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The process of discovering patterns and knowledge from large amounts of data. It's often used for market research and customer segmentation.

Creating reports that provide information to help organizations with decision-making. These reports pull from data analysis, visualizations, and KPIs.

A computing method that allows users to easily and selectively extract and query data to analyze it from different points of view.

A visual representation of the most important information needed to achieve one or more objectives; consolidated on a single screen so information can be monitored at a glance.

A measurable value that demonstrates how effectively a company is achieving key business objectives. Organizations use KPIs to evaluate their success at reaching targets.

Techniques that use statistical models and forecasting methods to understand the future and make data-driven decisions.

A process in database usage and especially in data warehousing that involves extracting data from outside sources, transforming it to fit operational needs, and loading it into the end target.

A system used for reporting and data analysis, often considered a core component of business intelligence. It is a central repository of integrated data from one or more disparate sources.

Large sets of data that are analyzed computationally to reveal patterns, trends, and associations, especially relating to human behavior and interactions.

The graphical representation of information and data. By using visual elements like charts, graphs, and maps, data visualization tools provide an accessible way to see and understand trends and patterns.

A method of data analysis that automates analytical model building. It is a branch of artificial intelligence based on the idea that systems can learn from data, identify patterns and make decisions with minimal human intervention.

Business intelligence systems that enable business users to access and work with corporate data even though they do not have a background in statistical analysis, business intelligence, or data mining.

A subset of a data warehouse that is usually oriented to a specific business line or team. It can increase the end-user response time due to the reduction in volume of data.

A business intelligence process designed to answer a single, specific business question. Users can create reports and data visualizations that are only meant for a one-time use.

The practice of comparing business processes and performance metrics to industry bests and best practices from other companies.

A feature that allows users to view detailed data that comprises aggregated data. It helps users to break down the aggregated information to finer granularity.

The process of detecting and correcting (or removing) corrupt or inaccurate records from a record set or database. It includes identifying incomplete, incorrect, inaccurate or irrelevant data and then replacing, modifying or deleting this data.

The process of using data to understand past and current business performance and make informed decisions. It is the foundation of business intelligence and data-driven strategy.

The process of collecting data from various sources and consolidating it into a single dataset that presents significant information in a visual context, which can help in analytical processing.

The combination of skills, technologies, applications and processes used by organizations to gain data-driven insights. These insights can be used to make strategic business decisions.

A data structure technique optimized for data warehousing tools. It is a logical design that outlines how data is stored in a database to answer business questions quickly and accurately.

The overall management of the availability, usability, integrity, and security of the data employed in an organization. It establishes policies and procedures and plans for managing data assets.

The administration of data that describes other data, with the goal to provide context and meaning. It helps users understand the data source, its structure, uses, and origin.

The act of reviewing source data, understanding its structure, content, and interrelationships, and identifying potential for data projects. This is particularly important for ensuring data quality.