Business intelligence
Business intelligence (BI) consists of strategies and technologies used by enterprises for the
BI tools can handle large amounts of structured and sometimes unstructured data to help organisations to identify, develop, and otherwise create new strategic
Business intelligence can be used by enterprises to support a wide range of business decisions ranging from operational to strategic. Basic operating decisions include
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
History
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:
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.
— Devens, p. 210
The ability to collect and react accordingly based on the information retrieved, Devens says, is central to business intelligence.[6]
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."[7]
In 1989, Howard Dresner (later a
Definition
According to Solomon Negash and Paul Gray, business intelligence (BI) can be defined as systems that combine:
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."[10]
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."[11] 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]
- 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 indicatorsoptimization
- 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
Compared with competitive intelligence
Though the term business intelligence is sometimes a synonym for
Compared with business analytics
Business intelligence and
Unstructured data
Business operations can generate a very large amount of
The management of semi-structured data is an unsolved problem in the information technology industry.[17] 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.[17][18] 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.[16]
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.
Limitations of semi-structured and unstructured data
This section needs to be updated. The reason given is: It's dubious that searchability and semantic analysis are still limitations at the current stage of NLP and AI development.(December 2023) |
There are several challenges to developing BI with semi-structured data. According to Inmon & Nesavich,[19] 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)[19] 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".
Metadata
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.[16][needs independent confirmation] 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.
Applications
Business intelligence can be applied to the following business purposes:
- Performance metrics and benchmarking inform business leaders of progress towards business goals.[20] (Business process management).[citation needed]
- 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]
- OLAP
- BI can facilitate collaboration both inside and outside the business by enabling data sharing and electronic data interchange[20]
- learning management and regulatory compliance.[citation needed]
Roles
Some common technical roles for business intelligence developers are:[22]
- Business analyst
- Data analyst
- Data engineer
- Data scientist
- Database administrator
Risk
In a 2013 report, Gartner categorized business intelligence vendors as either an independent "pure-play" vendor or a consolidated "mega-vendor".[23][non-primary source needed] 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][permanent dead link]
See also
- Analytic applications
- 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
- Agile Business Intelligence
References
- S2CID 30910248.
- ISBN 978-0-470-39240-9.)
- ISBN 978-0-9893086-0-1.
- ^ Chugh, R. & Grandhi, S. (2013,). "Why Business Intelligence? Significance of Business Intelligence tools and integrating BI governance with corporate governance". International Journal of E-Entrepreneurship and Innovation', vol. 4, no.2, pp. 1–14.
- ^
Golden, Bernard (2013). Amazon Web Services For Dummies. John Wiley & Sons. p. 234. ISBN 9781118652268. Retrieved 6 July 2014.
[...] traditional business intelligence or data warehousing tools (the terms are used so interchangeably that they're often referred to as BI/DW) are extremely expensive [...]
- ^ Miller Devens, Richard (1865). 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. D. Appleton and company. p. 210. Retrieved 15 February 2014.
business intelligence.
- ^
doi:10.1147/rd.24.0314. Archived from the original(PDF) on 13 September 2008.
- ^ D. J. Power (10 March 2007). "A Brief History of Decision Support Systems, version 4.0". DSSResources.COM. Retrieved 10 July 2008.
- ^ Power, D. J. "A Brief History of Decision Support Systems". Retrieved 1 November 2010.
- ISBN 978-3-540-48715-9.
- ^ Evelson, Boris (21 November 2008). "Topic Overview: Business Intelligence".
- ^ Evelson, Boris (29 April 2010). "Want to know what Forrester's lead data analysts are thinking about BI and the data domain?". Archived from the original on 6 August 2016. Retrieved 4 November 2010.
- ^ Kobielus, James (30 April 2010). "What's Not BI? Oh, Don't Get Me Started... Oops Too Late... Here Goes..." Archived from the original on 7 May 2010. Retrieved 4 November 2010.
"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.
- ^ "Business Analytics vs Business Intelligence?". timoelliott.com. 9 March 2011. Retrieved 15 June 2014.
- ^ Henschen, Doug (4 January 2010). "Analytics at Work: Q&A with Tom Davenport" (Interview). Archived from the original on 3 April 2012. Retrieved 26 September 2011.
- ^ .
- ^ a b Blumberg, R. & S. Atre (2003). "The Problem with Unstructured Data" (PDF). DM Review: 42–46. Archived from the original (PDF) on 25 January 2011.
- .
- ^ a b Inmon, B. & A. Nesavich, "Unstructured Textual Data in the Organization" from "Managing Unstructured data in the organization", Prentice Hall 2008, pp. 1–13
- ^ ISBN 9781449324681. Retrieved 8 May 2018.
- S2CID 15595226.
- ^ Roles in data - Learn | Microsoft Docs
- ^ Andrew Brust (14 February 2013). "Gartner releases 2013 BI Magic Quadrant". ZDNet. Retrieved 21 August 2013.
- ^ SaaS BI growth will soar in 2010 | Cloud Computing. InfoWorld (1 February 2010). Retrieved 17 January 2012.
Bibliography
- Ralph Kimball et al. "The Data warehouse Lifecycle Toolkit" (2nd ed.) Wiley ISBN 0-470-47957-4
- Peter Rausch, Alaa Sheta, Aladdin Ayesh : Business Intelligence and Performance Management: Theory, Systems, and Industrial Applications, Springer Verlag U.K., 2013, ISBN 978-1-4471-4865-4.
- Munoz, J.M. (2017). Global Business Intelligence. Routledge : UK. ISBN 978-1-1382-03686
- Chaudhuri, Surajit; Dayal, Umeshwar; Narasayya, Vivek (August 2011). "An Overview of Business Intelligence Technology". Communications of the ACM. 54 (8): 88–98. S2CID 13843514.