Business intelligence

Business Intelligence

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Engine
Type	Business software

Business intelligence (BI) refers to the technologies, applications, strategies, and practices used to collect, integrate, analyze, and present business information. The purpose of BI is to support better business decision-making. Essentially, Business Intelligence systems are data-driven Decision Support Systems (DSS). BI is sometimes used interchangeably with briefing books, report and query tools, and executive information systems.

Overview

With the aim of improving the quality of decisions, BI uses both structured data and unstructured data. Business intelligence encompasses a wide variety of tools, applications, and methodologies that enable organizations to collect data from internal systems and external sources, prepare it for analysis, develop and run queries against the data, and create reports, dashboards, and data visualizations to make the analytical results available to corporate decision-makers as well as operational workers.

History

The term "Business Intelligence" was first used in 1865 by Richard Millar Devens in his Cyclopædia of Commercial and Business Anecdotes. The modern BI tools evolved from the 1960s Decision Support Systems (DSS), which in turn led to the development of executive information systems, data warehouses, OLAP (On-Line Analytical Processing), and business analytics.

Technologies

BI technologies provide historical, current, and predictive views of business operations. Common functions of business intelligence technologies include reporting, online analytical processing, analytics, data mining, process mining, complex event processing, business performance management, benchmarking, text mining, predictive analytics, and prescriptive analytics. BI tools include SAP BusinessObjects, Oracle BI, and IBM Cognos, among others.

Applications

BI can be applied in different areas of a business, including financial analysis, marketing, sales, and human resources. BI technologies can handle large amounts of unstructured data to help identify, develop, and otherwise create new strategic business opportunities.

Challenges

Implementing BI systems comes with several challenges, such as the cost of the tools, the complexity of implementation, and the need for continuous maintenance and updates. Additionally, data quality and the integration of data from various sources remain significant challenges.

Future Trends

The future of BI is likely to be influenced by the increasing use of artificial intelligence and machine learning technologies, which can automate the analysis of data and provide more insightful predictions and prescriptive actions.

See also

• Data analysis
• Data mining
• Decision support system
• Predictive analytics