Brief History And Definition Of Business Intelligence

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Brief History and Definition of Business Intelligence The first use of the term “business intelligence” was found in Richard Millar Devens’ 1865 work, Cyclopaedia of Commercial and Business Anecdotes. He used it to describe the understanding banker Sir Henry Furnese had of political issues, instabilities, and the market before his competitors. It was not until 1958 that IBM computer scientist Hans Peter Luhn recognized the potential of business intelligence. Since then, Business Intelligence has continuously evolved from when it started as an automated system to disseminate information to the various sections of any industrial, scientific, or government organization (BetterBuys).
The market for Business Intelligence and analytics
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The addition of a BI platform to an organizations existing software can greatly improve the functionality of those programs. In Gartner’s “Magic Quadrant for Business Intelligence and Analytics Platform” article, they define the main uses of BI platforms as:
1. Centralized BI Provisioning: Supports a workflow from data to IT-delivered-and-managed content.
2. Decentralized Analytics: Supports a workflow from data to self-service analytics.
3. Governed Data Discovery: Supports a workflow from data to self-service analytics to systems-of-record, IT-managed content with governance, reusability and promotability.
4. OEM/Embedded BI: Supports a workflow from data to embedded BI content in a process or application. (Gartner)
These main uses are then broken down more into more categories and sub-categories of capabilities. Some of the more interesting categories are mobile, analytic dashboards and content, collaboration and social integration, free-form interactive exploration, and cloud deployment (Gartner). These specific functions and capabilities of BI are partly responsible for shifting the market of BI from IT-centric to user friendly for anyone. The capabilities that come with cloud deployment allow users to connect to both the cloud and on-premises data source at the same time. Cloud deployment has
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