.

Human Resources Dashboard (HR dashboard): enterprise data representation which displays important HR KPIs.


Index: The dataset maintained by search engine indexing.

Infographics: Visual representations of the data, through which the information is easily understood.

Institutional Performance Management (IPM): The process of basing an organization's actions and decisions on actual measured results of performance. It integrates performance measures, benchmarks, and goals in order to achieve optimal results.

Intelligent content: digital text, image, video, audio or multimedia data adaptation with coding in order to be processed automatically for various purposes and adapted for access to various devices and interfaces.

Interactive Data Viewer: A tool allowing to make calculations, sort data and visualize the obtained results in a graphic manner right away.

Interactive Visualization: A technology which enables representation of the data by means of manipulation of visual elements, such as chart images, changing their color, brightness, size, shape or motion.

Intranet: An internal network of the company that is accessed by the staff only.

IoT (Internet of Things): a system of interconnected computing devices, mechanical and digital machines, objects, animals or people that are equipped with unique identifiers (UID) and have the ability to transmit data over the network without requiring interaction from person to person or from person to computer.

IT solution: a set of programs, tools and software that IT companies sell to solve customer’s problem.


Javascript: A high-level interpreted programming language that is used in BI applications to customize styling, branding, and visualizations. It is as well characterized as dynamic, weakly typed, prototype-based and multi-paradigm.

Joint Application Development (JAD): A method that includes the participation of the end-user in the process of the application design and development.


Key Performance Indicator (KPI): A quantifiable measure for the evaluation of the company’s success in terms of achieving their objectives.

Kimball Approach: The dimensional approach made popular by in Ralph Kimball, which states that the data warehouse should be modeled using a Dimensional Model with a star schema or snowflake.

Knowledge management: a concept which  includes data gazaring, optimization, analyzation and sharing inside the company it terms of its resources, staff skills and documentation.


Lead and Lag: Analytical functions to work with multiple rows within a table.

Lead generator: a marketing activity aimed at informing about the availability of a product or service that provider can offer.

Level: A grouping of elements in a dimension of an OLAP cube.

LOB (Line-of-business): a set of products and services that company or organization can offer.

Location Intelligence: A business intelligence tool that allows gathering and interpreting geographic data for business purposes.

Logical Data Model: A model to display the conceptual structure of a data warehouse, independent of software or hardware implementation constraints. It clearly shows the concepts and their various relationships.

Lead generation: Exploitation of a computer program, a database, the Internet, or a specialized service to gain information which helps extend the scope of business.


Management Information System (MIS): A computerized information-processing system designed to support the activities of a company and organizational management.

Measure: A numeric value stored in an OLAP cube, for example, sales, prices, discounts etc.

Metadata: Data about data, or the sum of all documentation about the data warehousing process. It describes the contents of the data warehouse, its structure, and the processes involved in its setup.

Microsoft SQL Server: A relational database management system developed by Microsoft. As a database server, it is a software product with the primary function of storing and retrieving data as requested by other software applications which may run either on the same computer or on another computer across a network (including the Internet).

Mobile business intelligence: a software that extends desktop BI so they can operate on mobile devices.

MOLAP (multidimensional online analytical processing): Online analytical processing (OLAP) that indexes directly into a multidimensional database.

Multidimensional Expressions (MDX): A query language for OLAP systems and relational databases. It is also a calculation language, with syntax similar to spreadsheet formulas.


Named entity: the term used in data mining, it is a phrase that clearly identifies one element from a set of other elements that have similar attributes.

Non-value-adding: Activities within a company or supply chain that do not directly contribute to satisfying end consumers’ requirements. Activities that consumers do not pay for.

Normalization: a design technique for relational databases that allows a database and its users to consistently record and retrieve individual transactions with minimal risk of data loss, data error, or duplication.

NoSQL: Non-relational database with a capability of zooming optimized for the use of data models without a single scheme.


OLAP cube: A multidimensional database that is optimized for data warehouse and online analytical processing (OLAP) applications.

Online Analytical Processing (OLAP): Computer data processing that allows extracting and viewing data from different perspectives easily and quickly.

Online Transaction Processing (OLTP): The software which is intended for the support of transaction-oriented applications on the internet.

Operational Analytics: Data Analytics that is focused on improving the internal operations of the enterprise.

Operational Reporting: Real-time reporting on the enterprise day-to-day activities.


Personalization: The process of designing or creating something according to the end-user’s requirements.

Pivot table: A table that summarizes data from another table, and is made by applying an operation such as sorting, averaging, or summing to data in the first table, typically including grouping of the data.

Platform: A computing platform or digital platform is the environment in which a piece of software is executed. It may be the hardware or the operating system (OS), a web browser and associated application programming interfaces, or other underlying software, as long as the program code is executed with it.

Predictive Analysis: A BI analytic engine that enables users to forecast future business events.

PRM (Partner relationship management): a set of strategies, processes and software organization uses in order to optimize business processes with partners who sell their products.

Problem management: an  IT Service Management area focused on solving incidents and problems caused by end-user errors or IT infrastructure problems, and preventing the recurrence of such incidents.


Query: A request to the database in order to help select necessary information.

Query analysis: A process utilized in databases which make use of SQL in order to determine how to further optimize queries for the performance.


Ranking: A process of arranging items in a hierarchy.

Relational Database: A database based on a relational data model.

Relational Database Management System (RDBMS): A system used to store data stored in relational tables, typically organized according to the relationship between different data values.

Report: A detailed account of company's activities formed by means of the OLAP software.

Reporting: The collection of data from various sources and software tools for presentation to end-users in a way that is understandable and easy to analyze.

ROLAP (Relational OLAP): Online analytical processing that uses relational databases.

Roll UP: BI feature that helps users to summarize data at a higher level.


Scalability: The ability to increase volumes of data and the number of users to the data warehouse.

Schema: The structure that defines how data inside a database is organized.

SCM (Supply Chain Management): supervision of materials, information and finance in the process of their transition from supplier to manufacturer, to wholesaler, to retailer and consumer.

Slice and Dice: The division of large datasets into smaller portions so that they can be analyzed from different perspectives.

Software as a Service (SaaS): A software delivery model in which software is licensed on a subscription basis and is centrally hosted and typically accessed by end-users through a client via a web browser.

Structured Query Language (SQL): A declarative programming language that is used in relational databases for creation, modification and data management.


Table Relations: A basic element of a dataset created by means of comparing common fields in related tables.

Technology-Enabled Relationship Management (TERM): An approach that assumes that a company gets comprehensive customer information (needs, preferences, buying patterns) and contact channels (marketing, sales, service, and support).

Text Mining: Text analytics or, in other words, receiving good quality information from a text.

Transactional Systems: Databases that allow tracking daily transactions of an enterprise.

Transformation: Main component of the ETL (extraction-transformation-loading) component of data warehousing. It includes activities that manipulate, validate and standardize data.


Uniform Resource Locator (URL): A character string that identifies an Internet document’s exact name and location.

Unstructured Data: Information without certain structure or the one which is not organized in a proper manner.

UBA (User behavior analytics): a technology of tracking, collecting and assessing user’s data and activities with a help of monitoring systems.

User Experience: User perception and response resulting from their interaction with a company, product, or service.

User Interface: An interface that ensures transfer of information between the user and the computer system.


Visualization: Representation of an object, situation, or set of information as a chart or another image.

Visual Analytics: An application that involves visualizations and therefore allows identifying trends and key patterns.

Visual Workflow: An analytics feature that has transactional capabilities directly into a user’s interface.


What-if analysis: A method that allows modeling different variants of the project implementation.

Wireframe: A set of schemas with blocks and relationships.

Workboard: An interactive tool for data visualization that reflects the relevant KPIs and other data analysis, allowing to work directly on it and perform further analysis.

Write-Back: A capability to update an original system and at the same time save the source data.

Data Warehouse: A large store of data accumulated from a wide range of sources within a company and used to guide management decisions.


Zero Latency Enterprise (ZLE): An organization that can respond to internal and external events as they occur because information is exchanged across departmental or divisional boundaries without any delay.

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