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Difference between Business Intelligence and Data Warehouse

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Business Intelligence: Large business organizations usually receive large amounts of data from various sources. This data is always exploitable to obtain diverse sets of information that help in making better business decisions. These actionable insights may be descriptive, predictive, or prescriptive. BI represents the various methods and tools used for the collection, integration, analysis and visualization of business information. It could be considered synonymous with data analytics in particular to the business world.

Data Warehouse: Data Warehouse is a system and set of technologies at the back-end, that helps in collecting large amounts of dissimilar data from various sources and storing them for later use. Good data warehouses have business meaning backed into them facilitating future extraction and analysis. Business Intelligence is one of the applications that make use of data warehouses. Data Warehouses generally follow a multidimensional paradigm (related to OLAP) where data is held in Fact Tables (tables covering numbers such as revenue or costs) and Dimensions (things we want to view the facts by, such as region, office, or week). Business-Intelligence-vs-Data-Warehouse Below is a table of differences between Business Intelligence and Data Warehouse: 

Business Intelligence Data Warehouse
It is a set of tools and methods to analyze data and discover, extract and formulate actionable information that would be useful for business decisions. It is a system for storage of data from various sources in an orderly manner as to facilitate business-minded reads and writes.
It is a Decision Support System (DSS). It is a data storage system.
Serves at the front end. Serves at the back end.
The aim of business intelligence is to enable users to make informed, data-driven decisions. A data warehouse’s main aim is to provide the users of business intelligence; a structured and comprehensive view of available data of an organization.
Collects data from the data warehouse for analysis. Collects data from various disparate sources and organizes it for efficient BI analysis.
Comprises business reports, charts, graphs, etc. Comprises of data held in “fact tables” and “dimensions” with business meaning incorporated into them.
BI as such doesn’t have much use without a data warehouse as large amounts of various and useful data is required for analysis. BI is one of many use-cases for data warehouses, there are more applications for this system.
Handled by executives and analysts relatively higher up in the hierarchy. Handled and maintained by data engineers and system administrators who report to/work for the executives and analysts.
The role of Business Intelligence lies in improving the performance of business by utilizing tools and approaches that focus on counts, statistics, and visualization. The reflection of actual database development and integration process is given by Data Warehouse and in addition, Data Profiling and Company validation standards. 

It deals with- 

  • OLAP (Online Analytical Processing)
  • Data Visualization
  • Data Mining
  • Query/Reporting Tools

It deals with- 

  • Acquiring/gathering of data
  • Metadata management
  • Cleaning of data
  • Transforming data
  • Data dissemination
  • Data recovery/backup planning
Examples of BI software: SAP, Sisense, Datapine, Looker, etc. Examples of Data warehouse software: BigQuery, Snowflake, Amazon, Redshift, Panoply, etc.
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Last Updated : 01 Dec, 2022
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