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Business intelligence

Overview

Business intelligence mainly refers to computer

Computer

A computer is a programmable machine designed to sequentially and automatically carry out a sequence of arithmetic or logical operations. The particular sequence of operations can be changed readily, allowing the computer to solve more than one kind of problem...

-based techniques used in identifying, extracting

Data extraction

Data extraction is the act or process of retrieving data out of data sources for further data processing or data storage...

, and analyzing business data, such as sales revenue by products and/or departments, or by associated costs and incomes.

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Encyclopedia

Business intelligence mainly refers to computer

Computer

-based techniques used in identifying, extracting

Data extraction

Data extraction is the act or process of retrieving data out of data sources for further data processing or data storage...

, and analyzing business data, such as sales revenue by products and/or departments, or by associated costs and incomes.

BI technologies provide historical, current and predictive views of business operations. Common functions of business intelligence technologies are reporting, online analytical processing, analytics

Analytics

Analytics is the application of computer technology, operational research, and statistics to solve problems in business and industry. Analytics is carried out within an information system: while, in the past, statistics and mathematics could be studied without computers and software, analytics has...

, data mining

Data mining

Data mining , a relatively young and interdisciplinary field of computer science is the process of discovering new patterns from large data sets involving methods at the intersection of artificial intelligence, machine learning, statistics and database systems...

, process mining

Process mining

Process mining is a process management technique that allows for the analysis of business processes based on event logs. The basic idea is to extract knowledge from event logs recorded by an information system...

, complex event processing

Complex Event Processing

Complex event processing consists of processing many events happening across all the layers of an organization, identifying the most meaningful events within the event cloud, analyzing their impact, and taking subsequent action in real time....

, business performance management

Business performance management

Business performance management is a set of management and analytic processes that enable the management of an organization's performance to achieve one or more pre-selected goals...

, benchmarking

Benchmarking

Benchmarking is the process of comparing one's business processes and performance metrics to industry bests and/or best practices from other industries. Dimensions typically measured are quality, time and cost...

, text mining

Text mining

Text mining, sometimes alternately referred to as text data mining, roughly equivalent to text analytics, refers to the process of deriving high-quality information from text. High-quality information is typically derived through the devising of patterns and trends through means such as...

and predictive analytics.

Business intelligence aims to support better business decision-making. Thus a BI system can be called a decision support system

Decision support system

A decision support system is a computer-based information system that supports business or organizational decision-making activities. DSSs serve the management, operations, and planning levels of an organization and help to make decisions, which may be rapidly changing and not easily specified in...

(DSS). Though the term business intelligence is sometimes used as a synonym for competitive intelligence

Competitive intelligence

A broad definition of competitive intelligence is the action of defining, gathering, analyzing, and distributing intelligence about products, customers, competitors and any aspect of the environment needed to support executives and managers in making strategic decisions for an organization.Key...

, 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. Business intelligence understood broadly can include the subset of competitive intelligence.

History

In a 1958 article, IBM

IBM

International Business Machines Corporation or IBM is an American multinational technology and consulting corporation headquartered in Armonk, New York, United States. IBM manufactures and sells computer hardware and software, and it offers infrastructure, hosting and consulting services in areas...

researcher Hans Peter Luhn

Hans Peter Luhn

Hans Peter Luhn was a computer scientist for IBM, and creator of the Luhn algorithm and KWIC indexing. He was awarded over 80 patents....

used the term business intelligence. He defined intelligence as: "the ability to apprehend the interrelationships of presented facts in such a way as to guide action towards a desired goal."

Business intelligence as it is understood today is said to have evolved from the decision support systems which began in the 1960s and developed throughout the mid-1980s. DSS originated in the computer-aided models created to assist with decision making

Decision making

Decision making can be regarded as the mental processes resulting in the selection of a course of action among several alternative scenarios. Every decision making process produces a final choice. The output can be an action or an opinion of choice.- Overview :Human performance in decision terms...

and planning. From DSS, data warehouse

Data warehouse

In computing, a data warehouse is a database used for reporting and analysis. The data stored in the warehouse is uploaded from the operational systems. The data may pass through an operational data store for additional operations before it is used in the DW for reporting.A data warehouse...

s, Executive Information System

Executive information system

An executive information system is a type of management information system intended to facilitate and support the information and decision-making needs of senior executives by providing easy access to both internal and external information relevant to meeting the strategic goals of the organization...

s, OLAP and business intelligence came into focus beginning in the late 80s.

In 1989, Howard Dresner (later a Gartner Group analyst) proposed "business intelligence" as an umbrella term to describe "concepts and methods to improve business decision making by using fact-based support systems." It was not until the late 1990s that this usage was widespread.

Business intelligence and data warehousing

Often BI applications use data gathered from a data warehouse

Data warehouse

or a data mart

Data mart

A data mart is the access layer of the data warehouse environment that is used to get data out to the users. The data mart is a subset of the data warehouse which is usually oriented to a specific business line or team.- Terminology :...

. However, not all data warehouses are used for business intelligence, nor do all business intelligence applications require a data warehouse.

In order to distinguish between concepts of business intelligence and data warehouses, Forrester Research

Forrester Research

Forrester Research is an independent technology and market research company that provides its clients with advice about technology's impact on business and consumers. Forrester Research has five research centers in the US: Cambridge, Massachusetts; New York, New York; San Francisco, California;...

often 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." When using 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

Information management

Information management is the collection and management of information from one or more sources and the distribution of that information to one or more audiences. This sometimes involves those who have a stake in, or a right to that information...

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 latter, narrower business intelligence market as "referring to just the top layers of the BI architectural stack such as reporting, analytics and dashboards."

Business intelligence and business analytics

Thomas Davenport

Thomas H. Davenport

Thomas H. Davenport is an American academic and author specializing in business process innovation and knowledge management...

has argued that business intelligence should be divided into querying

Information retrieval

Information retrieval is the area of study concerned with searching for documents, for information within documents, and for metadata about documents, as well as that of searching structured storage, relational databases, and the World Wide Web...

, reporting, OLAP

OLAP

In computing, online analytical processing, or OLAP , is an approach to swiftly answer multi-dimensional analytical queries. OLAP is part of the broader category of business intelligence, which also encompasses relational reporting and data mining...

, an "alerts" tool, and business analytics

Business analytics

Business analytics refers to the skills, technologies, applications and practices for continuous iterative exploration and investigation of past business performance to gain insight and drive business planning. Business analytics focuses on developing new insights and understanding of business...

. In this definition, business analytics is the subset of BI based on statistics, prediction, and optimization.

Applications in an enterprise

Business Intelligence can be applied to the following business purposes (MARCKM), in order to drive business value:

Measurement
Measurement
Measurement is the process or the result of determining the ratio of a physical quantity, such as a length, time, temperature etc., to a unit of measurement, such as the metre, second or degree Celsius...

– program that creates a hierarchy of performance metrics
Performance metrics
A performance metric is a measure of an organization's activities and performance. Performance metrics should support a range of stakeholder needs from customers, shareholders to employees. While traditionally many metrics are financed based, inwardly focusing on the performance of the...

(see also Metrics Reference Model
Metrics Reference Model
The Metrics Reference Model is the reference model created by the Consortium for Advanced Management-International to be a single reference library of performance metrics...

) and Benchmarking
Benchmarking
Benchmarking is the process of comparing one's business processes and performance metrics to industry bests and/or best practices from other industries. Dimensions typically measured are quality, time and cost...

that informs business leaders about progress towards business goals (AKA Business process management
Business process management
Business process management is a holistic management approach focused on aligning all aspects of an organization with the wants and needs of clients. It promotes business effectiveness and efficiency while striving for innovation, flexibility, and integration with technology. BPM attempts to...

).
Analytics
Analytics
Analytics is the application of computer technology, operational research, and statistics to solve problems in business and industry. Analytics is carried out within an information system: while, in the past, statistics and mathematics could be studied without computers and software, analytics has...

– program that builds quantitative processes for a business to arrive at optimal decisions and to perform Business Knowledge Discovery. Frequently involves: data mining
Data mining
Data mining , a relatively young and interdisciplinary field of computer science is the process of discovering new patterns from large data sets involving methods at the intersection of artificial intelligence, machine learning, statistics and database systems...

, process mining
Process mining
Process mining is a process management technique that allows for the analysis of business processes based on event logs. The basic idea is to extract knowledge from event logs recorded by an information system...

, statistical analysis, Predictive analytics
Predictive analytics
Predictive analytics encompasses a variety of statistical techniques from modeling, machine learning, data mining and game theory that analyze current and historical facts to make predictions about future events....

, Predictive modeling, Business process modeling
Business process modeling
Business Process Modeling in systems engineering is the activity of representing processes of an enterprise, so that the current process may be analyzed and improved. BPM is typically performed by business analysts and managers who are seeking to improve process efficiency and quality...

, complex event processing
Complex Event Processing
Complex event processing consists of processing many events happening across all the layers of an organization, identifying the most meaningful events within the event cloud, analyzing their impact, and taking subsequent action in real time....

.
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
Data visualization
Data visualization is the study of the visual representation of data, meaning "information that has been abstracted in some schematic form, including attributes or variables for the units of information"....

, Executive information system
Executive information system
An executive information system is a type of management information system intended to facilitate and support the information and decision-making needs of senior executives by providing easy access to both internal and external information relevant to meeting the strategic goals of the organization...

, OLAP
OLAP
In computing, online analytical processing, or OLAP , is an approach to swiftly answer multi-dimensional analytical queries. OLAP is part of the broader category of business intelligence, which also encompasses relational reporting and data mining...
Collaboration
Collaboration
Collaboration is working together to achieve a goal. It is a recursive process where two or more people or organizations work together to realize shared goals, — for example, an intriguing endeavor that is creative in nature—by sharing...

/Collaboration platform
Collaboration platform
An emerging category of computer software, collaboration platforms are unified electronic platforms that support synchronous and asynchronous communication through a variety of devices and channels....

– program that gets different areas (both inside and outside the business) to work together through Data sharing
Data sharing
Data sharing is the practice of making data used for scholarly research available to other investigators. Replication has a long history in science...

and Electronic Data Interchange
Electronic Data Interchange
Electronic data interchange is the structured transmission of data between organizations by electronic means. It is used to transfer electronic documents or business data from one computer system to another computer system, i.e...

.
Knowledge Management
Knowledge management
Knowledge management comprises a range of strategies and practices used in an organization to identify, create, represent, distribute, and enable adoption of insights and experiences...

– 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
Learning Management
Learning Management is the capacity to design pedagogic strategies that achieve learning outcomes for students.- Definition :A learning management system is a software application or Web-based technology that ranges from managing training and training records to distributing courses to...

and Regulatory compliance
Regulatory compliance
In general, compliance means conforming to a rule, such as a specification, policy, standard or law. Regulatory compliance describes the goal that corporations or public agencies aspire to in their efforts to ensure that personnel are aware of and take steps to comply with relevant laws and...

/Compliance
Compliance (regulation)
In general, compliance means conforming to a rule, such as a specification, policy, standard or law. Regulatory compliance describes the goal that corporations or public agencies aspire to in their efforts to ensure that personnel are aware of and take steps to comply with relevant laws and...

.

Requirements gathering

According to Ralph Kimball

Ralph Kimball

Ralph Kimball is an author on the subject of data warehousing and business intelligence. He is widely regarded as one of the original architects of data warehousing and is known for long-term convictions that data warehouses must be designed to be understandable and fast...

, the requirements of business users impact nearly every decision made throughout the design and implementation of a DW/BI system. The business requirements relate to all aspects of the daily business processes and hence are critical to successful data warehousing. Business requirements analysis occurs at two distinct levels:

Macro level: understand business needs and priorities relative to the overall business strategy
Micro level: understand user needs and desires in the context of a single, relatively narrowly defined project.

Approach

There are two basic interactive techniques for gathering requirements:

Conducting interviews: Speaking with users about their jobs, their objectives, and their challenges. This is either done with individuals or small groups.
Facilitated sessions and seminars that encourage creative mind-mapping.

Identify the interview team

Lead interviewer – directs the questioning
Scribe – takes notes during the interview. A tape recorder may be used to supplement the scribe.
Observers (optional) – watch but do not contribute. This may be for the purpose of training the observers in the interview approach, or so that the observers can comment on the interview after the event.

Research the organization

Reports, review of business operations, part of the annual report to gain insight regarding organizational structure. If applicable, a copy of the resulting documentation from the latest internal business/ IT strategy and planning meeting.

Select the interviewees

Select a cross section of representatives. Study the organization to get a good idea of all the stakeholders in the project.
These include:

Business interviewees (to understand the key business processes)
IT and Compliance/Security Interviewees (to assess preliminary feasibility of the underlying operational source systems to support the requirements emerging from the business side of the house)

Develop the interview questionnaires

Multiple questionnaires should be developed because the questioning will vary by job function and level.

The questionnaires for the data audit sessions will differ from business requirements questionnaires
Be structured. This will help the interview flow and help organize your thoughts before the interview.

Schedule and sequence the interviews

Scheduling and rescheduling takes time; prepare these a good time in advance! Sequence your interviews by beginning with the business driver, followed by the business sponsor. This is to understand the playing field from their perspective. The optimal sequence would be:

Business driver
Business sponsor
An interviewee from the middle of the organizational hierarchy
Bottom of the organizational hierarchy

The bottom is a disastrous place to begin because you have no idea where you are headed. The top is great for overall vision, but you need the business background, confidence, and credibility to converse at those levels. If you are not adequately prepared with in-depth business familiarity, the safest route is to begin in the middle of the organization.

Prepare the interviewees

Make sure the interviewees are appropriately briefed and prepared to participate. As a minimum, a letter should be emailed to all interview participants to inform them about the process and the importance of their participation and contribution. The letter should explain that the goal is to understand their job responsibilities and business objectives, which then translate into the information and analyses required to get their job done. In addition they should be asked to bring copies of frequently used reports or spreadsheet analyses.

The letter should be signed by a high ranking sponsor, someone well respected by the interviewees. It is advisable not to attach a list of the fifty questions you might ask in hopes that the interviewees will come prepared with answers. The odds are that they won’t take the time to prepare responses and even get intimidated by the volume of your questions.

Issues with requirements gathering and interviews

The process of conducting an interview may seem exhaustive at first, but the ground rule is to be well prepared in all steps.
Techniques for questioning may be a good idea to investigate before conducting the interview. Ask open-ended questions such as why, how, what-if, and what-then questions. Ask unbiased questions.

Wrongfully asked questions can lead to wrong answers and, in the worst case, wrong requirements are gathered. The whole process is valuable in time and resources, and the wrong data can slow down the development of the whole BI installation. Be sure that everyone in the interviewee team is aware of their role to support that everything goes as planned. The next part is to synthesize around the business processes.

Prioritization of business intelligence projects

It is often difficult to provide a positive business case for business intelligence (BI) initiatives and often the projects will need to be prioritized through strategic initiatives. Here are some hints to increase the benefits for a BI project.

As described by Kimball you must determine the tangible benefits such as eliminated cost of producing legacy reports.
Enforce access to data for the entire organization. In this way even a small benefit, such as a few minutes saved, will make a difference when it is multiplied by the number of employees in the entire organization.
As described by Ross, Weil & Roberson for Enterprise Architecture, 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, which will be able to detect suitable business projects.

Success factors of implementation

Before implementing a BI solution, it is worth taking different factors into consideration before proceeding. According to Kimball et al., these are the three critical areas that you need to assess within your organization before getting ready to do a BI project:

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

The commitment and sponsorship of senior management is according to Kimball et al., the most important criteria for assessment. This is because having strong management backing will help overcome shortcomings elsewhere in the project. But as Kimball et al. state: “even the most elegantly designed DW/BI system cannot overcome a lack of business [management] sponsorship”.
It is very important that the management 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. It is imperative that there is support from multiple members of the management so the project will not fail if one person leaves the steering group. However, having many managers that work together on the project can also mean that the there are several different interests that attempt to pull the project in different directions. For instance if different departments want to put more emphasis on their usage of the implementation. This issue can be countered by an early and specific analysis of the different business areas that will benefit the most from the implementation. All stakeholders in project should participate in this analysis in order for them to feel ownership of the project and to find common ground between them.
Another management problem that should be encountered before start of implementation is if the Business sponsor is overly aggressive. If the management individual gets carried away by the possibilities of using BI and starts wanting the DW or BI implementation to include several different sets of data that were not included in the original planning phase. However, since extra implementations of extra data will most likely add many months to the original plan. It is probably a good idea to make sure that the person from management is aware of his actions.

Implementation should be driven by clear business needs

Because of the close relationship with senior management, another critical thing that needs to be assessed before the project is implemented is whether or not there actually is a business need and whether there is a clear business benefit by doing the implementation.
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.

The amount and quality of the available data

This ought to be the most important factor, since without good data – it does not really matter how good your management sponsorship or your business-driven motivation is. If you do not have the data, or the data does not have sufficient quality, any BI implementation will fail. Before implementation it is a very good idea to do data profiling; this analysis will be able to describe the “content, consistency and structure [..]” of the data. This should be done as early as possible in the process and if the analysis shows that your data is lacking, it is a good idea to put the project on the shelf temporarily while the IT department figures out how to do proper data collection.

User aspect

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. If the usability

Usability

Usability is the ease of use and learnability of a human-made object. The object of use can be a software application, website, book, tool, machine, process, or anything a human interacts with. A usability study may be conducted as a primary job function by a usability analyst or as a secondary job...

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 really use the system. If the system does not add value to the users´ mission, they will simply not use it.

In order to increase the user acceptance of a BI system, it may be advisable to consult the business users at an early stage of the DW/BI lifecycle, for example at the requirements gathering phase. This can provide an insight into the business process

Business process

A business process or business method is a collection of related, structured activities or tasks that produce a specific service or product for a particular customer or customers...

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.

Taking on a user-centered approach throughout the design and development stage may further increase the chance of rapid user adoption of the BI system.

Besides focusing on the user experience offered by the BI applications, it may also possible to motivate the users to utilize the system by adding an element of competition. Kimball suggests implementing a function on the Business Intelligence portal

Business Intelligence portal

Business Intelligence portal or BI portal is the primary access interface for DW/BI applications. When using a DW/BI application the BI portal is the users first impression of the DW/BI system...

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. Watson describes how a large call centre has implemented performance dashboards for all the call agents and that monthly incentive bonuses have been tied up to the performance metrics. Furthermore the agents can see how their own performance compares to the other team members. The implementation of this type of performance measurement and competition significantly improved the performance of the agents.

Other elements which may increase the success of BI can be by involving senior management in order to make BI a part of the organizational culture

Organizational culture

Organizational culture is defined as “A pattern of shared basic assumptions invented, discovered, or developed by a given group as it learns to cope with its problems of external adaptation and internal integration" that have worked well enough to be considered valid and therefore, to be taught to...

and also by providing the users with the necessary tools, training and support. By offering user training, more people may actually use the BI application.

Providing user support is necessary in order to maintain the BI system and assist users who run into problems. 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 helpdesk can be manned by e.g. power users or the DW/BI project team.

Marketplace

There are a number of business intelligence vendors, often categorized into the remaining independent "pure-play" vendors and the consolidated "megavendors" which have entered the market through a recent trend of acquisitions in the BI industry.

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).

Industry-specific

Specific considerations for business intelligence systems have to be taken in some sectors such as governmental banking regulations

Bank regulation

Bank regulations are a form of government regulation which subject banks to certain requirements, restrictions and guidelines. This regulatory structure creates transparency between banking institutions and the individuals and corporations with whom they conduct business, among other things...

. 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 laws.

Semi-structured or unstructured data

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 percent 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.

The management of semi-structured data is recognized as a major unsolved problem in the information technology industry. According to projections from Gartner (2003), white collar workers will 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. 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.

Unstructured data vs. Semi-structured data

Unstructured and semi-structured data have different meanings depending on their context. In the context of relational database systems, it refers to data that cannot be stored in columns and rows

Row (database)

In the context of a relational database, a row—also called a record or tuple—represents a single, implicitly structured data item in a table. In simple terms, a database table can be thought of as consisting of rows and columns or fields...

. It must be stored in a BLOB

Blob

- In biology :* Blob , sections of the visual cortex where groups of color-sensitive neurons assemble* Globster, an unidentified organic mass that washes up on the shoreline of an ocean or other body of water...

(binary large object), a catch-all data type available in most relational database

Relational database

A relational database is a database that conforms to relational model theory. The software used in a relational database is called a relational database management system . Colloquial use of the term "relational database" may refer to the RDBMS software, or the relational database itself...

management systems.

But many of these data types, 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, but no specific consensus seems to have been reached.

Problems with semi-structured or unstructured data

There are several challenges to developing BI with semi-structured data. According to Inmon & Nesavich, some of those are:

Physically accessing unstructured textual data – unstructured data is stored in a huge variety of formats.
Terminology
Terminology
Terminology is the study of terms and their use. Terms are words and compound words that in specific contexts are given specific meanings, meanings that may deviate from the meaning the same words have in other contexts and in everyday language. The discipline Terminology studies among other...

– 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) 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

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

Metadata

The term metadata is an ambiguous term which is used for two fundamentally different concepts . Although the expression "data about data" is often used, it does not apply to both in the same way. Structural metadata, the design and specification of data structures, cannot be about data, because at...

. A lot of 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

Information extraction

Information extraction is a type of information retrieval whose goal is to automatically extract structured information from unstructured and/or semi-structured machine-readable documents. In most of the cases this activity concerns processing human language texts by means of natural language...

Future

A 2009 Gartner paper predicted 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 will 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
Granularity
Granularity is the extent to which a system is broken down into small parts, either the system itself or its description or observation. It is the "extent to which a larger entity is subdivided...

application mashups
Mashup (web application hybrid)
In Web development, a mashup is a Web page or application that uses and combines data, presentation or functionality from two or more sources to create new services...

.

A 2009 Information Management special report predicted the top BI trends: "green computing

Green computing

Green computing or green IT, refers to environmentally sustainable computing or IT. In the article Harnessing Green IT: Principles and Practices, San Murugesan defines the field of green computing as "the study and practice of designing, manufacturing, using, and disposing of computers, servers,...

, social network

Social network

A social network is a social structure made up of individuals called "nodes", which are tied by one or more specific types of interdependency, such as friendship, kinship, common interest, financial exchange, dislike, sexual relationships, or relationships of beliefs, knowledge or prestige.Social...

ing, data visualization

Data visualization

Data visualization is the study of the visual representation of data, meaning "information that has been abstracted in some schematic form, including attributes or variables for the units of information"....

, mobile BI

Mobile business intelligence

Mobile Business Intelligence refers to the distribution of business data to mobile devices such as smartphones and tablet computers...

, predictive analytics

Predictive analytics

Predictive analytics encompasses a variety of statistical techniques from modeling, machine learning, data mining and game theory that analyze current and historical facts to make predictions about future events....

, composite application

Composite application

In computing, the term composite application expresses a perspective of software engineering that defines an application built by combining multiple existing functions into a new application. The technical concept can be compared to mashups...

s, cloud computing

Cloud computing

Cloud computing is the delivery of computing as a service rather than a product, whereby shared resources, software, and information are provided to computers and other devices as a utility over a network ....

and multitouch

Multi-touch

In computing, multi-touch refers to a touch sensing surface's ability to recognize the presence of two or more points of contact with the surface...

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Other business intelligence trends include the following:

Third party SOA-BI products increasingly address ETL issues of volume and throughput.
Cloud computing and Software-as-a-Service (SaaS) are ubiquitous.
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. 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.

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.