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Business intelligence

Business intelligence (BI) consists of strategies and technologies used by enterprises for the data analysis and management of business information.[1] Common functions of BI technologies include reporting, online analytical processing, analytics, dashboard development, data mining, process mining, complex event processing, business performance management, benchmarking, text mining, predictive analytics, and prescriptive analytics.

BI tools can handle large amounts of structured and sometimes unstructured data to help organisations to identify, develop, and otherwise create new strategic business opportunities. They aim to allow for the easy interpretation of these big data. Identifying new opportunities and implementing an effective strategy based on insights can provide businesses with a competitive market advantage and long-term stability, and help them take strategic decisions.[2]


Business intelligence can be used by enterprises to support a wide range of business decisions ranging from operational to strategic. Basic operating decisions include product positioning or pricing. Strategic business decisions involve priorities, goals, and directions at the broadest level. In all cases, BI is most effective when it combines data derived from the market in which a company operates (external data) with data from company sources internal to the business such as financial and operations data (internal data). When combined, external and internal data can provide a complete picture which, in effect, creates an "intelligence" that cannot be derived from any singular set of data.[3]


Among myriad uses, business intelligence tools empower organizations to gain insight into new markets, to assess demand and suitability of products and services for different market segments, and to gauge the impact of marketing efforts.[4]


BI applications use data gathered from a data warehouse (DW) or from a data mart, and the concepts of BI and DW combine as "BI/DW"[5] or as "BIDW". A data warehouse contains a copy of analytical data that facilitates decision support.

Data gathering

Data storage

Knowledge management

Physically accessing unstructured textual data – unstructured data is stored in a huge variety of formats.

 – Among researchers and analysts, there is a need to develop standardized terminology.

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".

[19]

and benchmarking inform business leaders of progress towards business goals.[20] (Business process management).

Performance metrics

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[21]

Analytics

dashboards and data visualization,[20] executive information system, and/or OLAP

Reporting

BI can facilitate both inside and outside the business by enabling data sharing and electronic data interchange[20]

collaboration

is concerned with the creation, distribution, use, and management of business intelligence, and of business knowledge in general.[20] Knowledge management leads to learning management and regulatory compliance.

Knowledge management

Business intelligence can be applied to the following business purposes:

Business analyst

Data analyst

Data engineer

Data scientist

Database administrator

Some common technical roles for business intelligence developers are:[22]

Risk[edit]

In a 2013 report, Gartner categorized business intelligence vendors as either an independent "pure-play" vendor or a consolidated "mega-vendor".[23] 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.[24]

Ralph Kimball et al. "The Data warehouse Lifecycle Toolkit" (2nd ed.) Wiley  0-470-47957-4

ISBN

Peter Rausch, Alaa Sheta, Aladdin Ayesh : Business Intelligence and Performance Management: Theory, Systems, and Industrial Applications, Springer Verlag U.K., 2013,  978-1-4471-4865-4.

ISBN

Munoz, J.M. (2017). Global Business Intelligence. Routledge : UK.  978-1-1382-03686

ISBN

Chaudhuri, Surajit; Dayal, Umeshwar; Narasayya, Vivek (August 2011). "An Overview of Business Intelligence Technology". Communications of the ACM. 54 (8): 88–98. :10.1145/1978542.1978562. S2CID 13843514.

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