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| ==BI technologies== | ==BI technologies== | ||
| For a BI technology system to work effectively, a company should have a secure computer system which can specify different levels of user access to the data 'warehouse,' depending on whether the user is a junior staffer, a manager, or an executive. As well, a BI system should have sufficient data capacity and a plan for how long data will be stored (data retention). Analysts should set benchmark and performance targets for the system. | For a ''BI technology'' system to work effectively, a company should have a secure computer system which can specify different levels of user access to the data 'warehouse,' depending on whether the user is a junior staffer, a manager, or an executive. As well, a BI system should have sufficient data capacity and a plan for how long data will be stored (data retention). Analysts should set benchmark and performance targets for the system. | ||
| Business intelligence analysts have developed software tools to gather and analyze large quantities of unstructured ], such as production metrics, sales statistics, attendance reports, and customer attrition figures. Each BI ] typically develops Business intelligence systems differently, to suit the demands of different sectors (e.g., retail companies, financial services companies, etc.). | Business intelligence analysts have developed software tools to gather and analyze large quantities of unstructured ], such as production metrics, sales statistics, attendance reports, and customer attrition figures. Each BI ] typically develops Business intelligence systems differently, to suit the demands of different sectors (e.g., retail companies, financial services companies, etc.). | ||
Revision as of 10:35, 29 November 2007
The term business intelligence (BI) dates to 1958. It refers to technologies, applications, and practices for the collection, integration, analysis, and presentation of business information and also sometimes to the information itself. The purpose of business intelligence is to support better business decision making. D. J. Power explains in "A Brief History of Decision Support Systems,"
BI describes a set of concepts and methods to improve business decision making by using fact-based support systems. BI is sometimes used interchangeably with briefing books, report and query tools and executive information systems. Business Intelligence systems are data-driven DSS.
BI systems provide historical, current, and predictive views of business operations, most often using data that has been gathered into a data warehouse or a data mart and occasionally working from operational data. Software elements support reporting, interactive "slice-and-dice" pivot-table analyses, visualization, and statistical data mining. Applications tackle sales, production, financial, and many other sources of business data for purposes that include, notably, business performance management.
BI technologies
For a BI technology system to work effectively, a company should have a secure computer system which can specify different levels of user access to the data 'warehouse,' depending on whether the user is a junior staffer, a manager, or an executive. As well, a BI system should have sufficient data capacity and a plan for how long data will be stored (data retention). Analysts should set benchmark and performance targets for the system.
Business intelligence analysts have developed software tools to gather and analyze large quantities of unstructured data, such as production metrics, sales statistics, attendance reports, and customer attrition figures. Each BI vendor typically develops Business intelligence systems differently, to suit the demands of different sectors (e.g., retail companies, financial services companies, etc.).
Business intelligence software and applications include a range of tools. Some BI applications are used to analyze performance, projects, or internal operations, such as AQL - Associative Query Logic, Scorecarding, Business activity monitoring, Business Performance Management and Performance Measurement, Business Planning, Business Process Re-engineering, Competitive Analysis, User/End-user Query and Reporting, Enterprise Management systems, Executive Information Systems (EIS), Supply Chain Management/Demand Chain Management, and Finance and Budgeting tools.
Other BI technologies are used to store and analyze data, such as Data mining (DM), Data Farming, and Data warehouses; Decision Support Systems (DSS) and Forecasting; Document warehouses and Document Management; Knowledge Management; Mapping, Information visualization, and Dashboarding; Management Information Systems (MIS); Geographic Information Systems (GIS); Trend Analysis; Software as a service (SaaS) Business Intelligence offerings (On Demand) — which is similar to traditional BI solutions, but software is hosted for customers by a provider; Online analytical processing (OLAP) and multidimensional analysis, sometimes called "Analytics" (based on the "hypercube" or "cube"); Real time business intelligence; Statistics and Technical Data Analysis; Web Mining; Text mining; and Systems intelligence.
Other BI applications are used to analyze or manage the "human" side of businesses, such as Customer Relationship Management (CRM) and Marketing tools and Human Resources applications.
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 to properly analyze the data, and as a result, companies often made business decisions primarily on the basis of intuition.
As businesses started automating more and more systems, more and more data became available. However, collection remained a challenge due to a lack of infrastructure for data exchange or to incompatibilities between systems. Analysis of the data that was gathered and reports on the data sometimes took months to generate. Such reports allowed informed long-term strategic decision-making. However, short-term tactical decision-making continued to rely on intuition.
Thus we have business intelligence, a term and a definition that date to a seminal October 1958 IBM Journal article by Hans Peter Luhn titled A Business Intelligence System. Luhn wrote,
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 modern businesses, increasing standards, automation, and technologies have led to vast amounts of data becoming available. Data warehouse technologies have set up repositories to store these data. Improved Extract, transform, load (ETL) and even recently Enterprise Application Integration tools have increased the speed of collecting the data. OLAP reporting technologies have allowed faster generation of new reports which analyze the data. Business intelligence has now become the art of sifting through large amounts of data, extracting pertinent information, and turning that information into knowledge from which actions can be taken.
Business intelligence software incorporates the ability to mine data, analyze, and report. Some modern BI software allows users to cross-analyze and perform deep data research rapidly for better analysis of sales or performance on an individual, department, or company level. In modern applications of business intelligence software, managers are able to quickly compile reports from data for forecasting, analysis, and business decision-making.
In 1989 Howard Dresner, later a Gartner Group analyst, popularized BI as an umbrella term to describe a set of concepts and methods to improve business decision-making by using fact-based decision support systems.
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 are things such as 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 inputted and calculated, performance data was often not available for weeks or months. Recently, 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 this large volume of data.
Trends
Currently organizations are staring to see that data and content should not be considered separate aspects of information management, but instead should be managed in an integrated enterprise approach. Enterprise information management brings Business Intelligence and Enterprise Content Management together. Interesting signs in this direction are recent acquisitions by SAP and IBM in the Business Intelligence Area. IBM for example earlier bought a market-leading ECM vendor.
See also
- Analytics
- Business intelligence tools
- Collective intelligence
- Competitive intelligence
- Digital dashboard
- List of management topics
- Location intelligence
- Online analytical processing
- OODA Loop
- OLAP
- Predictive analytics
- Business Intelligence 2.0 (BI 2.0)
- Spreadsheet
References
- ^ http://www.research.ibm.com/journal/rd/024/ibmrd0204H.pdf
- http://dssresources.com/history/dsshistoryv28.html
- Industry Analyst Think Strategies & it's SaaS Showplace
- http://dssresources.com/history/dsshistoryv28.html