Business intelligence ( BI ) consists of strategies and technologies used by the company for business data information analysis. BI technology provides historical, current, and predicted business operations. Common functions of business intelligence technologies include reporting, online analytic processing, analytics, data mining, process mining, complex event processing, business performance management, benchmarking, text mining, predictive analysis, and prescriptive analysis. BI technology can handle large amounts of structured and sometimes unstructured data to help identify, develop and create new strategic business opportunities. They aim to enable easy interpretation of these great data. Identifying new opportunities and implementing effective strategies based on insight can provide businesses with competitive market advantage and long-term stability.
Business intelligence can be used by companies to support 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 aggregating data coming from the market in which the company operates (external data) with data from internal to business sources such as financial and operating data (internal data). When combined, external and internal data can provide a complete picture that, in essence, creates "intelligence" that can not be derived from a single data set. Among many uses, business intelligence tools empower organizations to gain insight into new markets, to assess the demand and suitability of products and services for different market segments and to measure the impact of marketing efforts.
Often BI applications use data collected from data warehouse (DW) or from data mart, and BI and DW concepts are combined as "BI/DW" or as "BIDW". A data warehouse contains copies of analytical data that facilitate decision support.
Video Business intelligence
Components
Business intelligence consists of increasing the number of components including:
- Multidimensional aggregation and allocation
- Denormalization, tagging and standardization
- Real-time reporting with analytics marks
- Methods interact with unstructured data sources
- Group aggregation, budgeting and rolling estimates
- Statistical inference and probabilistic simulation
- Optimization of key performance indicators
- Version control and process management
- Open items management
Maps Business intelligence
History
The earliest known use of the term "Business Intelligence" was by Richard Millar Devens in 'CyclopÃÆ'Â|dia of Commercial and Business Anecdotes' from 1865. Devens used the term to describe how the banker Sir Henry Furnese made a profit by accepting and acting on information about his environment , before its competitors. "Throughout the Netherlands, Flanders, France and Germany, he maintained a complete and perfect business intelligence train, and the news of many battles thus fought was first accepted by him, and the fall of Namur added to his advantage, for the initial acceptance of the news >. "(Devens, (1865), p.Ã, 210). The ability to collect and react accordingly based on information taken, Furnes' superior ability, is currently still at the heart of BI.
In a 1958 article, IBM researcher Hans Peter Luhn used the term business intelligence. He uses Webster's dictionary definition of intelligence: "the ability to capture the interconnectedness of facts presented in such a way as to guide action toward desired goals."
Business intelligence as understood today is said to have evolved from a decision support system (DSS) that began in 1960 and developed throughout the mid-1980s. DSS comes from computer-assisted models designed to aid decision making and planning. From DSS, data warehouses, Executive Information Systems, OLAP and business intelligence began to focus on 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 using a fact-based support system." It was not until the late 1990s that this usage was widespread.
Critics see BI as evolving from mere business reporting along with the emergence of powerful and easy-to-use data analysis tools. In this case also criticized as keyword marketing in the context of the wave of "big data".
Data discovery
Data discovery is a keyword in BI for creating and using interactive reports and browsing data from multiple sources. Market research firm Gartner is promoting it in 2012.
While there is no official Data Discovery definition, the accepted process is user-driven to search for patterns or specific items in the data set. Data discovery applications use visual tools such as geographic maps, pivot tables, and hot maps to make the process of finding specific patterns or items quickly and intuitively. However, since the humam brain is not suitable for this task, data mining must ultimately be used to achieve this goal.
There is a growing understanding Business Intelligence is a field in which data is applied to strategic thinking, while the needs of worldly data to solve everyday problems must be addressed by a series of different processes and tools, collectively known as [Operational Intelligence]. This is for the Operational Intelligence Data Discovery concept seems to be more appropriately attached.
Data storage
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 useful and useful information used to enable more effective insights, strategies, tactics and operations, and decision-making. "Based on this definition, business intelligence also includes technologies such as data integration, data quality, data warehousing, master data management, content and text analysis, and many others that are sometimes marketed by the market to the" Information Management "segment. Therefore, Forrester refers to the data preparation
data usage - Forrester defines a narrower business intelligence market as, "... refers only to the top layers of BI architecture stacks like reporting, analysis, and dashboards."
Comparison with competitive intelligence
Although the term business intelligence is sometimes synonymous for competitive intelligence (as both support decision-making), BI uses technology, processes, and applications to analyze most of the internal, structured data and business processes while competitive intelligence gathers, analyzes and disseminates information with a topical focus on competitors company. If widely understood, business intelligence can include part of competitive intelligence.
Comparison with business analysis
Business intelligence and business analytics are sometimes used interchangeably, but there are alternative definitions. One contrast definition both, states that the term business intelligence refers to the collection of business data to seek information primarily through asking questions, reporting, and online analytical processes. Business analysis, on the other hand, uses statistical and quantitative tools for clear and predictive modeling.
In the alternative definition, Thomas Davenport, professor of information and management technology at Babson College, believes that business intelligence should be divided into queries, reporting, online analytical processing (OLAP), "warning" tools, and business analysis. In this definition, business analysis is a part of BI that focuses on statistics, predictions, and optimizations, rather than reporting functions.
Apps in an enterprise
Business intelligence can be applied to the following business goals, to drive business value:
- Measurements - programs that create performance metrics hierarchies (see also Reference Model Metrics) and benchmarking that tell business leaders about progress toward business goals (business process management).
- Analytics - programs that build quantitative processes for businesses to reach optimal decisions and to conduct business knowledge discovery. Often involves: data mining, process mining, statistical analysis, predictive analysis, predictive modeling, business process modeling, data pedigrees, complex event processing, and prescriptive analysis.
- Corporate reporting/reporting - a program that builds infrastructure for strategic reporting to serve strategic business management, not operational reporting. Often involves data visualization, executive information systems and OLAP.
- Collaboration/collaboration platforms - programs that get different areas (both inside and outside the business) to work together through data sharing and electronic data exchange.
- Knowledge Management - a program for making enterprises driven by data through strategies and practices to identify, create, represent, distribute, and enable the 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 proactive approach, such as a warning function that immediately notifies the end user if certain conditions are met. For example, if some business metrics exceed a predefined threshold, the metrics will be highlighted in standard reports, and business analysts may be warned via e-mail or other monitoring services.
Prioritize the project
It will be difficult to provide a positive business case for business intelligence initiatives, and often projects should be prioritized through strategic initiatives. BI projects can achieve higher priorities within the organization if managers consider the following:
- As Kimball explains, the BI managers must determine tangible benefits such as the costs omitted to produce inheritance reports.
- Data access for the entire organization must be enforced. In this way, even a few benefits, like a few minutes saved, make a difference when multiplied by the number of employees across the organization.
- As Ross explains, Weil & amp; Roberson for Corporate Architecture, managers should also consider letting the BI project be driven by other business initiatives with excellent business cases. To support this approach, an organization must have a company architect who can identify suitable business projects.
- Use a structured and quantitative methodology to create sustainable priorities according to the organization's actual needs, such as a weighted decision matrix.
Success factors implementation
According to Kimball et al. , there are three important areas that should be assessed by the organization before preparing to undertake the BI project:
- Level of commitment and project sponsor of senior management.
- Level of business needs to create BI implementation.
- Number and quality of business data available.
Business sponsors
Commitments and sponsorship of senior management in accordance with Kimball et al. , the most important criteria for assessment. This is because having strong management support helps to overcome deficiencies elsewhere in the project. However, as Kimball et al. : "even the most elegantly designed DW/BI system can not overcome the lack of business [management] support".
It is important that the personnel participating in the project have a vision and idea of ​​the benefits and weaknesses of implementing the BI system. The best business sponsors must have organizational influence and must be well connected within the organization. It is ideal that business sponsors demand but can also be realistic and supportive if their implementation is delayed or deprived. The sponsor of management must also be able to assume responsibility and be responsible for the failure and decline of the project. Support from some members of the management ensures the project does not fail if one person leaves the steering group. However, having multiple managers working together in a project can also mean that there are different interests that try to pull the project in different directions, just as different departments want to put more emphasis on its use. This problem can be solved by a preliminary and specific analysis of the most profitable business areas of its implementation. All stakeholders in the project must participate in this analysis to make them feel invested in the project and to find common ground.
Another management problem that may be encountered before the start of implementation is an overly aggressive business sponsor. The creep scope problem occurs when the sponsor requests unspecified data sets in the original planning phase.
Business requirements
Because of the close relationship with senior management, another important thing to be assessed before the project starts is whether there is a business need and whether there is a clear business advantage by implementing it. The needs and benefits of implementation are sometimes driven by competition and the need to profit in the marketplace. Another reason for the business-driven approach to BI implementation is the acquisition of other organizations that enlarge the original organization, it can sometimes be useful to implement DW or BI in order to create more oversight.
Companies that implement BI are often large, multinational organizations with diverse subsidiaries. They can be through the application of Business Intelligence Competency Center (BICC).
Well-designed BI solutions provide a consolidated view of key business data that is not available elsewhere in the organization, providing management visibility and control over actions that will not exist.
The amount and quality of data available
Without proper data, or with too little quality data, the BI implementation fails; no matter how good a management sponsor or motivation is driven by a business. Before implementation it is a good idea to do profiling data. This analysis identifies 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 the data is lacking, place the project temporarily while the IT department finds out how to collect the data correctly.
When planning for business data and business intelligence requirements, it is always advisable to consider specific scenarios that apply to specific organizations, and then select the business intelligence feature that is most appropriate for the scenario.
Often, scenarios revolve around different business processes, each built on one or more data sources. These sources are used by features that present the data as information to knowledge workers, who then act on the information. The organization's business needs for each business process are adopted according to the essential steps of business intelligence. Important business intelligence steps include but are not limited to:
- Open a business data source to collect the required data
- Convert business data into information and present it appropriately
- Query and data analysis
- Act on data collected
The quality aspect in business intelligence should cover all processes from source data to final reporting. At each step, quality gates are different:
- Source Data:
- Data Standardization: create comparable data (same unit, same pattern...)
- Master Data Management: unique reference
- Operational Data Store (ODS):
- Data Cleanup: detection & amp; fix inaccurate data
- Data Recording: check for inappropriate, null/empty values
- Data warehouse:
- Completeness: make sure all expected data is already loaded
- Referential integrity: unique and existing references over all sources
- Consistency between sources: check aggregated data vs sources
- Report:
- The uniqueness of the indicator: only one share dictionary indicator
- Formulation accuracy: local reporting formulas should be avoided or checked
User aspect
Some considerations should be made to successfully integrate the use of business intelligence systems within a company. Finally, the BI system must be accepted and utilized by the user in order to add value to the organization. If the usefulness of this system is bad, users may become frustrated and spend a lot of time figuring out how to use the system or may not be productive. If the system does not add value to the user's mission, they do not use it.
To improve user acceptance of the BI system, it may be advisable to consult with business users in the early stages of the DW/BI life cycle, for example in the requirements collection phase. It can provide insight into business processes and what users need from the BI system. There are several methods for collecting this information, such as questionnaires and interview sessions.
When collecting requirements from business users, local IT departments should also be consulted to determine to what extent it is possible to meet business needs based on available data.
Taking a user-centered approach during the design and development stage can further increase the chances of rapid user adoption of the BI system.
In addition to focusing on the user experience offered by BI applications, it may also motivate users to take advantage of the system by adding elements of the competition. Kimball suggests implementing a function on the Business Intelligence portal website where reports on system usage can be found. Thus, managers can see how well their department performs and compares themselves with others and this can spur them to encourage their staff to use the BI system even more.
In a 2007 article, H. J. Watson provides an example of how competitive elements can act as incentives. Watson explains how large call centers implement performance dashboards for all call agents, with monthly incentive bonuses related to performance metrics. Also, agents can compare their performance with other team members. Implementation of this type of performance and competition measurement significantly improves agency performance.
BI's opportunity to succeed can be enhanced by involving senior management to help make BI part of the organizational culture, and by providing users with the tools, training and support needed. Training encourages more people to use BI applications.
Providing user support is required to maintain the BI system and resolve user issues. User support can be included in many ways, for example by creating a website. The website must contain great content and tools to find the necessary information. Furthermore, helpdesk support can be used. The help desk can be manned by power users or the DW/BI project team.
BI Portal
The Business Intelligence portal (the BI portal) is the main access interface for Data Warehouse (DW) and Business Intelligence (BI) applications. The BI portal is the first impression of the user of the DW/BI system. This is usually a browser application, from which users have access to all the individual services of the DW/BI system, reports and other analytical functions. The BI portal should be implemented in such a way that it is easy for DW/BI application users to call the application functionality.
The main function of BI portal is to provide DW/BI application navigation system. This means that the portal must be implemented in a way that the user has access to all DW/BI application functions.
The most common way to design a portal is to customize it specifically with the organization's business processes where DW/BI applications are designed, in which way the portal can best fit the needs and needs of its users.
The BI portal should be easy to use and understand, and if possible have a look and feel similar to other applications or web content from the organization, DW/BI applications are designed for (consistency).
The following is a list of desired features for the general web portal and the BI portal in particular:
- Can be used
- Users should easily find what they need in BI tools.
- Rich Content
- Portal is not just a report printing tool, it should contain more functions like suggestions, help, support information and documentation.
- Clean
- Portals should be designed to be easy to understand and not too complex to confuse users
- Current
- Portal must be updated periodically.
- Interactive
- Portals should be implemented in a way that allows users to use their functions and encourage them to use the portal. Scalability and customization gives users the means to customize the portal to each user.
- Value Oriented
- It is important that the user has a feeling that the DW/BI application is a valuable resource worth working on.
Marketplace
There are a number of business intelligence vendors, often categorized into independent "pure-play" vendors and consolidated "megavendors" who have entered the market through the recent trend of acquisitions in the BI industry. The business intelligence market is growing gradually. In 2012, business intelligence services generate $ 13.1 billion in revenue.
Some companies that adopt BI software decide to choose from best-of-breed offerings instead of buying a comprehensive (full service) integrated solution.
Industry only
Special considerations for business intelligence systems should be taken in some sectors such as government banking regulations or health care. Information collected by banking institutions and analyzed with BI software should be protected from several groups or individuals, while fully available to other groups or individuals. Therefore, BI solutions should be sensitive to these needs and flexible enough to adapt to new regulations and changes to existing laws.
Semi-structured or unstructured data
Businesses create vast amounts of valuable information in the form of emails, memos, notes from call centers, news, user groups, chats, reports, web pages, presentations, image files, video files, and marketing materials. According to Merrill Lynch, over 85% of all business information exists in this form. This type of information is called semi-structured or unstructured data. However, organizations often only use these documents once.
Semi-structured data management is recognized as a major unsolved problem in the information technology industry. According to projections from Gartner (2003), white-collar workers spend 30 to 40 percent of their time searching, locating, and assessing unstructured data. BI uses structured and unstructured data, but the former is easy to find, and the latter contains a large amount of information needed for analysis and decision making. Because of the difficulty of locating, locating, and assessing unstructured or semi-structured data, organizations should not take advantage of this vast source of information, which may influence certain decisions, tasks, or projects. This may ultimately lead to informed decision making.
Therefore, when designing business intelligence/solutions-DW, the specific problems associated with semi-structured and unstructured data must be accommodated for structured data.
Unstructured data vs. semi-structured data
Unstructured and semi-structured data has different meanings depending on the context. In the context of a relational database system, unstructured data can not be stored in predictable columns and rows. One type of unstructured data is usually stored in a BLOB (binary large object), a catch-all data type available in most relational database management systems. Unstructured data can also refer to irregular or repetitive random column patterns (nonrepetitive) that vary from row to row in every file or document.
Many of these data types, such as email, word processing text files, PPTs, image files, and video files that conform to standards that offer metadata possibilities. Metadata can include information such as author and creation time, and these can be stored in a relational database. Therefore, it may be more accurate to discuss this as a semi-structured document or data, but no specific consensus seems to have been achieved.
Unstructured data can also be the knowledge that business users have about future business trends. Business forecasting is naturally parallel to the BI system because business users think of their business in aggregate. Capturing business knowledge that may exist only 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
There are some challenges to developing BI with semi-structured data. According to Inmon & amp; Nesavich, some of which are:
- Physically accessing unstructured textual data - unstructured data is stored in large formats.
- Terminology - Among researchers and analysts, there is a need to develop standard terminology.
- Data volumes - As stated earlier, up to 85% of all data exists as semi-structured data. Couple it with the need for word-to-word and semantic analysis.
- Unstructured textual data search - Simple search on some data, e.g. apples, results in links where there are references to that exact search term. (Inmon & Nesavich, 2008) provides an example: "a search is done on a long-term crime." In a simple search, the term crime is used, and everywhere there is a reference to a crime, a hit to an unstructured document is made "But a simple search is raw. It finds no reference to crime, arson, murder, embezzlement, car killing, and the like, even though this crime is a crime. "
Metadata usage
To resolve issues with search and data assessment, it is necessary to know something about the content. This can be done by adding context through the use of metadata. Many systems have already captured some metadata (eg file name, author, size, etc.), but more useful are metadata about the actual content - e.g. summary, topic, person or company mentioned. Two technologies designed to generate metadata about content are automated categorization and information extraction.
Prediction
A 2009 paper estimates these developments in the business intelligence market:
- Due to lack of information, processes, and equipment, by 2012, more than 35 percent of the top 5,000 global companies regularly fail to make in-depth decisions about significant changes in their businesses and markets.
- In 2012, business units will control at least 40 percent of the total budget for business intelligence.
- As of 2012, one-third of the analytics apps applied to business processes will be delivered via rough app mashups.
The 2009 Information Management Management Report predicts the top BI trends: "green computing, social networking services, data visualization, cellular BI, predictive analytics, composite applications, cloud computing and multitouch". Research conducted in 2014 shows that employees are more likely to have access to, and are more likely to engage with, cloud-based BI tools than traditional tools.
Other business intelligence trends include the following:
- The third-party SOA-BI product addresses the volume and throughput issues of ETL.
- The company embraces in-memory processing, 64-bit processing, and pre-packaged BI analytics applications.
- The operational app has a BI Callable component, with increased response time, scaling, and concurrency.
- The near-term BI analysis or real time is the basic expectation.
- Open source BI software replaces vendor offerings.
Other research tracks include a composite study of business intelligence and uncertain data. In this context, the data used is not considered appropriate, accurate and complete. Conversely, data is considered uncertain and therefore this uncertainty is propagated to the results generated by BI.
According to a study by the Aberdeen Group, there has been an increase in interest in Software-as-a-Service (SaaS) business intelligence over the past few years, with twice as many organizations using this deployment approach as one year ago - 15% 7% in 2008.
An article by InfoWorld's Chris Kanaracus shows similar growth data from research firm IDC, which predicts the market for SaaS BI will grow 22 percent annually through 2013 thanks to increased product sophistication, tense IT budgets, and other factors.
An analysis of 100 Business Intelligence and Analytics scores and company ratings based on several open variables
See also
References
Bibliography
- Ralph Kimball et al. "Data warehouse Lifecycle Toolkit" (2nd ed.) Wiley ISBNÃ, 0-470-47957-4
- Peter Rausch, Alaa Sheta, Aladdin Ayesh: Business Intelligence and Performance Management: Theories, Systems and Industrial Applications , Springer Verlag UK, 2013, ISBN 978-1-4471-4865-4.
- Munoz, J.M. (2017). Global Business Intelligence. Routledge: UK. ISBN 978-1-1382-03686
External links
- "Hadoop's Key Role in Business Intelligence and Data Warehousing" - St. University Joseph
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Chaudhuri, Surajit; Dayal, Umeshwar; Narasayya, Vivek (August 2011). "Overview of Business Intelligence Technology". ACM Communications . 54 (8): 88-98. doi: 10.1145/1978542.1978562 . Retrieved October 26 2011 .
Source of the article : Wikipedia
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