Business intelligence

Business intelligence

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Business intelligence (BI) refers to skills, knowledge, technologies, applications, quality, risks, security issues and practices used to help a business to acquire a better understanding of market behavior and commercial context. For this purpose it undertakes the collection, integration, analysis, interpretation and presentation of business information. By extension, "business intelligence" may refer to the collected information itself or the explicit knowledge developed from the information.
BI applications provide historical, current, and predictive views of business operations, most often using data already gathered into a data warehouse or a data mart and occasionally working from operational data. Software elements support the use of this information by assisting in the extraction, analysis, and reporting of information. Common functionality of business intelligence applications includes reporting, OLAP, analytics, dashboards, scorecards, data mining, corporate performance management (CPM), and predictive analysis.
BI applications tackle sales, production, financial, and many other sources of business data for purposes that include, notably, business performance management. BI operatives may gather information on comparable companies to produce benchmarks.
Business intelligence — the term dates at least to 1958 — aims to support better business decision-making.[1] Thus one can also characterize a BI system as a decision support system (DSS):[2] BI is sometimes used interchangeably with briefing books, report and query tools and executive information systems. In general, business intelligence systems are data-driven DSS.
Contents[hide]
1 History
2 Key intelligence topics
3 See also
4 References

History
Prior to the start of the Information Age in the late 20th century, businesses had to collect data from non-automated sources. Businesses then lacked the computing resources necessary to properly analyze the data, and as a result, companies often made business decisions primarily on the basis of intuition.
As businesses automated systems the amount of data increased but its collection remained difficult due to difficulties in moving information between or within systems. Analysis of information improved for long-term decision making, but was slow and often required the use of instinct or expertise to make short-term decisions. In 1958 Hans Peter Luhn defined business intelligence:[1]
In this paper, business is a collection of activities carried on for whatever purpose, be it science, technology, commerce, industry, law, government, defense, et cetera. The communication facility serving the conduct of a business (in the broad sense) may be referred to as an intelligence system. The notion of intelligence is also defined here, in a more general sense, as "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) popularized BI as an umbrella term to describe "concepts and methods to improve business decision making by using fact-based support systems."[2] In modern businesses the use of standards, automation and specialized software, including analytical tools, allows large volumes of data to be extracted, transformed, loaded and warehoused to greatly increase the speed at which information becomes available for decision-making.

[edit] Key intelligence topics
Business intelligence often uses key performance indicators (KPIs) to assess the present state of business and to prescribe a course of action. Examples of KPIs include lead conversion rate (in sales) and inventory turnover (in inventory management). Prior to the widespread adoption of computer and web applications, when information had to be manually input and calculated, performance data was often not available for weeks or months. Recently[update], banks have tried to make data available at shorter intervals and have reduced delays. The KPI methodology was further expanded with the Chief Performance Officer methodology which incorporated KPIs and root cause analysis into a single methodology.
Businesses that face higher operational/credit risk loading, such as credit card companies and "wealth management" services, often make KPI-related data available weekly. In some cases, companies may even offer a daily analysis of data. This fast pace requires analysts to use IT systems to process large volumes of data.