Here’s an expanded glossary of 30 common BI terms:
Data Warehouse: A central repository for storing large amounts of data from various sources.
Data Mart: A smaller, focused data warehouse that contains a subset of data relevant to a specific business function.
Data Mining: The process of discovering patterns and trends in large data sets.
Data Lake: A storage repository that holds a vast amount of raw data in its native format until it is needed.
ETL (Extract, Transform, Load): The process of extracting data from various sources, transforming it into a usable format, and loading it into a data warehouse or data mart.
OLAP (Online Analytical Processing): A technology that allows users to analyze multidimensional data.
OLTP (Online Transaction Processing): A technology that supports transaction-oriented applications, such as point-of-sale systems.
KPI (Key Performance Indicator): A measurable value that demonstrates how effectively a company is achieving key business objectives.
Dashboard: A visual display of the most important information needed to achieve a specific objective.
Data Visualization: The presentation of data in a graphical format to make it easier to understand.
Self-Service BI: A BI approach that empowers business users to access and analyze data independently.
AI and ML in BI: The application of artificial intelligence and machine learning techniques to automate data analysis and generate insights.
Data Governance: A framework of processes, roles, and policies designed to ensure the effective management of data across an organization.
Data Quality: The accuracy, completeness, consistency, and timeliness of data.
Data Cleansing: The process of identifying and correcting errors in data.
Data Integration: The process of combining data from multiple sources into a unified view.
Data Modeling: The process of creating a conceptual model of a database.
Data Mining: The process of discovering patterns and trends in large data sets.
Predictive Analytics: The use of statistical techniques to predict future outcomes.
Prescriptive Analytics: The use of data and analytics to recommend optimal decisions.
Data Catalog: A centralized repository of information about data assets.
Metadata: Data about data.
Data Lineage: The history of data, from its source to its final destination.
Data Security: The protection of data from unauthorized access, use, disclosure, disruption, modification, or destruction.
Data Privacy: The protection of personal information.
Data Sensitivity: The degree to which data is confidential or critical to an organization.
Data Loss Prevention (DLP): A strategy to prevent sensitive data from being lost or stolen.
Data Masking: The process of obscuring sensitive data to protect privacy.
Data Anonymization: The process of removing personally identifiable information from data.
Data Virtualization: A technology that provides a unified view of data from multiple sources without physically integrating it.