Enterprise Data - Revision history

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← Older revision		Revision as of 16:46, 2 February 2023
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	*Reference Data: It is a standardized subset of master data which is less volatile than elements of master data. As the name suggests, it is used as a reference across the organization to maintain common standards. For example, a list of countries and corresponding states and cities is reference data.		*Reference Data: It is a standardized subset of master data which is less volatile than elements of master data. As the name suggests, it is used as a reference across the organization to maintain common standards. For example, a list of countries and corresponding states and cities is reference data.
	*Metadata: It provides descriptive, structural information about other data related to business activities. For example, the Name of a person may have 3 components First Name (mandatory), Last Name (mandatory), and Middle Name (optional). This information is metadata about the name of a person.		*Metadata: It provides descriptive, structural information about other data related to business activities. For example, the Name of a person may have 3 components First Name (mandatory), Last Name (mandatory), and Middle Name (optional). This information is metadata about the name of a person.
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		+	== Model Enterprise Data<ref>[https://www.cms.gov/Research-Statistics-Data-and-Systems/CMS-Information-Technology/DataAdmin/downloads/enterprisedataplanning.pdf Model Enterprise Data]</ref> ==
		+	This process creates the Enterprise Data Model (EDM), which is a conceptual or semantic data model for business use. Its objective is to synthesize common entity meanings across agency business functions and to move the agency toward interoperable data architecture through stable, non-redundant shared data and reusable information exchange structures.
		+	*The Enterprise Data Model is a “living” model. As such, it provides reusable data artifacts to new projects and, during the course of project logical data modeling, is updated with new and discovered data artifacts whose make-up suggests their potential for reuse across multiple business-line information processes.
		+	*The Enterprise Data Model serves as the reference for the ideal description and security designation of important business data entities.
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		+	The following processes occur during enterprise data design activities:
		+	*Define Enterprise Business Entities
		+	*Define Entity Relationships
		+	*Analyze Entity States
		+	*Determine Entity Identifiers
		+	*Define Enterprise Attributes

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 Enterprise data can have a significant impact on the financial well-being of an organization. Enterprises often implement security measures such as encryption technologies in order to protect their valuable enterprise data from potential threats like cyberattacks or accidental loss due to human error. Enterprise data loss can result in serious consequences such as loss of revenue, customer trust, or even legal action against the organization.
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 *Reference Data: It is a standardized subset of master data which is less volatile than elements of master data. As the name suggests, it is used as a reference across the organization to maintain common standards. For example, a list of countries and corresponding states and cities is reference data.
 *Metadata: It provides descriptive, structural information about other data related to business activities. For example, the Name of a person may have 3 components First Name (mandatory), Last Name (mandatory), and Middle Name (optional). This information is metadata about the name of a person.
 == See Also ==
-[[Enterprise Data Integration (EDI)]]
+[[Enterprise Data Management (EDM)]]

← Older revision		Revision as of 15:36, 2 February 2023
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	Enterprise Data can be used to gain insights into operations, network security threats and risks, strategic initiatives such as sales reporting or customer relationship management (CRM), IoT sensor information, or weather trends. It is essential for businesses to understand their enterprise data in order to make informed decisions that will benefit their organization. Data modeling techniques are often used by enterprises when dealing with large amounts of enterprise data in order to make sense of it all more quickly and efficiently.		Enterprise Data can be used to gain insights into operations, network security threats and risks, strategic initiatives such as sales reporting or customer relationship management (CRM), IoT sensor information, or weather trends. It is essential for businesses to understand their enterprise data in order to make informed decisions that will benefit their organization. Data modeling techniques are often used by enterprises when dealing with large amounts of enterprise data in order to make sense of it all more quickly and efficiently.

−	Enterprise data can have a significant impact on the financial well-being of an organization. Enterprises often implement security measures such as encryption technologies in order to protect their valuable enterprise data from potential threats like cyberattacks or accidental loss due to human error. Enterprise data loss can result in serious consequences such as loss of revenue, customer trust or even legal action against the organization.	+	Enterprise data can have a significant impact on the financial well-being of an organization. Enterprises often implement security measures such as encryption technologies in order to protect their valuable enterprise data from potential threats like cyberattacks or accidental loss due to human error. Enterprise data loss can result in serious consequences such as loss of revenue, customer trust, or even legal action against the organization.
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		+	== Characteristics of Enterprise Data<ref>[https://www.techopedia.com/definition/28048/enterprise-data Characteristics of Enterprise Data]</ref> ==
		+	A key asset component, enterprise data is subdivided into internal and external data categories, which are classified according to organizational processes, resources, and/or standards.
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		+	There is no precise standard for what defines enterprise data from small or medium-sized businesses. However, once an organization gets to the point where it has many operating units in different locations, its needs clearly get more complicated as compared to the one-location business with a single IT department.
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		+	Enterprise Data characteristics include:
		+	*Integration: Ensures a single consistent version of enterprise data for sharing throughout an organization
		+	*Minimized redundancy, disparity, and errors: As enterprise data is shared by all of an organization's users, data redundancy and disparity must be minimized. Data modeling and management strategies are directed toward these requirements.
		+	*Quality: To ensure data quality, enterprise data must follow organizational or other identified standards for varying internal and external data components.
		+	*Scalability: Data must be scalable, flexible, and robust to meet different enterprise requirements.
		+	*Security: Enterprise data must be secured via authorized and controlled access.
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		+	== Types of Enterprise Data<ref>[https://medium.com/data-strategy-and-management/identifying-issues-with-enterprise-data-management-chapter-0-1cf3ec6b5757 Types of Enterprise Data]</ref> ==
		+	To resolve data issues, it is important to understand different types of enterprise data. Its importance lies in the fact that these different types of data serve their specific purpose and there are different approaches to managing them. Another important reason to understand different types of Enterprise Data is that it helps in adopting a modular and agile approach to fix data issues.
		+	*Master Data: It represents non-transactional information about business objects. It is a consistent and uniform set of identifiers that describe core entities of the business operations. Usually, master data defines unique entities in Products, Customers, Vendors, etc. domains. For example, a specific vendor which has a standardized definition and properties is a master data whose unique ID is used in transactional systems for business activities.
		+	*Transactional Data: It describes core business activities and transactions. Transactional data may contain information about your procurement, production, selling, etc. activities. The description of a sale of a product is transactional data.
		+	*Analytical Data: Analytical (or reporting) data is an aggregated compilation of transactional data which can be sliced and diced with the help of master data for business analysis.
		+	*Reference Data: It is a standardized subset of master data which is less volatile than elements of master data. As the name suggests, it is used as a reference across the organization to maintain common standards. For example, a list of countries and corresponding states and cities is reference data.
		+	*Metadata: It provides descriptive, structural information about other data related to business activities. For example, the Name of a person may have 3 components First Name (mandatory), Last Name (mandatory), and Middle Name (optional). This information is metadata about the name of a person.
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		+	== See Also ==
		+	[[Enterprise Data Integration (EDI)]]
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		+	== References ==
		+	<references/>
		+	__NOTOC__

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−	'''~~Content Coming Soon~~'''	+	== What is Enterprise Data? ==
		+	'''Enterprise Data''' is digital information that flows through a company's systems, including both structured data (such as spreadsheets and databases) and unstructured data (images, videos, graphics and is shared by the users of an organization, across departments and/or geographic regions.
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		+	Enterprise Data can be used to gain insights into operations, network security threats and risks, strategic initiatives such as sales reporting or customer relationship management (CRM), IoT sensor information, or weather trends. It is essential for businesses to understand their enterprise data in order to make informed decisions that will benefit their organization. Data modeling techniques are often used by enterprises when dealing with large amounts of enterprise data in order to make sense of it all more quickly and efficiently.
		+
		+	Enterprise data can have a significant impact on the financial well-being of an organization. Enterprises often implement security measures such as encryption technologies in order to protect their valuable enterprise data from potential threats like cyberattacks or accidental loss due to human error. Enterprise data loss can result in serious consequences such as loss of revenue, customer trust or even legal action against the organization.