Business Intelligence (BI) refers to skills, processes, technologies, applications and practices used to support decision making.

BI technologies provide historical, current, and predictive views of business operations. Common functions of Business Intelligence technologies are reporting, OLAP, analytics, data mining, business performance management, benchmarking, text mining, and predictive analytics.

Business Intelligence often aims to support better business decision-making.^[1] Thus a BI system can be called a decision support system (DSS).^[2] The term business intelligence is often used as a synonym for competitive intelligence.

Contents 1 History 2 Business intelligence and data warehousing 3 Business intelligence and business analytics 4 Getting Business Intelligence projects prioritised 5 Critical Success Factors of Business Intelligence Implementation 6 The future of business intelligence 7 See also 8 References

[edit] History

In a 1958 article, IBM researcher Hans Peter Luhn used the term business intelligence. He defined intelligence as:^[1] "the ability to apprehend the interrelationships of presented facts in such a way as to guide action towards a desired goal."

In 1989 Howard Dresner (later a Gartner Group analyst) proposed BI as an umbrella term to describe "concepts and methods to improve business decision making by using fact-based support systems."^[2] It was not until the late 1990s that this usage was widespread.

[edit] Business intelligence and data warehousing

Often BI applications use data gathered from a data warehouse or a data mart. However, not all data warehouses are used for business intelligence nor do all business intelligence applications require a data warehouse.

[edit] Business intelligence and business analytics

Thomas Davenport has argued that business intelligence should be divided into querying, reporting, OLAP, an "alerts" tool, and business analytics

[edit] Getting Business Intelligence projects prioritised

It is often difficult to provide a positive business case for Business Intelligence (BI) initiatives and often the projects will need to be prioritized through strategic initiatives. Here are some hints to increase the benefits for a BI project.
• As described by Kimball^[3] you must determine the tangible benefits such as eliminated cost of producing legacy report
• Enforce access to data for the entire organization. In this way even a small benefit, such as a few minutes saved, will make a difference when it is multiplied with the no of employees in the entire organisation
• As described by Ross, Weil & Roberson for Enterprise Architecture^[4], consider letting the BI project be driven by other business initiatives with excellent business cases. To support this approach, the organisation must have Enterprise Architects, which will be able to detect suitable business projects.

[edit] Critical Success Factors of Business Intelligence Implementation

Although there could be many factors that could affect the implementation process of a BI system, a research by Naveen^[5] shows, the following are the critical success factors for an business intelligence implementation:

a. Business driven methodology & project management

b. Clear vision & planning

c. Committed management support & sponsorship

d. Data management & quality issues

e. Mapping the solutions to the user requirements

f. Performance considerations of the BI system

g. Robust & extensible framework

[edit] The future of business intelligence

A 2009 Gartner paper predicted these developments in business intelligence market .^[6]

• Because of lack of information, processes, and tools, through 2012, more than 35 percent of the top 5,000 global companies will regularly fail to make insightful decisions about significant changes in their business and markets.
• By 2012, business units will control at least 40 percent of the total budget for business intelligence.
• By 2010, 20 per cent of organizations will have an industry-specific analytic application delivered via software as a service as a standard component of their business intelligence portfolio.
• In 2009, collaborative decision making will emerge as a new product category that combines social software with business intelligence platform capabilities.
• By 2012, one-third of analytic applications applied to business processes will be delivered through coarse-grained application mashups.

[edit] See also

• Data mining
• Accounting Intelligence
• Analytics
• Analytic Applications
• Artificial Intelligence Marketing
• Business Intelligence 2.0
• Business intelligence tools
• Business Process Discovery
• Business Service Management
• Dashboards (management information systems)
• Document Intelligence
• Integrated business planning
• Location intelligence
• Meteorological intelligence
• Online analytical processing
• Operational Intelligence
• Predictive analytics
• Process mining
• Runtime Intelligence
• Sales intelligence
• Test and Learn

[edit] References