Business Intelligence (BI) world is rapidly evolving, and it's crucial to keep up with the latest terms and jargon. Whether you're a seasoned BI professional, an aspiring analyst, or simply curious about the field, understanding these terms can offer a more profound grasp of the subject. Our comprehensive glossary, spanning from A to Z, demystifies the complexities of BI and provides succinct explanations for each term. Dive in and stay informed, one term at a time.

Business Intelligence Terms Glossary

Ad-Hoc Reporting: Reporting done on-the-fly. Allows users to create and modify reports spontaneously based on current needs.

Agile: An iterative approach to project management and software development that prioritizes flexibility and collaboration. It involves regular feedback and adjustments to ensure the final product meets user needs.

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Analysis Services: Tools and platforms used to analyze datasets, often provided by specific vendors, to support business decision-making.

Analytics: The process of collecting, processing, and analyzing data to uncover patterns, correlations, and insights to inform decision-making.

Benchmarking: The process of comparing a business's performance metrics to industry bests or best practices from other industries.

Business Analytics: The convergence of business and analytics, it uses data and statistical methods to provide actionable insights for businesses to improve processes, increase efficiency, and drive profitability.

Back-End: Refers to the part of a software application or platform that operates behind the scenes, often dealing with data storage, retrieval, and management.

BI Application Designer: A professional who designs and develops Business Intelligence applications tailored to an organization's needs.

BI Project Sponsor: The individual or group providing resources and support for a BI initiative, ensuring its alignment with business objectives.

Big Data: Large and complex data sets, often from diverse sources, that traditional database systems struggle to process. They require special tools and methodologies for analysis.

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Business Driver: Key operational or strategic factors that influence business performance and decision-making.

Business Intelligence (BI): The practice of collecting, analyzing, and presenting business data to support decision-making and enhance organizational performance.

Business Lead: The primary person responsible for driving a business initiative or project, often collaborating with IT and other departments.

Business Process Intelligence (BPI): Tools and techniques that help in the analysis of business processes.

Business Owners: Stakeholders or leaders in an organization who have decision-making authority, often regarding strategic initiatives or projects.

Churn Rate: A KPI that measures the rate at which customers leave or cease paying for a product or service.

Collaborative Business Intelligence: A BI approach that integrates collaboration tools and processes to enable teams to share and discuss data insights and analytics.

Cube: In BI, a cube is a multi-dimensional data structure used in OLAP databases that allows for the quick querying and analysis of data.

Dashboard: A visual representation of data, showcasing metrics, KPIs, and other key data points relevant to a business or specific process.

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Data Architect: An expert who designs and structures data systems and databases to ensure data integrity, efficiency, and accessibility.

Data Architecture: The overarching structure, organization, and design of databases and data systems within an organization.

Database: A structured set of data held in a computer, especially one that is accessible in various ways.

Database Warehouse Management System (DBMS): Software that manages databases, providing functionalities for data storage, retrieval, and manipulation.

Blog: What is SQL Data Warehouse?

Data Cleansing: The process of identifying and rectifying (or removing) errors and inconsistencies in data to improve its quality.

Data Feed or Live Data Feed: A stream of continuously updated data provided in real-time or near-real-time.

Data Intelligence: The practice of using data analytics and other techniques to gain insights, often used to inform strategy and decision-making.

Data Manager: A professional responsible for overseeing the storage, organization, utilization, and analysis of data in an organization.

Data Mining: The process of analyzing large datasets to uncover hidden patterns, correlations, and insights using statistical methods and machine learning.

Data Model: An abstract representation of the organizational data, often visual, which provides a structured and logical view of data elements and their relationships.

Data Source: Any repository or file where data is stored and from which it can be retrieved and analyzed.

Data Discovery: The process through which businesses collect and analyze data from various sources to gain insight or solve specific queries.

Data Integration: The process of combining data from different sources to provide a unified view.

Data Lake: A storage repository that can store a large amount of structured, semi-structured, and unstructured data.

Data Mart: A subset of a data warehouse that is usually oriented to a specific business line or team.

Data Visualization: The representation of data in a graphical format, making it easier to understand trends, outliers, and patterns.

Data Warehouse: A centralized repository that aggregates data from various sources to support business intelligence activities, including reporting, data analysis, and other analytics.

Data Warehouse Automation (DWA): The use of technology to automate the various processes involved in the design, development, and management of data warehouses.

Data Warehouse Developer: A professional who designs, constructs, tests, and maintains a data warehouse system.

Descriptive Analytics: Techniques that describe historical performance.

Dimension: In BI, a dimension provides context to data, for example, time, geography, or product categories in a sales dataset.

Dimension Table: A table in a star schema of a data warehouse that stores attributes, or dimensions, that describe the objects in a fact table.

Drillthrough/Drill down: BI functionalities that allow users to click on a data point and view detailed underlying data.

ETL Tool: Software that extracts, transforms, and loads data into a data warehouse.

Extract, Transform, Load (ETL): A process in database usage to pull data (extract) from a source, transform it into the required format, and then load it into a data warehouse.

Executive BI: BI tools and systems specifically designed for C-level executives or decision-makers in an organization.

Executive Dashboard: A simplified, high-level visual representation of key metrics and KPIs tailored for executive consumption.

Fact Table: In data warehousing, a table that contains the measures (like sales or profits) and keys to related dimension tables.

Field Transformation: The process of changing the format, structure, or value of a data field.

Financial Reporting: The process of producing statements and reports that provide a summary of an organization's financial performance and position.

Front-End: The interface or user-facing part of a software application or platform.

Full Load: The process of populating a data warehouse or database with data for the first time, often followed by incremental loads for updates.

Governed Data: Refers to data that is managed and overseen to ensure accuracy, consistency, and security. By governing data, organizations ensure it's reliable, compliant with regulations, and suitable for decision-making.

Heat Map: A data visualization tool that shows levels of activity in different areas of a dataset using color.

Hierarchy: In the context of data, a hierarchy refers to levels of data categorized in a structured way, often from broad to specific. For instance, in a geographical hierarchy, you might have country at the top, followed by state, then city.

Human Resources Dashboard (HR dashboard): A visual representation of key HR metrics and performance indicators. These dashboards offer an at-a-glance view of data related to employee performance, recruitment, retention, and more, assisting HR professionals in making informed decisions.

In memory: Refers to data storage within a system's main memory (RAM) as opposed to on a disk. This facilitates faster data access and processing since reading from and writing to RAM is much quicker than disk operations.

Incremental Load: The process of only loading new or changed data into a database or data warehouse, rather than reloading the entire dataset. This approach optimizes performance and reduces the time and resources required for data refresh processes.

Index: A data structure that improves the speed of data retrieval on a database. By indexing a database, you can quickly locate and access the data without scanning every row, much like an index in a book helps you find information faster.

In-Memory Analytics: Refers to the analysis of data directly from system memory (RAM) instead of querying from a disk-based database. This results in significantly faster data processing and real-time analytical performance.

Inmom Approach: A variation of "Inmon Approach," named after its founder Bill Inmon. This approach promotes the creation of a centralized data warehouse that feeds data marts. It emphasizes ensuring data integrity, consistency, and reliability across the organization.

Jet Data Manager (JDM): A tool within the Jet Analytics suite that aids in transforming data into a structure suitable for reporting and analysis, ensuring data quality and consistency.

Jet Analytics (Formerly Jet Enterprise): A complete BI and reporting solution that integrates seamlessly with Microsoft Dynamics. It provides fast, flexible financial and business reporting inside of Excel.

Joins: An SQL operation used to combine rows from two or more tables based on a related column.

Kimball Approach: A data warehousing methodology by Ralph Kimball which focuses on building data marts. It's a bottom-up approach emphasizing the fast delivery of business solutions.

Key Performance Indicator (KPI): A Key Performance Indicator, or KPI, is a measurable value that indicates how effectively an organization is achieving its key business objectives. KPIs are used across industries to evaluate the success of an activity, operation, or initiative. They are often chosen based on an organization's strategic goals, allowing for performance comparison over time or against set benchmarks. By monitoring KPIs, decision-makers can identify areas needing improvement, make informed choices, and align their actions with organizational goals. Essentially, KPIs act as a compass, guiding businesses towards desired outcomes and ensuring consistent progression towards set targets.

Lead/Lag: Analytic functions that provide access to more than one row within a result set without having to perform a self-join. They offer insights into preceding and succeeding data points.

Level: In data hierarchies, it refers to the layers of detail or granularity.

Many-to-Many Relationships: When multiple records in a table are related to multiple records in another table.

Marketing Dashboard: A visual representation showcasing marketing metrics, KPIs, and other key data points.

Multidimensional expressions (or MDX): A query language for OLAP databases that makes it possible to design complex and sophisticated queries.

Measure: Quantitative values that are based on a column in the fact table and can be aggregated.

Member(s): Individual data points within a dimension in an OLAP cube.

Metadata: Metadata is often referred to as "data about data." It provides a detailed description or information about other sets of data, making it easier to retrieve, manage, and understand them. Metadata can range from the basic (like the creation date of a file) to the more complex (like the methodology used in data collection). In databases, metadata could describe the structure of tables, data types, relationships, and origins. For digital content, metadata can include information about the content's creator, format, and rights. Overall, metadata plays a critical role in managing, categorizing, and maintaining data in various systems.

Metrics: Standards of measurement that offer insights into performance, often representing important business aspects.

Microsoft BI Stack: A collection of tools by Microsoft that support data analysis and reporting, including SSIS, SSAS, SSRS, and Power BI.

Mobile Analytics: Analytics solutions tailored for mobile devices, aiding in accessing insights on-the-go.

Mobile Dashboards: Visual interfaces designed for mobile devices that display key business metrics.

Online Analytical Processing (OLAP): A category of software tools that allows users to analyze data from multiple dimensions.

Operational Reporting: Regular, short-term reports used for the day-to-day management of operations.

Pivot Table: A data processing tool used to query, arrange and aggregate data from a database.

Predictive Analytics: Techniques that use historical data to predict future outcomes.

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Prescriptive Analytics: Techniques that suggest a course of action based on data analysis.

Power BI: A suite of business analytics tools by Microsoft which allow visualization of data and sharing insights across an organization.

Microsoft Power BI Course

Project Manager: A project manager is a professional responsible for planning, initiating, executing, monitoring, and closing projects. They act as the bridge between the project team and stakeholders, ensuring that requirements are clear and the project's goals are met on time and within budget. Their responsibilities often include risk assessment, resource allocation, timeline creation, and stakeholder communication. A project manager utilizes various tools, methodologies, and best practices to bring structure to the process and drive efficiency. In essence, they are the guiding force behind a project, ensuring that every phase progresses smoothly towards a successful completion.

Query: A request for data retrieval from a database.

Real-time Analytics: Analyzing data immediately as it is produced or ingested.

Relational Database: A database structured to recognize relations between stored items of information.

Report Distribution: The process of delivering reports to end-users or stakeholders.

SaaS BI (Software as a Service for BI): Cloud-based BI tools that are provided as a service to organizations without the need for server installation or maintenance.

Self-Service BI: Tools and platforms that allow business users to access and work with corporate data even if they don't have a background in statistical analysis or data mining.

Sales Dashboard: A visual representation of sales metrics and KPIs, aiding in tracking performance and forecasting.

Schema: The structure of a database described in a formal language supported by the database management system.

Scorecard: A visual display of important metrics and KPIs, often used to monitor performance against objectives.

Services Delivery Manager (BI delivery team): A role overseeing the delivery of BI solutions, ensuring that projects are completed timely and meet requirements.

Slowly Changing Dimensions (SCD): Techniques in handling changes over time in the attributes of a dimension in data warehousing.

Snowflake Schema: A normalized form of a Star Schema in a data warehouse, where dimensions are normalized into multiple related tables.

SQL Server Analysis Services (SSAS): A tool by Microsoft used to create Online Analytical Processing (OLAP) and data mining applications.

SQL Server Integration Services (SSIS): A tool by Microsoft for data integration and workflow applications.

Star Schema: The simplest style of data mart schema with a central fact table and multiple dimensions.

Stored Procedure: A set of SQL statements with an assigned name that can be executed at once.

Subject Matter Expert (SME): An individual with deep knowledge in a particular area or topic.

Surrogate Key: An artificial key, usually an auto-incremented number, used in the database as a unique identifier for a record.

Table Relations: The connections between different tables in a database, established based on common columns.

Time Series Analysis: Methods that analyze a series of data points in chronological order.

View: A virtual table based on the result-set of an SQL statement. It contains rows and columns like a real table.

Visual Studio: An integrated development environment (IDE) by Microsoft used for developing software applications, web sites, web apps, and more.

The Business Intelligence field is continuously evolving, with new concepts and technologies emerging, so the terminology list can keep growing. Add this page to your bookmarks because we will be updating it frequently!


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