Business intelligence

The earliest known use of the term business intelligence is in Richard Millar Devens' Cyclopædia of Commercial and Business Anecdotes (1865). Devens used the term to describe how the banker Sir Henry Furnese gained profit by receiving and acting upon information about his environment, prior to his competitors:

{{quote|Throughout Holland, Flanders, France, and Germany, he maintained a complete and perfect train of business intelligence. The news of the many battles fought was thus received first by him, and the fall of Namur added to his profits, owing to his early receipt of the news.|author=Devens |source=p. 210}}

The ability to collect and react accordingly based on the information retrieved, Devens says, is central to business intelligence.{{cite book|last=Miller Devens|first=Richard|title=Cyclopaedia of Commercial and Business Anecdotes; Comprising Interesting Reminiscences and Facts, Remarkable Traits and Humors of Merchants, Traders, Bankers Etc. in All Ages and Countries|url=https://archive.org/details/cyclopaediacomm00devegoog|quote=business intelligence.|publisher=D. Appleton and company|access-date=15 February 2014|page=[https://archive.org/details/cyclopaediacomm00devegoog/page/n262 210]|year=1865}}

When Hans Peter Luhn, a researcher at IBM, used the term business intelligence in an article published in 1958, he employed the Webster's Dictionary definition of intelligence: "the ability to apprehend the interrelationships of presented facts in such a way as to guide action towards a desired goal."

{{cite journal|url= http://www.research.ibm.com/journal/rd/024/ibmrd0204H.pdf|doi=10.1147/rd.24.0314|title= A Business Intelligence System|author=Luhn, H. P. |author-link= Hans Peter Luhn |year= 1958 |journal= IBM Journal of Research and Development|volume= 2|issue= 4|pages= 314–319|archive-url=https://web.archive.org/web/20080913121526/http://www.research.ibm.com/journal/rd/024/ibmrd0204H.pdf|archive-date=2008-09-13}}

In 1989, Howard Dresner (later a Gartner analyst) proposed business intelligence as an umbrella term to describe "concepts and methods to improve business decision making by using fact-based support systems."{{cite web |url= http://dssresources.com/history/dsshistory.html |title= A Brief History of Decision Support Systems, version 4.0 |access-date=10 July 2008 |author= D. J. Power |date= 10 March 2007|publisher= DSSResources.COM }} It was not until the late 1990s that this usage was widespread.{{cite web |url=http://dssresources.com/history/dsshistory.html |title=A Brief History of Decision Support Systems |last=Power |first=D. J. |access-date=1 November 2010 }}

According to Solomon Negash and Paul Gray, business intelligence (BI) can be defined as systems that combine:

• Data gathering
• Data storage
• Knowledge management

with analysis to evaluate complex corporate and competitive information for presentation to planners and decision makers, with the objective of improving the timeliness and the quality of the input to the decision process."{{cite book |title=Topic Overview: Business Intelligence |last=Springer-Verlag Berlin Heidelberg |first=Springer-Verlag Berlin Heidelberg |date=21 November 2008|doi=10.1007/978-3-540-48716-6 |isbn=978-3-540-48715-9 }}

According to Forrester Research, 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."{{cite web |url=https://www.forrester.com/report/Topic+Overview+Business+Intelligence/-/E-RES39218 |title=Topic Overview: Business Intelligence |last=Evelson |first=Boris |date=21 November 2008}} Under this definition, business intelligence encompasses information management (data integration, data quality, data warehousing, master-data management, text- and content-analytics, et al.). Therefore, Forrester refers to data preparation and data usage as two separate but closely linked segments of the business-intelligence architectural stack.

Some elements of business intelligence are:{{citation needed|date=August 2018}}

• Multidimensional aggregation and allocation
• Denormalization, tagging, and standardization
• Realtime reporting with analytical alert
• A method of interfacing with unstructured data sources
• Group consolidation, budgeting, and rolling forecasts
• Statistical inference and probabilistic simulation
• Key performance indicators optimization
• Version control and process management
• Open item management

Forrester distinguishes this from the business-intelligence market, which is "just the top layers of the BI architectural stack, such as reporting, analytics, and dashboards."{{cite web |url=http://blogs.forrester.com/boris_evelson/10-04-29-want_know_what_forresters_lead_data_analysts_are_thinking_about_bi_and_data_domain |title=Want to know what Forrester's lead data analysts are thinking about BI and the data domain? |last=Evelson |first=Boris |date=29 April 2010 |access-date=4 November 2010 |archive-url=https://web.archive.org/web/20160806102752/http://blogs.forrester.com/boris_evelson/10-04-29-want_know_what_forresters_lead_data_analysts_are_thinking_about_bi_and_data_domain |archive-date=6 August 2016 |url-status=dead }}

Though the term business intelligence is sometimes 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 gathers, analyzes, and disseminates information with a topical focus on company competitors. If understood broadly, competitive intelligence can be considered as a subset of business intelligence.{{cite web |url=http://blogs.forrester.com/james_kobielus/10-04-30-what%E2%80%99s_not_bi_oh_don%E2%80%99t_get_me_startedoops_too_latehere_goes |title=What's Not BI? Oh, Don't Get Me Started... Oops Too Late... Here Goes... |last=Kobielus |first=James |date=30 April 2010 |quote="Business" intelligence is a non-domain-specific catchall for all the types of analytic data that can be delivered to users in reports, dashboards, and the like. When you specify the subject domain for this intelligence, then you can refer to "competitive intelligence", "market intelligence", "social intelligence", "financial intelligence", "HR intelligence", "supply chain intelligence", and the like. |access-date=4 November 2010 |archive-url=https://web.archive.org/web/20100507103207/http://blogs.forrester.com/james_kobielus/10-04-30-what%E2%80%99s_not_bi_oh_don%E2%80%99t_get_me_startedoops_too_latehere_goes |archive-date=7 May 2010 |url-status=dead }}

Business intelligence and business analytics are sometimes used interchangeably, but there are alternate definitions.{{cite web|url=http://timoelliott.com/blog/2011/03/business-analytics-vs-business-intelligence.html |title=Business Analytics vs Business Intelligence? |publisher=timoelliott.com |date=2011-03-09 |access-date=2014-06-15}} Thomas Davenport, professor of information technology and management at Babson College argues that business intelligence should be divided into querying, reporting, Online analytical processing (OLAP), an "alerts" tool, and business analytics. In this definition, business analytics is the subset of BI focusing on statistics, prediction, and optimization, rather than the reporting functionality.{{Cite interview |url=http://www.informationweek.com/news/software/bi/222200096 |title=Analytics at Work: Q&A with Tom Davenport |last=Henschen |first=Doug |date=4 January 2010 |access-date=26 September 2011 |archive-date=3 April 2012 |archive-url=https://web.archive.org/web/20120403080949/http://www.informationweek.com/news/software/bi/222200096 |url-status=dead }}

Business operations can generate a very large amount of data in the form of e-mails, memos, notes from call-centers, news, user groups, chats, reports, web-pages, presentations, image-files, video-files, and marketing material. According to Merrill Lynch, more than 85% of all business information exists in these forms; a company might only use such a document a single time.{{cite journal|last1=Rao|first1=R.|year=2003|title=From unstructured data to actionable intelligence|url=http://www.ramanarao.com/papers/rao-itpro-2003-11.pdf|journal=IT Professional|volume=5|issue=6|pages=29–35|doi=10.1109/MITP.2003.1254966}} Because of the way it is produced and stored, this information is either unstructured or semi-structured.

The management of semi-structured data is an unsolved problem in the information technology industry.{{cite journal|author1=Blumberg, R.|author2=S. Atre|name-list-style=amp|year=2003|title=The Problem with Unstructured Data|url=http://soquelgroup.com/Articles/dmreview_0203_problem.pdf|url-status=dead|journal=DM Review|pages=42–46|archive-url=https://web.archive.org/web/20110125033210/http://soquelgroup.com/Articles/dmreview_0203_problem.pdf|archive-date=25 January 2011}} According to projections from Gartner (2003), white-collar workers spend 30–40% of their time searching, finding, and assessing unstructured data. BI uses both structured and unstructured data. The former is easy to search, and the latter contains a large quantity of the information needed for analysis and decision-making.{{cite journal|author=Negash, S|year=2004|title=Business Intelligence|journal=Communications of the Association for Information Systems|volume=13|pages=177–195|doi=10.17705/1CAIS.01315|doi-access=free}} Because of the difficulty of properly searching, finding, and assessing unstructured or semi-structured data, organizations may not draw upon these vast reservoirs of information, which could influence a particular decision, task, or project. This can ultimately lead to poorly informed decision-making.

Therefore, when designing a business intelligence/DW-solution, the specific problems associated with semi-structured and unstructured data must be accommodated for as well as those for the structured data.

{{update|part=section|reason=It's dubious that searchability and semantic analysis are still limitations at the current stage of NLP and AI development|date=December 2023}}

There are several challenges to developing BI with semi-structured data. According to Inmon & Nesavich,Inmon, B. & A. Nesavich, "Unstructured Textual Data in the Organization" from "Managing Unstructured data in the organization", Prentice Hall 2008, pp. 1–13 some of those are:

• Physically accessing unstructured textual data – unstructured data is stored in a huge variety of formats.
• Terminology – Among researchers and analysts, there is a need to develop standardized terminology.
• Volume of data – As stated earlier, up to 85% of all data exists as semi-structured data. Couple that with the need for word-to-word and semantic analysis.
• Searchability of unstructured textual data – A simple search on some data, e.g. apple, results in links where there is a reference to that precise search term. (Inmon & Nesavich, 2008) gives an example: "a search is made on the term felony. In a simple search, the term felony is used, and everywhere there is a reference to felony, a hit to an unstructured document is made. But a simple search is crude. It does not find references to crime, arson, murder, embezzlement, vehicular homicide, and such, even though these crimes are types of felonies".

To solve problems with searchability and assessment of data, it is necessary to know something about the content. This can be done by adding context through the use of metadata.{{Needs independent confirmation|reason=The article is written by a founder of a company that made automatic categorization software. Not sufficient to establish that using automatically generated metadata is a mainstream approach of applying BI to unstructured data.|date=December 2023}} Many systems already capture some metadata (e.g. filename, author, size, etc.), but more useful would be metadata about the actual content – e.g. summaries, topics, people, or companies mentioned. Two technologies designed for generating metadata about content are automatic categorization and information extraction.

Generative business intelligence is the application of generative AI techniques, such as large language models, in business intelligence. This combination facilitates data analysis and enables users to interact with data more intuitively, generating actionable insights through natural language queries. Microsoft Copilot was for example integrated into the business analytics tool Power BI.{{Cite web |last=Novet |first=Jordan |date=2023-05-23 |title=Microsoft is bringing an A.I. chatbot to data analysis |url=https://www.cnbc.com/2023/05/23/microsoft-launches-fabric-including-copilot-for-power-bi.html |access-date=2024-08-19 |website=CNBC |language=en}}

Business intelligence can be applied to the following business purposes:

• Performance metrics and benchmarking inform business leaders of progress towards business goals. (Business process management).{{cn|date=December 2023}}
• Analytics quantify processes for a business to arrive at optimal decisions, and to perform business knowledge discovery. Analytics may variously involve data mining, process mining, statistical analysis, predictive analytics, predictive modeling, business process modeling, data lineage, complex event processing, and prescriptive analytics. For example within banking industry, academic research has explored potential for BI based analytics in credit evaluation, customer churn management for managerial adoption{{cite journal |last1=Moro |first1=Sérgio |last2=Cortez |first2=Paulo |last3=Rita |first3=Paulo |title=Business intelligence in banking: A literature analysis from 2002 to 2013 using text mining and latent Dirichlet allocation |journal=Expert Systems with Applications |date=February 2015 |volume=42 |issue=3 |pages=1314–1324 |doi=10.1016/j.eswa.2014.09.024|hdl=10071/8522 |s2cid=15595226 |hdl-access=free }}
• Reporting, dashboards and data visualization,{{cite book |url=https://books.google.com/books?id=xDXfeopC-kMC&pg=PA140 |title=Developing Business Intelligence Apps for SharePoint |author1=Feldman, D. |author2=Himmelstein, J. |publisher=O'Reilly Media, Inc |pages=140–1 |year=2013 |isbn=9781449324681 |access-date=8 May 2018}} executive information system, and/or OLAP
• BI can facilitate collaboration both inside and outside the business by enabling data sharing and electronic data interchange
• Knowledge management is concerned with the creation, distribution, use, and management of business intelligence, and of business knowledge in general. Knowledge management leads to learning management and regulatory compliance.{{cn|date=December 2023}}

Some common technical roles for business intelligence developers are:[https://docs.microsoft.com/en-us/learn/modules/data-analytics-microsoft/3-roles Roles in data - Learn | Microsoft Docs]

• Business analyst
• Data analyst
• Data engineer
• Data scientist
• Database administrator
• Financial analyst

==Risk==

In a 2013 report, Gartner categorized business intelligence vendors as either an independent "pure-play" vendor or a consolidated "mega-vendor".{{cite news|url=https://www.zdnet.com/article/gartner-releases-2013-bi-magic-quadrant/ |title=Gartner releases 2013 BI Magic Quadrant |work=ZDNet |author=Andrew Brust| date= 2013-02-14|access-date=21 August 2013}}{{Primary source inline|reason=Secondary source supporting importance of the categorization needed|date=December 2023}} In 2019, the BI market was shaken within Europe for the new legislation of GDPR (General Data Protection Regulation) which puts the responsibility of data collection and storage onto the data user with strict laws in place to make sure the data is compliant. Growth within Europe has steadily increased since May 2019 when GDPR was brought. The legislation refocused companies to look at their own data from a compliance perspective but also revealed future opportunities using personalization and external BI providers to increase market share.[https://www.infoworld.com/article/2300051/saas-bi-growth-will-soar-in-2010.html SaaS BI growth will soar in 2010]. InfoWorld (1 February 2010). Retrieved 17 January 2012.

==See also==

{{div col|colwidth=20em}}

• Agile Business Intelligence
• Analytic applications
• Arcplan
• Artificial intelligence marketing
• Business activity monitoring
• Business Intelligence 2.0
• Business Intelligence Competency Center
• Business intelligence software
• Business process discovery
• Business process management
• Customer dynamics
• Decision engineering
• Embedded analytics
• Enterprise planning systems
• Integrated business planning
• Management information system
• Mobile business intelligence
• Operational intelligence
• Process mining
• Real-time business intelligence
• Sales intelligence
• Test and learn

{{div col end}}

{{Reflist|2}}

• {{cite book|first=Ralph |last=Kimball |display-authors=etal |title=The Data warehouse Lifecycle Toolkit" |edition=2nd |publisher=John Wiley & Sons Inc. |year=1998 |isbn=0-470-47957-4 |url=https://archive.org/details/datawarehouselif00kimb |url-access=registration}}
• {{cite book|first1=Peter |last1=Rausch |first2=Alaa |last2=Sheta |first3=Aladdin |last3=Ayesh |title=Business Intelligence and Performance Management: Theory, Systems, and Industrial Applications |publisher=Springer Verlag U.K. |year=2013 |ISBN=978-1-4471-4865-4}}.
• {{cite book| last=Munoz |first=J.M. |year=2017 |title=Global Business Intelligence |publisher=Routledge : UK. |isbn=978-1-1382-03686}}
• {{cite journal |title = An Overview of Business Intelligence Technology |date = August 2011 |first1 = Surajit |last1 = Chaudhuri |first2 = Umeshwar |last2 = Dayal |first3 = Vivek |last3 = Narasayya |journal=Communications of the ACM |volume = 54 |issue= 8 |pages = 88–98 |doi = 10.1145/1978542.1978562 |doi-access = |s2cid = 13843514 }}

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