Master Data - Revision history

User: The LinkTitles extension automatically added links to existing pages (https://github.com/bovender/LinkTitles).

2021-02-06T17:01:51Z

The LinkTitles extension automatically added links to existing pages (https://github.com/bovender/LinkTitles).

User: Master data refers to data units that are non-transactional, top level and relational business entities or elements that are joinable in observable ways. An organization may use master data on more than one platform or across a variety of software program

2019-01-06T02:39:27Z

Master data refers to data units that are non-transactional, top level and relational business entities or elements that are joinable in observable ways. An organization may use master data on more than one platform or across a variety of software program

User: Created page with "'''Content Coming Soon'''"

2018-12-19T23:59:43Z

Created page with "'''Content Coming Soon'''"

New page

'''Content Coming Soon'''

@@ Line 1: / Line 1: @@
-Master data refers to data units that are non-transactional, top level and relational business entities or elements that are joinable in observable ways. An organization may use master data on more than one platform or across a variety of software programs or technologies.<ref>Defining Master Data [https://www.techopedia.com/definition/839/master-data Techopedia]</ref>
+Master [[data]] refers to data units that are non-transactional, top level and relational [[business]] entities or elements that are joinable in observable ways. An [[organization]] may use master data on more than one platform or across a variety of [[software]] programs or technologies.<ref>Defining Master Data [https://www.techopedia.com/definition/839/master-data Techopedia]</ref>
 '''Master Data - Other Definitions'''<br />
 *Gartner defines Master Data as “Master data is the consistent and uniform set of identifiers and extended attributes that describes the core entities of the enterprise including customers, prospects, citizens, suppliers, sites, hierarchies and chart of accounts.”
-*Master data is having “consistent definitions of business entities (e.g., customer or product) and data about them across multiple IT systems and possibly beyond the enterprise to partnering businesses.” Phillip Russom, TDWI
+*Master data is having “consistent definitions of business entities (e.g., [[customer]] or [[product]]) and data about them across multiple IT systems and possibly beyond the enterprise to partnering businesses.” Phillip Russom, TDWI
 No matter where it comes from, the definition of master data has several common themes:
 *Master data is critical for operational and analytical business decision-making.
 *Master data is scattered throughout the enterprise.
-*Master data establishes a standard definition for business-critical data that is shared across the enterprise and collectively represents a “single source of truth.”<ref>The common theme in Master Data Definitions [https://www.semarchy.com/en/overview/what-is-master-data/ Semarchy]</ref>
+*Master data establishes a [[standard]] definition for business-critical data that is shared across the enterprise and collectively represents a “single source of truth.”<ref>The common theme in Master Data Definitions [https://www.semarchy.com/en/overview/what-is-master-data/ Semarchy]</ref>
@@ Line 17: / Line 17: @@
 Deciding what information to share as master data typically involves several considerations:
-*Costs: Information such as market data that is purchased from data vendors may have a significant cost. As such, organizations typically want to purchase it and use it efficiently.
+*Costs: Information such as [[market]] data that is purchased from data vendors may have a significant cost. As such, organizations typically want to purchase it and use it efficiently.
-*Carnality: Data with a large number of elements. For example, reference data with millions of rows may have more value as master data than reference data with four rows.
+*Carnality: Data with a large number of elements. For example, reference data with millions of rows may have more [[value]] as master data than reference data with four rows.
 *Life Cycle: Data that remains valuable for a significant period of time. For example, customers records may be relevant for years or decades.
-*Volatility: Data that doesn't change at all may not require a master data management solution. Such data can be defined as constants in a variety of systems.
+*Volatility: Data that doesn't change at all may not require a master data [[management]] solution. Such data can be defined as constants in a variety of systems.
-*Value: Data that is critical to business strategy and operations.
+*Value: Data that is critical to business [[strategy]] and operations.
 *Reuse: Data that is used in many places has more value as master data. Typical candidates for master data include customer, product and market data that tend to get used by a large number of systems and analytical tools.<ref>What Information is shared as Master Data? [https://simplicable.com/new/types-of-master-data Simplicable]</ref>
@@ Line 32: / Line 32: @@
-'''Master Data versus Reference Data'''<ref>Master Data versus Reference Data [http://www.b-eye-network.com/blogs/white/archives/2007/11/master_data_ver.php BeyeNetwork]</ref><br />
+'''Master Data versus [[Reference Data]]'''<ref>Master Data versus Reference Data [http://www.b-eye-network.com/blogs/white/archives/2007/11/master_data_ver.php BeyeNetwork]</ref><br />
-Master data is defined as data about the key business entities of an organization. Examples include customer, product, organizational structure, and chart of accounts. A common question about master data is, What is the difference between master data and reference data? Some people take the position that they are the same thing, but it can be argued that not all reference data is master data. For example, lookup and code tables that are used to encode information, such as state names and order codes, are not strictly master data tables. The dividing line between master data and reference data is not always clear cut. One solution is to break master data into two types: master reference data and master business entity data. Master reference data has well defined and simple data structures, has simple keys and governance rules, is often standardized (US state codes, for example), involves only a few applications, and is reasonably stable. Master business entity data, such as customer, on the other hand, is usually ill-defined, has complex data structures and relationships, requires compound and intelligent keys and complex governance rules, is not usually standardized, involves many business processes, and changes frequently. Does this distinction really matter? When developing data quality management and master data management systems it can do. Cleaning and managing master reference status is a reasonable easy job. The opposite is true for master business entity data.
+Master data is defined as data about the key business entities of an organization. Examples include customer, product, organizational structure, and chart of accounts. A common question about master data is, What is the difference between master data and reference data? Some people take the position that they are the same thing, but it can be argued that not all reference data is master data. For example, lookup and code tables that are used to encode information, such as state names and order codes, are not strictly master data tables. The dividing line between master data and reference data is not always clear cut. One solution is to break master data into two types: master reference data and master business entity data. Master reference data has well defined and simple data structures, has simple keys and [[governance]] rules, is often standardized (US state codes, for example), involves only a few applications, and is reasonably stable. Master business entity data, such as customer, on the other hand, is usually ill-defined, has complex data structures and relationships, requires compound and intelligent keys and complex governance rules, is not usually standardized, involves many business processes, and changes frequently. Does this distinction really matter? When developing data [[quality]] management and master data management systems it can do. Cleaning and managing master reference status is a reasonable easy job. The opposite is true for master business entity data.
-'''Master Data Governance'''<br />
+'''Master [[Data Governance]]'''<br />
-Master Data governance is the overall management of the quality, integrity, availability, usability and security of the master data of the organization. The governance also refers to the consistent management of data completeness, accuracy and relevancy of the data including business process management and risk management surrounding the handling of data in an organization. The governance strategy enables to gain visibility into your data assets and get early warning of potential data breaches
+Master Data governance is the overall management of the quality, integrity, availability, [[usability]] and security of the master data of the organization. The governance also refers to the consistent management of data completeness, accuracy and relevancy of the data including business [[process]] management and [[risk]] management surrounding the handling of data in an organization. The governance strategy enables to gain visibility into your data assets and get early warning of potential data breaches
@@ Line 45: / Line 45: @@
 '''Master Data Purification'''<ref>Master Data Purification [http://www.si-am.co.za/si-am/master-data-manage/ SIAM]</ref>br />
-There are usually numerous reasons for Companies to embarked on a Master Data purification journey, the main goals of such an exercise are to improve business systems, business processes as well as the customer experience.
+There are usually numerous reasons for Companies to embarked on a Master Data purification journey, the main [[goals]] of such an exercise are to improve business systems, business processes as well as the customer experience.
 *Targeting Pain Points
 **Fragmented customer data impacting the customer experience.
 **Inconsistencies in disparate systems as a result of acquisitions that cause governance challenges.
-**Limited self-service capability that impact the business users in becoming efficient.
+**Limited self-[[service]] capability that [[impact]] the business users in becoming efficient.
 *Best Practices
 **Clearly defining the business problem.
-**Getting leadership and ongoing commitment from business.
+**Getting [[leadership]] and ongoing commitment from business.
 **Understanding the data issues and knowing how they are affecting the company.
 **Defining data ownership, governance and steering data stewards.

← Older revision		Revision as of 02:39, 6 January 2019
Line 1:		Line 1:
−	'''~~Content Coming Soon~~'''	+	Master data refers to data units that are non-transactional, top level and relational business entities or elements that are joinable in observable ways. An organization may use master data on more than one platform or across a variety of software programs or technologies.<ref>Defining Master Data [https://www.techopedia.com/definition/839/master-data Techopedia]</ref>
		+
		+
		+	'''Master Data - Other Definitions'''<br />
		+	*Gartner defines Master Data as “Master data is the consistent and uniform set of identifiers and extended attributes that describes the core entities of the enterprise including customers, prospects, citizens, suppliers, sites, hierarchies and chart of accounts.”
		+	*Master data is having “consistent definitions of business entities (e.g., customer or product) and data about them across multiple IT systems and possibly beyond the enterprise to partnering businesses.” Phillip Russom, TDWI
		+
		+	No matter where it comes from, the definition of master data has several common themes:
		+	*Master data is critical for operational and analytical business decision-making.
		+	*Master data is scattered throughout the enterprise.
		+	*Master data establishes a standard definition for business-critical data that is shared across the enterprise and collectively represents a “single source of truth.”<ref>The common theme in Master Data Definitions [https://www.semarchy.com/en/overview/what-is-master-data/ Semarchy]</ref>
		+
		+
		+	[[File:Master Data.jpg\|400px\|Master Data]]<br />
		+	source: [https://www.winshuttle.com/blog/master-data-asset/ WinShuttle]
		+
		+
		+	Deciding what information to share as master data typically involves several considerations:
		+	*Costs: Information such as market data that is purchased from data vendors may have a significant cost. As such, organizations typically want to purchase it and use it efficiently.
		+	*Carnality: Data with a large number of elements. For example, reference data with millions of rows may have more value as master data than reference data with four rows.
		+	*Life Cycle: Data that remains valuable for a significant period of time. For example, customers records may be relevant for years or decades.
		+	*Volatility: Data that doesn't change at all may not require a master data management solution. Such data can be defined as constants in a variety of systems.
		+	*Value: Data that is critical to business strategy and operations.
		+	*Reuse: Data that is used in many places has more value as master data. Typical candidates for master data include customer, product and market data that tend to get used by a large number of systems and analytical tools.<ref>What Information is shared as Master Data? [https://simplicable.com/new/types-of-master-data Simplicable]</ref>
		+
		+
		+	'''Types of Data'''<ref>Types of Data [https://en.wikipedia.org/wiki/Master_data Wikipedia]</ref><br />
		+	Master data is a single source of common business data used across multiple systems, applications, and/or processes. Yet other data types and distinctions exist:
		+	*Reference data represents the set of permissible values to be used by other (master or transaction) data fields. Reference data classifies and describes data and normally changes slowly, reflecting changes in the modes of operation of the business, rather than changing in the normal course of business.
		+	*Enterprise master data represents the single source of basic business data used across the entire enterprise, regardless of location.
		+	*Market master data represents the single source of basic business data used across a marketplace, regardless of location. This stands in contrast from enterprise master data in that it can be used by multiple enterprises within a value chain, facilitating "integration of multiple data sources and literally (putting) everyone in the market on the same page." An example of market master data is the UPC (Universal Product Code) found on consumer products.
		+
		+
		+	'''Master Data versus Reference Data'''<ref>Master Data versus Reference Data [http://www.b-eye-network.com/blogs/white/archives/2007/11/master_data_ver.php BeyeNetwork]</ref><br />
		+	Master data is defined as data about the key business entities of an organization. Examples include customer, product, organizational structure, and chart of accounts. A common question about master data is, What is the difference between master data and reference data? Some people take the position that they are the same thing, but it can be argued that not all reference data is master data. For example, lookup and code tables that are used to encode information, such as state names and order codes, are not strictly master data tables. The dividing line between master data and reference data is not always clear cut. One solution is to break master data into two types: master reference data and master business entity data. Master reference data has well defined and simple data structures, has simple keys and governance rules, is often standardized (US state codes, for example), involves only a few applications, and is reasonably stable. Master business entity data, such as customer, on the other hand, is usually ill-defined, has complex data structures and relationships, requires compound and intelligent keys and complex governance rules, is not usually standardized, involves many business processes, and changes frequently. Does this distinction really matter? When developing data quality management and master data management systems it can do. Cleaning and managing master reference status is a reasonable easy job. The opposite is true for master business entity data.
		+
		+
		+	'''Master Data Governance'''<br />
		+	Master Data governance is the overall management of the quality, integrity, availability, usability and security of the master data of the organization. The governance also refers to the consistent management of data completeness, accuracy and relevancy of the data including business process management and risk management surrounding the handling of data in an organization. The governance strategy enables to gain visibility into your data assets and get early warning of potential data breaches
		+
		+
		+	[[File:Master Data1.jpg\|400px\|Master Data Governance]]<br />
		+	source: [http://datapals.com/services/master-data-and-governance/ DataPals]
		+
		+
		+	'''Master Data Purification'''<ref>Master Data Purification [http://www.si-am.co.za/si-am/master-data-manage/ SIAM]</ref>br />
		+	There are usually numerous reasons for Companies to embarked on a Master Data purification journey, the main goals of such an exercise are to improve business systems, business processes as well as the customer experience.
		+	*Targeting Pain Points
		+	**Fragmented customer data impacting the customer experience.
		+	**Inconsistencies in disparate systems as a result of acquisitions that cause governance challenges.
		+	**Limited self-service capability that impact the business users in becoming efficient.
		+	*Best Practices
		+	**Clearly defining the business problem.
		+	**Getting leadership and ongoing commitment from business.
		+	**Understanding the data issues and knowing how they are affecting the company.
		+	**Defining data ownership, governance and steering data stewards.
		+	**Starting small, celebrating early successes and incrementally delivering solution with strong focus on business value.
		+	**Continuously measuring and monitoring data quality.
		+	**Following best practices and ensuring that the program addresses the business problem.
		+
		+
		+	===See Also===
		+	[[Enterprise_Data_Warehouse_(EDW)\|Enterprise Data Warehouse (EDW)]]<br />
		+	[[Data_Cleansing\|Data Cleansing]]<br />
		+	[[Data_Management\|Data Management]]<br />
		+	[[Predictive_Analytics\|Predictive Analytics]]<br />
		+	[[Customer_Data_Management_(CDM)\|Customer Data Management (CDM)]]<br />
		+	[[Big_Data\|Big Data]]<br />
		+	[[Business_Intelligence\|Business Intelligence]]<br />
		+	[[Data_Analysis\|Data Analysis]]<br />
		+	[[Data_Analytics\|Data Analytics]]<br />
		+	[[Data_Cleansing\|Data Cleansing]]<br />
		+	[[Data_Mining\|Data Mining]]<br />
		+	[[Data_Warehouse\|Data Warehouse]]<br />
		+	[[Enterprise_Data_Warehouse_(EDW)\|Enterprise Data Warehouse (EDW)]]<br />
		+	[[Metadata_Management\|Metadata Management]]<br />
		+	[[Master_Data_Management_(MDM)\|Master Data Management (MDM)]]
		+
		+
		+	===References===
		+	<references/>
		+
		+
		+	===Further Reading===
		+	*MDM 101 - What Is Master Data [https://www.profisee.com/resources/our-blog/articleid/138/master-data-examples Profisee]
		+	*Master Data in the Cloud [http://blogs.adatis.co.uk/matthow/post/Master-Data-in-the-Cloud Matt How]
		+	*Three Common Master Data Problems You Never Knew You Had [https://www.designmind.com/common-master-data-problems/ DesignMind]