Business intelligence (BI) refers to computer-based techniques used in spotting, digging-out, and analyzing business data, such as sales revenue by products and/or departments or associated costs and incomes. [1]
BI technologies provide historical, current, and predictive views of business operations. Common functions of Business Intelligence technologies are reporting, online analytical processing, analytics, data mining, business performance management, benchmarking, text mining, and predictive analytics.
Business Intelligence often aims to support better business decision-making.[2] Thus a BI system can be called a decision support system (DSS).[3] Though the term business intelligence is often used as a synonym for competitive intelligence, because they both support decision making, BI uses technologies, processes, and applications to analyze mostly internal, structured data and business processes while competitive intelligence, is done by gathering, analyzing and disseminating information with or without support from technology and applications, and focuses on all-source information and data (unstructured or structured), mostly external, but also internal to a company, to support decision making.
In a 1958 article, IBM researcher Hans Peter Luhn used the term business intelligence. He defined intelligence as:[2] "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."[3] It was not until the late 1990s that this usage was widespread.
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.
In order to relate, but also separate the concepts of business intelligence and data warehouses, Forrester Researchoften defines Business Intelligence in one of two ways. Typically, Forreser uses the following broad definition: Business Intelligence is a set of methodologies, processes, architectures, and technologies that transform raw data into meaningful and useful information used to enable more effective strategic, tactical, and operational insights and decision-making. But when using this definition, business intelligence also includes technologies such as data integration, data quality, data warehousing, master data management, text and content analytics, and many others that the market sometimes lumps into the Information Management segment. Therefore, Forrester also refers to the data preparation and data usage, as two separate, but closely linked segments of the business intelligence architectural stack. And Forrester defines the latter, narrower business intelligence market as: A set of methodologies, processes, architectures, and technologies that leverage the output of information management processes for analysis, reporting, performance management, and information delivery.
Thomas Davenport has argued that business intelligence should be divided into querying, reporting, OLAP, an "alerts" tool, and business analytics.
Business Intelligence can be applied to the following business purposes (MARCKM), in order to drive business value:
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.
Although there could be many factors that could affect the implementation process of a BI system, research by Naveen K. Vodapalli [6] shows that the following are the critical success factors for business intelligence implementation:
A 2009 Gartner paper predicted[7] these developments in the business intelligence market.