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Business intelligence
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Business Intelligence (BI) comprises the strategies and technologies used by enterprises for the data analysis of business information.[1] BI technologies provide historical, current and predictive views of business operations. Common functions of business intelligence technologies include reporting, online analytical processing, analytics, data mining, process mining, complex event processing, business performance management, benchmarking, text mining, predictive analytics and prescriptive analytics. BI technologies can handle large amounts of structured and sometimes unstructured data to help 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.[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 more complete picture which, in effect, creates an "intelligence" that cannot be derived by any singular set of data.[3] Amongst 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]
Often[quantify] 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.
Contents
[hide]
1 Components
2 History
2.1 Data discovery
3 Data warehousing
4 Comparison with competitive intelligence
5 Comparison with business analytics
6 Applications in an enterprise
7 Prioritization of projects
8 Success factors of implementation
8.1 Business sponsorship
8.2 Business needs
8.3 Amount and quality of available data
9 User aspect
10 BI Portals
11 Marketplace
11.1 Industry-specific
12 Semi-structured or unstructured data
12.1 Unstructured data vs. semi-structured data
12.2 Problems with semi-structured or unstructured data
12.3 The use of metadata
13 2009 predictions
14 See also
15 References
16 Bibliography
17 External links
Components[edit]
Business intelligence is made up of an increasing number of components including:
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
History[edit]
The earliest known use of the term "Business Intelligence" is in Richard Millar Devens’ in the ‘Cyclopædia of Commercial and Business Anecdotes’ from 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, (1865), p. 210). The ability to collect and react accordingly based on the information retrieved, an ability that Furnese excelled in, is today still at the very heart of BI.[6]
In a 1958 article, IBM researcher Hans Peter Luhn used the term business intelligence. 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]
Business intelligence as it is understood today is said to have evolved from the decision support systems (DSS) that began in the 1960s and developed throughout the mid-1980s. DSS originated in the computer-aided models created to assist with decision making and planning. From DSS, data warehouses, Executive Information Systems, OLAP and business intelligence came into focus beginning in the late 80s.
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."[8] It was not until the late 1990s that this usage was widespread.[9]
Critics see BI as evolved from mere business reporting together with the advent of increasingly powerful and easy-to-use data analysis tools. In this respect it has also been criticized as a marketing buzzword in the context of the "big data" surge.[10]
Data discovery[edit]
Data discovery is a buzzword in BI for creating and using interactive reports and exploring data from multiple sources. The market research firm Gartner promoted it in 2012.[11]
Data discovery is a user-driven process of searching for patterns or specific items in a data set. Data discovery applications use visual tools such as geographical maps, pivot tables, and heat maps to make the process of finding patterns or specific items rapid and intuitive. Statistical and data mining techniques can be employed to accomplish these goals.
Data discovery is a type of business intelligence in that they both provide the end-user with an application that visualizes data using dashboards, static and parameterized reports, and pivot tables. Visualization of data in traditional BI incorporated standard charting, key performance indicators, and limited graphical representation and interactivity. BI is undergoing transformation in capabilities it offers, with a focus on end-user data analysis and discovery, access to larger volumes of data and an ability to create high fidelity presentations of information.
Data warehousing[edit]
To distinguish between the concepts of business intelligence and data warehouses, Forrester Research defines business intelligence in one of two ways:
Using a broad definition: "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."[12] Under this definition, business intelligence also includes technologies such as data integration, data quality, data warehousing, master-data management, text- and content-analytics, and many others that the market sometimes lumps into the "Information Management" segment. Therefore, Forrester refers to data preparation and data usage as two separate but closely linked segments of the business-intelligence architectural stack.
Forrester defines the narrower business-intelligence market as, "...referring to just the top layers of the BI architectural stack such as reporting, analytics and dashboards."[13]
Comparison with competitive intelligence[edit]
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, business intelligence can include the subset of competitive intelligence.[14]
Comparison with business analytics[edit]
Business intelligence and business analytics are sometimes used interchangeably, but there are alternate definitions.[15] One definition contrasts the two, stating that the term business intelligence refers to collecting business data to find information primarily through asking questions, reporting, and online analytical processes. Business analytics, on the other hand, uses statistical and quantitative tools for explanatory and predictive modelling.[16]
In an alternate definition, 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.[17]
Applications in an enterprise[edit]
Business intelligence can be applied to the following business purposes, in order to drive business value.[citation needed]
Measurement – program that creates a hierarchy of performance metrics (see also Metrics Reference Model) and benchmarking that informs business leaders about progress towards business goals (business process management).
Analytics – program that builds quantitative processes for a business to arrive at optimal decisions and to perform business knowledge discovery. Frequently involves: data mining, process mining, statistical analysis, predictive analytics, predictive modeling, business process modeling, data lineage, complex event processing and prescriptive analytics.
Reporting/enterprise reporting – program that builds infrastructure for strategic reporting to serve the strategic management of a business, not operational reporting. Frequently involves data visualization, executive information system and OLAP.
Collaboration/collaboration platform – program that gets different areas (both inside and outside the business) to work together through data sharing and electronic data interchange.
Knowledge management – program to make the company data-driven through strategies and practices to identify, create, represent, distribute, and enable adoption of insights and experiences that are true business knowledge. Knowledge management leads to learning management and regulatory compliance.
In addition to the above, business intelligence can provide a pro-active approach, such as alert functionality that immediately notifies the end-user if certain conditions are met. For example, if some business metric exceeds a pre-defined threshold, the metric will be highlighted in standard reports, and the business analyst may be alerted via e-mail or another monitoring service. This end-to-end process requires data governance, which should be handled by the expert.[citation needed]
Prioritization of projects[edit]
It can be difficult to provide a positive business case for business intelligence initiatives, and often the projects must be prioritized through strategic initiatives. BI projects can attain higher prioritization within the organization if managers consider the following:
As described by Kimball, the BI manager must determine the tangible benefits such as eliminated cost of producing legacy reports.[18]
Data access for the entire organization must be enforced.[19] In this way even a small benefit, such as a few minutes saved, makes a difference when multiplied by the number of employees in the entire organization.
As described by Ross, Weil & Roberson for Enterprise Architecture,[20] managers should also consider letting the BI project be driven by other business initiatives with excellent business cases. To support this approach, the organization must have enterprise architects who can identify suitable business projects.
Using a structured and quantitative methodology to create defensible prioritization in line with the actual needs of the organization, such as a weighted decision matrix.[21]
Success factors of implementation[edit]
According to Kimball et al., there are three critical areas that organizations should assess before getting ready to do a BI project:[22]
The level of commitment and sponsorship of the project from senior management.
The level of business need for creating a BI implementation.
The amount and quality of business data available.
Business sponsorship[edit]
The commitment and sponsorship of senior management is according to Kimball et al., the most important criteria for assessment.[23] This is because having strong management backing helps overcome shortcomings elsewhere in the project. However, as Kimball et al. state: “even the most elegantly designed DW/BI system cannot overcome a lack of business [management] sponsorship”.[24]
It is important that personnel who participate in the project have a vision and an idea of the benefits and drawbacks of implementing a BI system. The best business sponsor should have organizational clout and should be well connected within the organization. It is ideal that the business sponsor is demanding but also able to be realistic and supportive if the implementation runs into delays or drawbacks. The management sponsor also needs to be able to assume accountability and to take responsibility for failures and setbacks on the project. Support from multiple members of the management ensures the project does not fail if one person leaves the steering group. However, having many managers work together on the project can also mean that there are several different interests that attempt to pull the project in different directions, such as if different departments want to put more emphasis on their usage. This issue can be countered by an early and specific analysis of the business areas that benefit the most from the implementation. All stakeholders in the project should participate in this analysis in order for them to feel invested in the project and to find common ground.
Another management problem that may be encountered before the start of an implementation is an overly aggressive business sponsor. Problems of scope creep occur when the sponsor requests data sets that were not specified in the original planning phase.
Business needs[edit]
Because of the close relationship with senior management, another critical thing that must be assessed before the project begins is whether or not there is a business need and whether there is a clear business benefit by doing the implementation.[25] The needs and benefits of the implementation are sometimes driven by competition and the need to gain an advantage in the market. Another reason for a business-driven approach to implementation of BI is the acquisition of other organizations that enlarge the original organization it can sometimes be beneficial to implement DW or BI in order to create more oversight.
Companies that implement BI are often large, multinational organizations with diverse subsidiaries.[26] They may go through the implementation of a Business Intelligence Competency Center (BICC).
A well-designed BI solution provides a consolidated view of key business data not available anywhere else in the organization, giving management visibility and control over measures that otherwise would not exist.
Amount and quality of available data[edit]
Without proper data, or with too little quality data, any BI implementation fails; it does not matter how good the management sponsorship or business-driven motivation is. Before implementation it is a good idea to do data profiling. This analysis identifies the “content, consistency and structure [..]”[25] of the data. This should be done as early as possible in the process and if the analysis shows that data is lacking, put the project on hold temporarily while the IT department figures out how to properly collect data.
When planning for business data and business intelligence requirements, it is always advisable to consider specific scenarios that apply to a particular organization, and then select the business intelligence features best suited for the scenario.
Often, scenarios revolve around distinct business processes, each built on one or more data sources. These sources are used by features that present that data as information to knowledge workers, who subsequently act on that information. The business needs of the organization for each business process adopted correspond to the essential steps of business intelligence. These essential steps of business intelligence include but are not limited to:
Go through business data sources in order to collect needed data
Convert business data to information and present appropriately
Query and analyze data
Act on the collected data
The quality aspect in business intelligence should cover all the process from the source data to the final reporting. At each step, the quality gates are different:
Source Data:
Data Standardization: make data comparable (same unit, same pattern...)
Master Data Management: unique referential
Operational Data Store (ODS):
Data Cleansing: detect & correct inaccurate data
Data Profiling: check inappropriate value, null/empty
Data warehouse:
Completeness: check that all expected data are loaded
Referential integrity: unique and existing referential over all sources
Consistency between sources: check consolidated data vs sources
Reporting:
Uniqueness of indicators: only one share dictionary of indicators
Formula accuracy: local reporting formula should be avoided or checked
User aspect[edit]
Some considerations must be made in order to successfully integrate the usage of business intelligence systems in a company. Ultimately the BI system must be accepted and utilized by the users in order for it to add value to the organization.[27][28] If the usability of the system is poor, the users may become frustrated and spend a considerable amount of time figuring out how to use the system or may not be able to be productive. If the system does not add value to the users´ mission, they simply don't use it.[28]
To increase user acceptance of a BI system, it can be advisable to consult business users at an early stage of the DW/BI lifecycle, for example at the requirements gathering phase.[27] This can provide an insight into the business process and what the users need from the BI system. There are several methods for gathering this information, such as questionnaires and interview sessions.
When gathering the requirements from the business users, the local IT department should also be consulted in order to determine to which degree it is possible to fulfill the business's needs based on the available data.[27]
Taking a user-centered approach throughout the design and development stage may further increase the chance of rapid user adoption of the BI system.[28]
Besides focusing on the user experience offered by the BI applications, it may also possibly motivate the users to utilize the system by adding an element of competition. Kimball[27] suggests implementing a function on the Business Intelligence portal website where reports on system usage can be found. By doing so, managers can see how well their departments are doing and compare themselves to others and this may spur them to encourage their staff to utilize the BI system even more.
In a 2007 article, H. J. Watson gives an example of how the competitive element can act as an incentive.[29] Watson describes how a large call centre implemented performance dashboards for all call agents, with monthly incentive bonuses tied to performance metrics. Also, agents could compare their performance to other team members. The implementation of this type of performance measurement and competition significantly improved agent performance.
BI chances of success can be improved by involving senior management to help make BI a part of the organizational culture, and by providing the users with necessary tools, training, and support.[29] Training encourages more people to use the BI application.[27]
Providing user support is necessary to maintain the BI system and resolve user problems.[28] User support can be incorporated in many ways, for example by creating a website. The website should contain great content and tools for finding the necessary information. Furthermore, helpdesk support can be used. The help desk can be manned by power users or the DW/BI project team.[27]
BI Portals[edit]
A Business Intelligence portal (BI portal) is the primary access interface for Data Warehouse (DW) and Business Intelligence (BI) applications. The BI portal is the user's first impression of the DW/BI system. It is typically a browser application, from which the user has access to all the individual services of the DW/BI system, reports and other analytical functionality. The BI portal must be implemented in such a way that it is easy for the users of the DW/BI application to call on the functionality of the application.[30]
The BI portal's main functionality is to provide a navigation system of the DW/BI application. This means that the portal has to be implemented in a way that the user has access to all the functions of the DW/BI application.
The most common way to design the portal is to custom fit it to the business processes of the organization for which the DW/BI application is designed, in that way the portal can best fit the needs and requirements of its users.[31]
The BI portal needs to be easy to use and understand, and if possible have a look and feel similar to other applications or web content of the organization the DW/BI application is designed for (consistency).
The following is a list of desirable features for web portals in general and BI portals in particular:
Usable
User should easily find what they need in the BI tool.
Content Rich
The portal is not just a report printing tool, it should contain more functionality such as advice, help, support information and documentation.
Clean
The portal should be designed so it is easily understandable and not over-complex as to confuse the users
Current
The portal should be updated regularly.
Interactive
The portal should be implemented in a way that makes it easy for the user to use its functionality and encourage them to use the portal. Scalability and customization give the user the means to fit the portal to each user.
Value Oriented
It is important that the user has the feeling that the DW/BI application is a valuable resource that is worth working on.
Marketplace[edit]
There are a number of business intelligence vendors, often categorized into the remaining independent "pure-play" vendors and consolidated "megavendors" that have entered the market through a recent trend[32] of acquisitions in the BI industry.[33] The business intelligence market is gradually growing. In 2012 business intelligence services brought in $13.1 billion in revenue.[34]
Some companies adopting BI software decide to pick and choose from different product offerings (best-of-breed) rather than purchase one comprehensive integrated solution (full-service).[35]
Industry-specific[edit]
Specific considerations for business intelligence systems have to be taken in some sectors such as governmental banking regulations or healthcare.[36] The information collected by banking institutions and analyzed with BI software must be protected from some groups or individuals, while being fully available to other groups or individuals. Therefore, BI solutions must be sensitive to those needs and be flexible enough to adapt to new regulations and changes to existing law.[citation needed]
Semi-structured or unstructured data[edit]
Businesses create a huge amount of valuable information 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 and news. According to Merrill Lynch, more than 85% of all business information exists in these forms. These information types are called either semi-structured or unstructured data. However, organizations often only use these documents once.[37]
The managements of semi-structured data is recognized as a major unsolved problem in the information technology industry.[38] According to projections from Gartner (2003), white collar workers spend anywhere from 30 to 40 percent of their time searching, finding and assessing unstructured data. BI uses both structured and unstructured data, but the former is easy to search, and the latter contains a large quantity of the information needed for analysis and decision making.[38][39] 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.[37]
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.[39]
Unstructured data vs. semi-structured data[edit]
Unstructured and semi-structured data have different meanings depending on their context. In the context of relational database systems, unstructured data cannot be stored in predictably ordered columns and rows. One type of unstructured data is typically stored in a BLOB (binary large object), a catch-all data type available in most relational database management systems. Unstructured data may also refer to irregularly or randomly repeated column patterns that vary from row to row within each file or document.[citation needed]
Many of these data types, however, like e-mails, word processing text files, PPTs, image-files, and video-files conform to a standard that offers the possibility of metadata. Metadata can include information such as author and time of creation, and this can be stored in a relational database. Therefore, it may be more accurate to talk about this as semi-structured documents or data,[38] but no specific consensus seems to have been reached.
Unstructured data can also simply be the knowledge that business users have about future business trends. Business forecasting naturally aligns with the BI system because business users think of their business in aggregate terms. Capturing the business knowledge that may only exist in the minds of business users provides some of the most important data points for a complete BI solution.
Problems with semi-structured or unstructured data[edit]
There are several challenges to developing BI with semi-structured data. According to Inmon & Nesavich,[40] 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 a 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)[40] 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.”
The use of metadata[edit]
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.[37] 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.
2009 predictions[edit]
A 2009 paper predicted[41] these developments in the business intelligence market:
Because of lack of information, processes, and tools, through 2012, more than 35 percent of the top 5,000 global companies regularly fail to make insightful decisions about significant changes in their business and markets.
By 2012, business units will control at least 40 percent of the total budget for business intelligence.
By 2012, one-third of analytic applications applied to business processes will be delivered through coarse-grained application mashups.
A 2009 Information Management special report predicted the top BI trends: "green computing, social networking services, data visualization, mobile BI, predictive analytics, composite applications, cloud computing and multitouch".[42] Research undertaken in 2014 indicated that employees are more likely to have access to, and more likely to engage with, cloud-based BI tools than traditional tools.[43]
Other business intelligence trends include the following:
Third party SOA-BI products increasingly address ETL issues of volume and throughput.
Companies embrace in-memory processing, 64-bit processing, and pre-packaged analytic BI applications.
Operational applications have callable BI components, with improvements in response time, scaling, and concurrency.
Near or real time BI analytics is a baseline expectation.
Open source BI software replaces vendor offerings.
Other lines of research include the combined study of business intelligence and uncertain data.[44][45] In this context, the data used is not assumed to be precise, accurate and complete. Instead, data is considered uncertain and therefore this uncertainty is propagated to the results produced by BI.
According to a study by the Aberdeen Group, there has been increasing interest in Software-as-a-Service (SaaS) business intelligence over the past years, with twice as many organizations using this deployment approach as one year ago – 15% in 2009 compared to 7% in 2008.[46]
An article by InfoWorld’s Chris Kanaracus points out similar growth data from research firm IDC, which predicts the SaaS BI market will grow 22 percent each year through 2013 thanks to increased product sophistication, strained IT budgets, and other factors.[47]
An analysis of top 100 Business Intelligence and Analytics scores and ranks the firms based on several open variables[48]
See also[edit]
Accounting intelligence
Analytic applications
Artificial intelligence marketing
Business Intelligence 2.0
Business process discovery
Business process management
Business activity monitoring
Business service management
Comparison of OLAP Servers
Customer dynamics
Data Presentation Architecture
Data visualization
Decision engineering
Enterprise planning systems
Infonomics
Document intelligence
Integrated business planning
Location intelligence
Media intelligence
Meteorological intelligence
Mobile business intelligence
Multiway Data Analysis
Operational intelligence
Business Information Systems
Business intelligence tools
Process mining
Real-time business intelligence
Runtime intelligence
Sales intelligence
Test and learn
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^ Jump up to: a b c d e f Kimball
^ Jump up to: a b c d Swain Scheps Business Intelligence for Dummies, 2008, ISBN 978-0-470-12723-0
^ Jump up to: a b Watson, Hugh J.; Wixom, Barbara H. (2007). "The Current State of Business Intelligence". Computer. 40 (9): 96. doi:10.1109/MC.2007.331.
Jump up ^ The Data Warehouse Lifecycle Toolkit (2nd ed.). Ralph Kimball (2008).
Jump up ^ Microsoft Data Warehouse Toolkit. Wiley Publishing. (2006)
Jump up ^ Andrew Brust (2013-02-14). "Gartner releases 2013 BI Magic Quadrant". ZDNet. Retrieved 21 August 2013.
Jump up ^ Pendse, Nigel (7 March 2008). "Consolidations in the BI industry". The OLAP Report.
Jump up ^ "Gartner Says Worldwide Business Intelligence, CPM and Analytic Applications/Performance Management Software Market Grew Seven Percent in 2012". Gartner. Retrieved 11 May 2017.
Jump up ^ Imhoff, Claudia (4 April 2006). "Three Trends in Business Intelligence Technology".
Jump up ^ Mettler T, Vimarlund V (2009). "Understanding business intelligence in the context of healthcare". Health Informatics Journal. 15 (3): 254–264. doi:10.1177/1460458209337446.
^ Jump up to: a b c Rao, R. (2003). "From unstructured data to actionable intelligence" (PDF). IT Professional. 5 (6): 29. doi:10.1109/MITP.2003.1254966.
^ Jump up to: a b c Blumberg, R. & S. Atre (2003). "The Problem with Unstructured Data" (PDF). DM Review: 42–46. Archived from the original (PDF) on 25 January 2011.
^ Jump up to: a b Negash, S (2004). "Business Intelligence" (PDF). Communications of the Association of Information Systems. 13: 177–195.
^ Jump up to: 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
Jump up ^ Gartner Reveals Five Business Intelligence Predictions for 2009 and Beyond. gartner.com. 15 January 2009
Jump up ^ Campbell, Don (23 June 2009). "10 Red Hot BI Trends". Information Management.
Jump up ^ Lock, Michael (27 March 2014). "Cloud Analytics in 2014: Infusing the Workforce with Insight".
Jump up ^ Rodriguez, Carlos; Daniel, Florian; Casati, Fabio; Cappiello, Cinzia (2010). "Toward Uncertain Business Intelligence: The Case of Key Indicators". IEEE Internet Computing. 14 (4): 32. doi:10.1109/MIC.2010.59.
Jump up ^ Rodriguez, C.; Daniel, F.; Casati, F. & Cappiello, C. (2009), Computing Uncertain Key Indicators from Uncertain Data (PDF), pp. 106–120
Jump up ^ Julian, Taylor (10 January 2010). "Business intelligence implementation according to customer's needs". APRO Software. Retrieved 16 May 2016.
Jump up ^ SaaS BI growth will soar in 2010 | Cloud Computing. InfoWorld (2010-02-01). Retrieved 17 January 2012.
Jump up ^ "Top 100 analytics companies ranked and scored by Mattermark - Business Intelligence - Dashboards - Big Data".
Bibliography[edit]
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.
External links[edit]
"The Key Role Hadoop Plays in Business Intelligence and Data Warehousing" - St. Joseph's University
Chaudhuri, Surajit; Dayal, Umeshwar; Narasayya, Vivek (August 2011). "An Overview Of Business Intelligence Technology". Communications of the ACM. 54 (8): 88–98. doi:10.1145/1978542.1978562. Retrieved 26 October 2011.
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Demand curve
From Wikipedia, the free encyclopedia
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An example of a demand curve shifting. The shift from D1 To D2 means an increase in demand with consequences for the other variables
In economics, the demand curve is the graph depicting the relationship between the price of a certain commodity and the amount of it that consumers are willing and able to purchase at any given price. It is a graphic representation of a market demand schedule.[1] The demand curve for all consumers together follows from the demand curve of every individual consumer: the individual demands at each price are added together, assuming independent decision-making.
Demand curves are used to estimate behaviors in competitive markets, and are often combined with supply curves to estimate the equilibrium price (the price at which sellers together are willing to sell the same amount as buyers together are willing to buy, also known as market clearing price) and the equilibrium quantity (the amount of that good or service that will be produced and bought without surplus/excess supply or shortage/excess demand) of that market.[2] In a monopolistic market, the demand curve facing the monopolist is simply the market demand curve.
Demand curves are usually considered as theoretical structures that are expected to exist in the real world, but real world measurements of actual demand curves are difficult and rare.[3]
Contents
[hide]
1 Characteristics
2 Linear demand curve
3 Shift
3.1 Demand shifters
3.2 Changes that decrease or increase demand
3.3 Factors affecting market demand
4 Movement along a demand curve
5 Discreteness of amounts
6 Units of measures
7 Price elasticity of demand (PED)
8 Taxes and subsidies
9 See also
10 References
11 External links
Characteristics[edit]
According to convention, the demand curve is drawn with price on the vertical (y) axis and quantity on the horizontal (x) axis. The function actually plotted is the inverse demand function.
The demand curve usually slopes downwards from left to right; that is, it has a negative association. The negative slope is often referred to as the "law of demand", which means people will buy more of a service, product, or resource as its price falls. The demand curve is related to the marginal utility curve, since the price one is willing to pay depends on the utility. However, the demand directly depends on the income of an individual while the utility does not. Thus it may change indirectly due to change in demand for other commodities.
However, with Veblen goods, such as status symbols, the utility value of the good is largely the price and demand is higher at higher prices and the demand curve may curve upwards. With a Giffen good the price is taken by the market as a signal of quality, irrespective of the true nature of the product, and hence demand may be very low when priced low and increase at higher price points.
Linear demand curve[edit]
The demand curve is often graphed as a straight line of the form Q = a − bP where a and b are parameters. The constant "a" embodies the effects of all factors other than price that affect demand. If income were to change, for example, the effect of the change would be represented by a change in the value of "a" and be reflected graphically as a shift of the demand curve. The constant "b" is the slope of the demand curve and shows how the price of the good affects the quantity demanded.[4]
The graph of the demand curve uses the inverse demand function in which price is expressed as a function of quantity. The standard form of the demand equation can be converted to the inverse equation by solving for P or P = a/b − Q/b.[4]
More plainly, in the equation P = a − bQ, "a" is the intercept where price is zero (where the demand curve intercepts the Y-axis), "b" is the slope of the demand curve, "Q" is quantity and "P" is price.
There is movement along a demand curve when a change in price causes the quantity demanded to change.[5] It is important to distinguish between movement along a demand curve, and a shift in a demand curve. Movements along a demand curve happen only when the price of the good changes.[6] When a non-price determinant of demand changes the curve shifts. These "other variables" are part of the demand function. They are "merely lumped into intercept term of a simple linear demand function." [6] Thus a change in a non-price determinant of demand is reflected in a change in the x-intercept causing the curve to shift along the x axis.[7]
Shift[edit]
The shift of a demand curve takes place when there is a change in any non-price determinant of demand, resulting in a new demand curve.[5] Non-price determinants of demand are those things that will cause demand to change even if prices remain the same—in other words, the things whose changes might cause a consumer to buy more or less of a good even if the good's own price remained unchanged.[8] Some of the more important factors are the prices of related goods (both substitutes and complements), income, population, and expectations. However, demand is the willingness and ability of a consumer to purchase a good under the prevailing circumstances; so, any circumstance that affects the consumer's willingness or ability to buy the good or service in question can be a non-price determinant of demand. As an example, weather could be a factor in the demand for beer at a baseball game.
When income increases, the demand curve for normal goods shifts outward as more will be demanded at all prices, while the demand curve for inferior goods shifts inward due to the increased attainability of superior substitutes. With respect to related goods, when the price of a good (e.g. a hamburger) rises, the demand curve for substitute goods (e.g. chicken) shifts out, while the demand curve for complementary goods (e.g. tomato sauce) shifts in (i.e. there is more demand for substitute goods as they become more attractive in terms of value for money, while demand for complementary goods contracts in response to the contraction of quantity demanded of the underlying good).[5]
Demand shifters[edit]
Changes in disposable income, the magnitude of the shift also being related to the income elasticity of demand.
Changes in tastes and preferences—tastes and preferences are assumed to be fixed in the short-run. This assumption of fixed preferences is a necessary condition for aggregation of individual demand curves to derive market demand.
Changes in expectations.
Changes in the prices of related goods (substitutes and complements)
Population size and composition
N.B. Whilst variations in the price of the actual product affects the overall quantity demanded, economists do not consider price to affect the demand curve.
Changes that decrease or increase demand[edit]
A number of business publications have published opinion pieces on the actions that raise demand.[9][10][11]
Factors affecting market demand[edit]
Market or aggregate demand is the summation of individual demand curves. In addition to the factors which can affect individual demand there are three factors that can affect market demand (cause the market demand curve to shift):
a change in the number of consumers,
a change in the distribution of tastes among consumers,
a change in the distribution of income among consumers with different tastes.[12]
Some circumstances which can cause the demand curve to shift in include:
Decrease in price of a substitute
Increase in price of a complement
Decrease in income if good is normal good
Increase in income if good is inferior good
Movement along a demand curve[edit]
There is movement along a demand curve when a change in price causes the quantity demanded to change.[5] It is important to distinguish between movement along a demand curve, and a shift in a demand curve. Movements along a demand curve happen only when the price of the good changes.[6] When a non-price determinant of demand changes the curve shifts. These "other variables" are part of the demand function. They are "merely lumped into intercept term of a simple linear demand function." [6] Thus a change in a non-price determinant of demand is reflected in a change in the x-intercept causing the curve to shift along the x axis.[7]
Discreteness of amounts[edit]
If a commodity is sold in whole units, and these are valuable for a consumer, then the individual demand curve can hardly be approximated by a continuous curve. It is a set function of the price, defined by a price above which no unit is bought, a price range for which one is bought, etc.
Units of measures[edit]
If the local currency is dollars, for example, then the units of measurement of the variable "price" are "dollars per unit of the good" and the units of measurement of "quantity" are "units of the good per time (e.g., per week or per year). Thus quantity demanded is a flow variable.
Price elasticity of demand (PED)[edit]
Main article: Price elasticity of demand
PED is a measure of the sensitivity of the quantity variable, Q, to changes in the price variable, P. Elasticity answers the question of how much the quantity will change in percentage terms for a 1% change in the price, and is thus important in determining how revenue will change. PED is negative because of the inverse relationship between the price of a good and the quantity of the good demanded, a consequence of the law of demand.
The elasticity of demand indicates how sensitive the demand for a good is to a price change. If the absolute value of PED is between zero and 1, demand is said to be inelastic; if the absolute value of PED equals 1, the demand is unitary elastic; and if the absolute value of Price elasticity of demand is greater than 1, demand is elastic. A low coefficient implies that changes in price have little influence on demand. A high elasticity indicates that consumers will respond to a price rise by buying a lot less of the good and that consumers will respond to a price cut by buying a lot more...
Taxes and subsidies[edit]
A sales tax on the commodity does not directly change the demand curve, if the price axis in the graph represents the price including tax. Similarly, a subsidy on the commodity does not directly change the demand curve, if the price axis in the graph represents the price after deduction of the subsidy.
If the price axis in the graph represents the price before addition of tax and/or subtraction of subsidy then the demand curve moves inward when a tax is introduced, and outward when a subsidy is introduced.
See also[edit]
Wikimedia Commons has media related to Supply and demand curves.
Demand (economics)
Effect of taxes and subsidies on price
Feasibility condition
Hicksian demand
Inverse demand function
Law of demand
Marshallian demand
Price point
Sonnenschein–Mantel–Debreu theorem
Supply and demand
Wikiversity:Building the demand curve
References[edit]
Jump up ^ O'Sullivan, Arthur; Sheffrin, Steven M. (2003). Economics: Principles in Action. Upper Saddle River, New Jersey 07458: Pearson Prentice Hall. pp. 81–82. ISBN 0-13-063085-3.
Jump up ^ Krugman, Paul, and Wells, Robin. Microeconomics. Worth Publishers, New York. 2005.
Jump up ^ http://freakonomics.com/podcast/uber-economists-dream/
^ Jump up to: a b Besanko and Braeutigam (2005) p/ 91.
^ Jump up to: a b c d Case, K.E., Fair, R.C. (1994). 'Demand, Supply, and Market Equilibrium', Chapter 4 in Principles of Economics, 3rd ed., Prentice Hall Englewood Cliffs, New Jersey
^ Jump up to: a b c d Underwood, Instructor's Manual, Microeconomics 5th ed. (Prentice-Hall 2001) at 5.
^ Jump up to: a b The x intercept is affected because the standard diagram uses the inverse demand function
Jump up ^ http://www.harpercollege.edu/mhealy/eco212i/lectures/s&d/s&d.htm
Jump up ^ Kittaneh, Firas (January 24, 2014). "5 Strategies for Generating Consumer Demand". Entrepreneur. Retrieved August 30, 2017.
Jump up ^ Patel, Sujan (October 22, 2016). "7 Marketing Tips To Create A Demand For Your New Product". Forbes. Retrieved August 30, 2017.
Jump up ^ Ylywotzky, Adrian (September 6, 2011). "The Six Secrets Of Demand Creation". Fast Company. Retrieved August 30, 2017.
Jump up ^ Binger, B & Hoffman, E.: Microeconomics with Calculus, 2nd ed. Addison-Wesley 1998. A change in relative price changes the distribution of income which in turn changes the demand curve.
External links[edit]
Pricing to the demand curve
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