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Intelligence Engine
An intelligence engine is a type of enterprise information management that combines business rule management, predictive, and prescriptive analytics to form a unified information access platform that provides real-time intelligence through search technologies, dashboards and/or existing business infrastructure. Intelligence Engines are process and/or business problem specific, resulting in industry and/or function-specific marketing trademarks associated with them. They can be differentiated from enterprise resource planning (ERP) software in that intelligence engines include organization-level business rules and proactive decision management functionality. History The first intelligence engine application appears to have been introduced in 2001 by Sonus Networks, Inc. in their patent US6961334 B1. Applied to the field of telecommunications systems, the intelligence engine was composed of a database queried by a data distributor layer, received by a telephony management layer an ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Enterprise Information Management
Enterprise information management (EIM) is a business discipline specializing in providing solutions for optimal use of information within organizations, for instance to support decision-making processes or day-to-day operations that require the availability of knowledge. EIM It aims to overcome traditional/legacy IT-related barriers to managing information at an enterprise level. ThInternational Organization for Standardization(ISO) recognises information management as a separate function to information technology (IT/ICT). Enterprise Information Management practices are guided by thsuite of ISO Standardsthat are managed bISO Technical Committee 46(ISO/TC 46). EIM combines enterprise content management (ECM), business process management (BPM), customer experience management Customer experience (CX) is a totality of cognitive, affective, sensory, and behavioral consumer responses during all stages of the consumption process including pre-purchase, consumption, and post-purch ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Factiva
Factiva is a business information and research tool owned by Dow Jones & Company. Factiva aggregates content from both licensed and free sources. Providing organizations with search, alerting, dissemination, and other information management capabilities. Factiva products claim to provide access to more than 32,000 sources such as newspapers, journals, magazines, television and radio transcripts, photos, etc. These are sourced from nearly every country in the world in 28 languages, including more than 600 continuously updated newswires. History The company was founded as a joint-venture between Reuters and Dow Jones & Company in May 1999 under the Dow Jones Reuters Business Interactive name, and renamed Factiva six months later. Timothy M. Andrews, a longtime Dow Jones executive, was founding president and chief executive of the venture. Mr. Andrews was succeeded by Clare Hart in January 2000, another longtime Dow Jones executive, who was serving as Factiva's vice pres ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Big Data
Though used sometimes loosely partly because of a lack of formal definition, the interpretation that seems to best describe Big data is the one associated with large body of information that we could not comprehend when used only in smaller amounts. In it primary definition though, Big data refers to data sets that are too large or complex to be dealt with by traditional data-processing application software. Data with many fields (rows) offer greater statistical power, while data with higher complexity (more attributes or columns) may lead to a higher false discovery rate. Big data analysis challenges include capturing data, data storage, data analysis, search, sharing, transfer, visualization, querying, updating, information privacy, and data source. Big data was originally associated with three key concepts: ''volume'', ''variety'', and ''velocity''. The analysis of big data presents challenges in sampling, and thus previously allowing for only observations and sampling. ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Information Management
Information management (IM) concerns a cycle of organizational activity: the acquisition of information from one or more sources, the custodianship and the distribution of that information to those who need it, and its ultimate disposal through archiving or deletion. This cycle of information organisation involves a variety of stakeholders, including those who are responsible for assuring the quality, accessibility and utility of acquired information; those who are responsible for its safe storage and disposal; and those who need it for decision making. Stakeholders might have rights to originate, change, distribute or delete information according to organisational information management policies. Information management embraces all the generic concepts of management, including the planning, organizing, structuring, processing, controlling, evaluation and reporting of information activities, all of which is needed in order to meet the needs of those with organisational r ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Data Management
Data management comprises all disciplines related to handling data as a valuable resource. Concept The concept of data management arose in the 1980s as technology moved from sequential processing (first punched cards, then magnetic tape) to random access storage. Since it was now possible to store a discrete fact and quickly access it using random access disk technology, those suggesting that data management was more important than business process management used arguments such as "a customer's home address is stored in 75 (or some other large number) places in our computer systems." However, during this period, random access processing was not competitively fast, so those suggesting "process management" was more important than "data management" used batch processing time as their primary argument. As application software evolved into real-time, interactive usage, it became obvious that both management processes were important. If the data was not well defined, the data wo ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Prescriptive Analytics
Prescriptive analytics is a form of business analytics which suggests decision options for how to take advantage of a future opportunity or mitigate a future risk, and shows the implication of each decision option. It enables an enterprise to consider "the best course of action to take" in the light of information derived from descriptive and predictive analytics.Basu, Atanu''Five pillars of prescriptive analytics success'' ''Analytics'', March / April 2013, accessed 3 December 2022 Overview Prescriptive analytics is the third and final phase of business analytics, which also includes descriptive and predictive analytics.http://www.analytics-magazine.org/november-december-2010/54-the-analytics-journey Referred to as the "final frontier of analytic capabilities", prescriptive analytics entails the application of mathematical and computational sciences and suggests decision options for how to take advantage of the results of descriptive and predictive phases. The first stage of bus ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Predictive Analytics
Predictive analytics encompasses a variety of statistical techniques from data mining, predictive modeling, and machine learning that analyze current and historical facts to make predictions about future or otherwise unknown events. In business, predictive models exploit patterns found in historical and transactional data to identify risks and opportunities. Models capture relationships among many factors to allow assessment of risk or potential associated with a particular set of conditions, guiding decision-making for candidate transactions. The defining functional effect of these technical approaches is that predictive analytics provides a predictive score (probability) for each individual (customer, employee, healthcare patient, product SKU, vehicle, component, machine, or other organizational unit) in order to determine, inform, or influence organizational processes that pertain across large numbers of individuals, such as in marketing, credit risk assessment, fraud detecti ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Enterprise Information Management
Enterprise information management (EIM) is a business discipline specializing in providing solutions for optimal use of information within organizations, for instance to support decision-making processes or day-to-day operations that require the availability of knowledge. EIM It aims to overcome traditional/legacy IT-related barriers to managing information at an enterprise level. ThInternational Organization for Standardization(ISO) recognises information management as a separate function to information technology (IT/ICT). Enterprise Information Management practices are guided by thsuite of ISO Standardsthat are managed bISO Technical Committee 46(ISO/TC 46). EIM combines enterprise content management (ECM), business process management (BPM), customer experience management Customer experience (CX) is a totality of cognitive, affective, sensory, and behavioral consumer responses during all stages of the consumption process including pre-purchase, consumption, and post-purch ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Decision Management
Decision management, also known as enterprise decision management (EDM) or business decision management (BDM) entails all aspects of designing, building and managing the automated decision-making systems that an organization uses to manage its interactions with customers, employees and suppliers. Digital Revolution, Computerization has changed the way organizations are approaching their decision-making because it requires that they automate more decisions, to handle response times and unattended operation required by computerization, and because it has enabled "information-based decisions" – decisions based on analysis of historical behavioral data, prior decisions, and their outcomes. Overview Decision management was described in 2005 as an "emerging important discipline, due to an increasing need to automate high-volume decisions across the enterprise and to impart precision, consistency, and agility in the decision-making process". Decision management is implemented "via the use ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Data Science
Data science is an interdisciplinary field that uses scientific methods, processes, algorithms and systems to extract or extrapolate knowledge and insights from noisy, structured and unstructured data, and apply knowledge from data across a broad range of application domains. Data science is related to data mining, machine learning, big data, computational statistics and analytics. Data science is a "concept to unify statistics, data analysis, informatics, and their related methods" in order to "understand and analyse actual phenomena" with data. It uses techniques and theories drawn from many fields within the context of mathematics, statistics, computer science, information science, and domain knowledge. However, data science is different from computer science and information science. Turing Award winner Jim Gray imagined data science as a "fourth paradigm" of science ( empirical, theoretical, computational, and now data-driven) and asserted that "everything about sc ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Business Intelligence Tools
Business intelligence software is a type of application software designed to retrieve, analyze, transform and report data for business intelligence. The applications generally read data that has been previously stored, often - though not necessarily - in a data warehouse or data mart. History Development of business intelligence software The first comprehensive business intelligence systems were developed by IBM and Siebel (currently acquired by Oracle) in the period between 1970 and 1990. At the same time, small developer teams were emerging with attractive ideas, and pushing out some of the products companies still use nowadays. In 1988, specialists and vendors organized a Multiway Data Analysis Consortium in Rome, where they considered making data management and analytics more efficient, and foremost available to smaller and financially restricted businesses. By 2000, there were many professional reporting systems and analytic programs, some owned by top performing software pro ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
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