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Enterprise Data Management
Enterprise data management (EDM) is the ability of an organization to precisely define, easily integrate and effectively retrieve data for both internal applications and external communication. EDM focuses on the creation of accurate, consistent, and transparent content. EDM emphasizes data precision, granularity, and meaning and is concerned with how the content is integrated into business applications as well as how it is passed along from one business process to another. EDM arose to address circumstances where users within organizations independently source, model, manage and store data. Uncoordinated approaches by various segments of the organization can result in data conflicts and quality inconsistencies, lowering the trustworthiness of the data as it is used for operations and reporting. The goal of EDM is trust and confidence in data assets. Its components are: Strategy and governance EDM requires a strategic approach to choosing the right processes, technologies ...
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Business Application
Business software (or a business application) is any software or set of computer programs used by business users to perform various business functions. These business applications are used to increase productivity, measure productivity, and perform other business functions accurately. Overview Much business software is developed to meet the needs of a specific business, and therefore is not easily transferable to a different business environment, unless its nature and operation are identical. Due to the unique requirements of each business, off-the-shelf software is unlikely to completely address a company's needs. However, where an on-the-shelf solution is necessary, due to time or monetary considerations, some level of customization is likely to be required. Exceptions do exist, depending on the business in question, and thorough research is always required before committing to bespoke or off-the-shelf solutions. Some business applications are interactive, i.e., they have a gr ...
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Data Model
A data model is an abstract model that organizes elements of data and standardizes how they relate to one another and to the properties of real-world entities. For instance, a data model may specify that the data element representing a car be composed of a number of other elements which, in turn, represent the color and size of the car and define its owner. The term data model can refer to two distinct but closely related concepts. Sometimes it refers to an abstract formalization of the objects and relationships found in a particular application domain: for example the customers, products, and orders found in a manufacturing organization. At other times it refers to the set of concepts used in defining such formalizations: for example concepts such as entities, attributes, relations, or tables. So the "data model" of a banking application may be defined using the entity-relationship "data model". This article uses the term in both senses. A data model explicitly determines the ...
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Web Data Integration
Web data integration (WDI) is the process of aggregating and managing data from different websites into a single, homogeneous workflow. This process includes data access, transformation, mapping, quality assurance and fusion of data. Data that is sourced and structured from websites is referred to as " web data". WDI is an extension and specialization of data integration that views the web as a collection of heterogeneous databases. Data integration techniques in the context of the web, forms the foundation for businesses taking advantage of data available on the ever-increasing number of publicly-accessible websites. Corporate spending on this area amounted to about USD 2.5bn in 2017, and it is expected that by 2020 the market will reach almost USD 7bn. Sources Web data integration extends and specializes data integration to see the web as a collection of views of databases accessible over the web protocols, including, but not limited to: * Open data catalogs * Government data ...
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Master Data
Master data represents "data about the business entities that provide context for business transactions". The most commonly found categories of master data are parties (individuals and organisations, and their roles, such as customers, suppliers, employees), products, financial structures (such as ledgers and cost centers) and locational concepts. Master data should be distinguished from reference data. While both provide context for business transactions, reference data is concerned with classification and categorisation, while master data is concerned with business entities. Master data is, by its nature, almost always non-transactional in nature. There exist edge cases where an organization may need to treat certain transactional processes and operations as "master data". This arises, for example, where information about master data entities, such as customers or products, is only contained within transactional data such as orders and receipts and is not housed separately. ...
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Master Data Management
Master data management (MDM) is a technology-enabled discipline in which business and information technology work together to ensure the uniformity, accuracy, stewardship, semantic consistency and accountability of the enterprise's official shared master data assets. Drivers for master data management Organisations, or groups of organisations, may establish the need for master data management when they hold more than one copy of data about a business entity. Holding more than one copy of this master data inherently means that there is an inefficiency in maintaining a "single version of the truth" across all copies. Unless people, processes and technology are in place to ensure that the data values are kept aligned across all copies, it is almost inevitable that different versions of information about a business entity will be held. This causes inefficiencies in operational data use, and hinders the ability of organisations to report and analyze. At a basic level, master data ma ...
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Metadata
Metadata is "data that provides information about other data", but not the content of the data, such as the text of a message or the image itself. There are many distinct types of metadata, including: * Descriptive metadata – the descriptive information about a resource. It is used for discovery and identification. It includes elements such as title, abstract, author, and keywords. * Structural metadata – metadata about containers of data and indicates how compound objects are put together, for example, how pages are ordered to form chapters. It describes the types, versions, relationships, and other characteristics of digital materials. * Administrative metadata – the information to help manage a resource, like resource type, permissions, and when and how it was created. * Reference metadata – the information about the contents and quality of statistical data. * Statistical metadata – also called process data, may describe processes that collect, process, or produce st ...
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Data Mapping
In computing and data management, data mapping is the process of creating data element mappings between two distinct data models. Data mapping is used as a first step for a wide variety of data integration tasks, including: * Data transformation or data mediation between a data source and a destination * Identification of data relationships as part of data lineage analysis * Discovery of hidden sensitive data such as the last four digits of a social security number hidden in another user id as part of a data masking or de-identification project * Consolidation of multiple databases into a single database and identifying redundant columns of data for consolidation or elimination For example, a company that would like to transmit and receive purchases and invoices with other companies might use data mapping to create data maps from a company's data to standardized ANSI ASC X12 messages for items such as purchase orders and invoices. Standards X12 standards are generic Electro ...
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Stewardship
Stewardship is an ethical value that embodies the responsible planning and management of resources. The concepts of stewardship can be applied to the environment and nature, economics, health, property, information, theology, cultural resources etc. History of the term Stewardship was originally made up of the tasks of a domestic steward, from stiġ (''house'', ''hall'') and weard, (''ward'', ''guard'', ''guardian'', ''keeper''). Stewardship in the beginning referred to the household servant's duties for bringing food and drink to the castle's dining hall. Stewardship responsibilities were eventually expanded to include the domestic, service and management needs of the entire household. Commercial stewardship tends to the domestic and service requirements of passengers on ships, trains, airplanes or guests in restaurants. This concept of stewardship continues to be referenced within these specific categories. Stewardship is now generally recognized as the acceptance or ass ...
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Data Normalization
In mathematics and computer science, a canonical, normal, or standard form of a mathematical object is a standard way of presenting that object as a mathematical expression. Often, it is one which provides the simplest representation of an object and which allows it to be identified in a unique way. The distinction between "canonical" and "normal" forms varies from subfield to subfield. In most fields, a canonical form specifies a ''unique'' representation for every object, while a normal form simply specifies its form, without the requirement of uniqueness. The canonical form of a positive integer in decimal representation is a finite sequence of digits that does not begin with zero. More generally, for a class of objects on which an equivalence relation is defined, a canonical form consists in the choice of a specific object in each class. For example: *Jordan normal form is a canonical form for matrix similarity. *The row echelon form is a canonical form, when one conside ...
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Data Cleansing
Data cleansing or data cleaning is the process of detecting and correcting (or removing) corrupt or inaccurate records from a record set, table, or database and refers to identifying incomplete, incorrect, inaccurate or irrelevant parts of the data and then replacing, modifying, or deleting the dirty or coarse data. Data cleansing may be performed interactively with data wrangling tools, or as batch processing through scripting or a data quality firewall. After cleansing, a data set should be consistent with other similar data sets in the system. The inconsistencies detected or removed may have been originally caused by user entry errors, by corruption in transmission or storage, or by different data dictionary definitions of similar entities in different stores. Data cleaning differs from data validation in that validation almost invariably means data is rejected from the system at entry and is performed at the time of entry, rather than on batches of data. The actual process of ...
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Glossary
A glossary (from grc, γλῶσσα, ''glossa''; language, speech, wording) also known as a vocabulary or clavis, is an alphabetical list of Term (language), terms in a particular domain of knowledge with the definitions for those terms. Traditionally, a glossary appears at the end of a book and includes terms within that book that are either newly introduced, uncommon, or specialized. While glossaries are most commonly associated with non-fiction books, in some cases, fiction novels may come with a glossary for unfamiliar terms. A bilingual glossary is a list of terms in one language defined in a second language or Gloss (annotation), glossed by synonyms (or at least near-synonyms) in another language. In a general sense, a glossary contains explanations of concepts relevant to a certain field of study or action. In this sense, the term is related to the notion of ontology. Automatic methods have been also provided that transform a glossary into an ontology or a computational ...
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Change Management
Change management (sometimes abbreviated as CM) is a collective term for all approaches to prepare, support, and help individuals, teams, and organizations in making organizational change. It includes methods that redirect or redefine the use of resources, business process, budget allocations, or other modes of operation that significantly change a company or organization. Organizational change management (OCM) considers the full organization and what needs to change, while change management may be used solely to refer to how people and teams are affected by such organizational transition. It deals with many different disciplines, from behavioral and social sciences to information technology and business solutions. As change management becomes more necessary in the business cycle of organizations, it is beginning to be taught as its own academic discipline at universities. There are a growing number of universities with research units dedicated to the study of organizational cha ...
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