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MDM matching algorithms benefit from all of the following data characteristics except for which of the following?
Distinctiveness across the population of data
Low number of common data points
High level of comparability of the data elements
Structural heterogeneity of data elements
High validity of the data
MDM matching algorithms benefit from various data characteristics but do not benefit from "Structural heterogeneity of data elements."
Matching Algorithms:These are used in MDM to identify and link data records that refer to the same entity across different systems.
Data Characteristics:
Distinctiveness:Helps in accurately matching records.
Common Data Points:Aids in the comparison process.
Comparability:Facilitates effective matching.
Validity:Ensures the data is accurate and reliable.
Structural Heterogeneity:Different structures can complicate the matching process, making it harder to align data.
References:
DAMA-DMBOK: Data Management Body of Knowledge, 2nd Edition.
CDMP Study Guide
The MDM process step responsible for determining whether two references to real world objects refer to the same object or different objects is known as:
Data Model Management
Data Acquisition
Entity Resolution
Data Sharing & Stewardship
Data Validation. Standardization, and Enrichment
Entity resolution is a critical step in the MDM process that identifies whether different data records refer to the same real-world entity. This ensures that each entity is uniquely represented within the master data repository.
Data Model Management:
Focuses on defining and maintaining data models that describe the structure, relationships, and constraints of the data.
Data Acquisition:
Involves gathering and bringing data into the MDM system but does not deal with resolving entities.
Entity Resolution:
This process involves matching and linking records from different sources that refer to the same entity. Techniques such as deterministic matching (based on exact matches) and probabilistic matching (based on similarity scores) are used.
Entity resolution helps in deduplication and ensuring a single, unified view of each entity within the MDM system.
Data Sharing & Stewardship:
Focuses on managing data access and ensuring that data is shared responsibly and accurately.
Data Validation, Standardization, and Enrichment:
Ensures data quality by validating, standardizing, and enriching data but does not directly address entity resolution.
The MDM program's governance scope should include:
Internal data subscribers to the master data
Master Data authored in the MDM user interface
External third parties using Master Data
Data sources of the master data Q All of the above
The governance scope of an MDM program should be comprehensive, encompassing all aspects and stakeholders involved in the management and usage of master data.
Internal Data Subscribers to the Master Data:
Internal users and systems that consume master data must be governed to ensure they have access to accurate and consistent data.
Master Data Authored in the MDM User Interface:
Governance should include the processes and controls around how master data is created and maintained through the MDM interface.
External Third Parties Using Master Data:
External parties that use or access master data must be governed to ensure data security, privacy, and compliance with regulations.
Data Sources of the Master Data:
The sources from which master data is derived must be governed to ensure data quality, integrity, and consistency.
Which is NOT considered a type of Master Data relationship?
Customer Household
Survivorship
Fixed-Level Hierarchy
Ragged-Level Hierarchy
Grouping based on common criteria
Master Data relationships define how different master data entities are related to each other within an organization. These relationships are crucial for understanding and managing the dataeffectively. The types of master data relationships generally include hierarchies, groupings, and associations that help in organizing and categorizing the data.
Customer Household:
This refers to grouping individual customers into a single household entity. It is commonly used in consumer industries to understand the relationships and dynamics within a household.
Fixed-Level Hierarchy:
A hierarchy with a predetermined number of levels. Each level has a specific position and relationship to other levels, such as organizational hierarchies or product categorization.
Ragged-Level Hierarchy:
Similar to fixed-level hierarchies, but with varying levels of depth. It accommodates entities that may not fit neatly into a fixed-level structure, providing flexibility in the hierarchy.
Grouping based on common criteria:
This involves creating groups or segments of data based on shared attributes or criteria. For example, grouping products by category or customers by region.
Survivorship (NOT a relationship):
Survivorship pertains to the process of determining the most accurate and relevant data when multiple records exist for the same entity. It is a data quality and management process, not a type of relationship.
Which of the following is NOT ,1 characteristic of n deterministic matching algorithm?
Is better suited when there is no great consequence to an error in matching
Is not highly dependent on the quality of the data being matched
Has a discrete all or nothing outcome
Matches exact character to character of one or more fields
All identifiersbeing matched have equal weight
Deterministic matching algorithms rely on exact matches between data fields to determine if records are the same. These algorithms require high-quality data because any discrepancy, such as typographical errors or variations in data entry, can prevent a match.
Characteristics of deterministic matching:
It has a discrete all or nothing outcome (C).
It matches exact character to character of one or more fields (D).
All identifiers being matched have equal weight (E).
Since deterministic matching is highly dependent on the quality of the data being matched, option B is incorrect.
References:
DAMA-DMBOK: Data Management Body of Knowledge (2nd Edition), Chapter 11: Reference and Master Data Management.
"Master Data Management and Data Governance" by Alex Berson and Larry Dubov.
MDM is a lifecycle management process that includes the following activities with the exception of which activity?
Provisioning of access to trusted data across applications, either through direct reads, data services, or by replication feeds to transactional, warehousing or analytical data stores
Identifying multiple instances of the same entity represented within and across data sources: building and maintaining identifiers and cross-references to enable information integration
Ensuring effective and efficient retrieval and use of data and information by ETL logic
Enforcing the use of Master Data values within the organization
Identifying improperly matched or merged instances and ensuring they are resolved and correctly associated with identifiers
MDM (Master Data Management) is a lifecycle management process that includes various activities to ensure the quality, consistency, and accessibility of master data across an organization. These activities include:
Provisioning of Access: Ensuring that trusted master data is accessible across applications through various methods such as direct reads, data services, or replication feeds.
Identifying Multiple Instances: Detecting and managing multiple representations of the same entity within and across data sources. This involves creating and maintaining identifiers and cross-references for integration.
Enforcing Use of Master Data: Ensuring that the organization consistently uses master data values in processes and applications.
Resolving Improper Matches: Identifying and resolving improperly matched or merged data instances to maintain data integrity.
The activity of "Ensuring effective and efficient retrieval and use of data and information by ETL logic" (C) is not specific to MDM. While ETL (Extract, Transform, Load) processes are crucial for data integration and warehousing, they are not a core activity unique to the MDM lifecycle.
References:
DAMA-DMBOK: Data Management Body of Knowledge, 2nd Edition.
"Master Data Management and Data Governance" by Alex Berson and Larry Dubov.
Which of these metrics can be used to measure metadata documentation quality?
Random survey based on Enterprise definition of quality
Currency of metadata in the repository
Collision Logic on two sources measuring how much they match
All of these
Percentage of attributes that have definitions
Measuring metadata documentation quality involves several metrics that collectively provide a comprehensive view of the quality and effectiveness of metadata management practices.
Random Survey based on Enterprise Definition of Quality:
Conducting surveys among data users to gather feedback on the perceived quality of metadata documentation. This helps in understanding user satisfaction and identifying areas for improvement.
Currency of Metadata in the Repository:
Ensuring that metadata is up-to-date and accurately reflects the current state of the data. This is crucial for maintaining the relevance and usefulness of metadata.
Collision Logic on Two Sources Measuring How Much They Match:
Comparing metadata from different sources to identify discrepancies and ensure consistency. This metric helps in assessing the alignment and accuracy of metadata across systems.
Percentage of Attributes that have Definitions:
Measuring the completeness of metadata by checking the percentage of attributes that have well-defined descriptions. This ensures that all data elements are clearly documented and understood.
Managing Master Data involves:
Managing transaction data
Managing process models
Managing database keys
Managing structured and unstructured data
Managing security risks
Managing Master Data involves several key activities, primarily focusing on:
Structured and Unstructured Data:
Structured Data: Managing well-defined data types, such as relational databases, where data is organized into tables and fields.
Unstructured Data: Handling data that does not have a predefined format or structure, such as emails, documents, and multimedia files.
Comprehensive Management:
Data Integration: Ensuring that data from various sources, both structured and unstructured, is integrated into the master data repository.
Data Quality: Implementing processes and tools to maintain high data quality for both structured and unstructured data.
References:
Data Management Body of Knowledge (DMBOK), Chapter 7: Master Data Management
DAMA International, "The DAMA Guide to the Data Management Body of Knowledge (DMBOK)"
MOM Harmonization ensures that the data changes of one application:
Are synchronized with all other applications who depend on that data
Are recorded in the repository or data dictionary
Agree with the overall MDM architecture
include changes to the configuration of the database as well as the data
Has a data steward to preview the data for quality
Master Data Management (MDM) Harmonization ensures that the data changes of one application are synchronized with all other applications that depend on that data.
MDM Harmonization Definition:This process involves aligning and reconciling data from different sources to ensure consistency and accuracy across the enterprise.
Synchronization:Ensuring that changes in one application are reflected across all dependent applications prevents data inconsistencies and maintains data integrity.
References:
DAMA-DMBOK: Data Management Body of Knowledge, 2nd Edition.
CDMP Study Guide
International Classification of Diseases (ICD) codes are an example of:
Industry Reference Data
None of these
Geographic Reference Data
Computational Reference Data
Internal Reference Data
International Classification of Diseases (ICD) codes are a type of industry reference data.
ICD Codes:
Developed by the World Health Organization (WHO), ICD codes are used globally to classify and code all diagnoses, symptoms, and procedures recorded in conjunction with hospital care.
They are essential for health care management, epidemiology, and clinical purposes.
Industry Reference Data:
Industry reference data pertains to standardized data used within a particular industry to ensure consistency, accuracy, and interoperability.
ICD codes fall into this category as they are standardized across the healthcare industry, facilitating uniformity in data reporting and analysis.
Other Options:
Geographic Reference Data:Includes data like country codes, region codes, and GPS coordinates.
Computational Reference Data:Used in computational processes and algorithms.
Internal Reference Data:Data used internally within an organization that is not standardized across industries.
ISO 8000 is a Master Data international standard tor what purpose?
Provides a standard format for defining a model for a data dictionary
Provide guidance only to the Buy side of the supply chain
To replace the ISO 9000 standard
Define and measure data quality
Defines a format to exchange data between parties
ISO 8000 is an international standard focused on data quality and information exchange. Its primary purpose is to define and measure the quality of data, ensuring that it meets the requirements for completeness, accuracy, and consistency. The standard provides guidelines for data quality management, including requirements for data governance, data quality metrics, and procedures for improving data quality over time. ISO 8000 is not meant to replace ISO 9000, which is focused on quality management systems, but to complement it by addressing data quality specifically.
References:
ISO 8000: Overview and Benefits of ISO 8000, International Organization for Standardization (ISO)
DAMA-DMBOK2 Guide: Chapter 12 – Data Quality Management
A 'Curation Zone' is a data architecture component used to:
Perform advanced analytic
Ingest raw source system data
Validate source system content
Share reference data
Semantically formalize source system content
A 'Curation Zone' is a data architecture component used to semantically formalize source system content. This involves:
Data Curation: The process of organizing, integrating, and enriching raw data to make it meaningful and useful.
Semantic Formalization: Applying semantic models, ontologies, and metadata to standardize and contextualize the data.
Data Quality Enhancement: Ensuring the data meets quality standards through cleansing and validation processes.
Metadata Management: Capturing and managing metadata to provide context and meaning to the data.
The curation zone plays a critical role in transforming raw data into high-quality, semantically enriched information that can be effectively used for analysis, decision-making, and operational processes.
References:
DAMA-DMBOK: Data Management Body of Knowledge, 2nd Edition.
"Data Governance: How to Design, Deploy, and Sustain an Effective Data Governance Program" by John Ladley.
Which of the following isNOT part of MDM Lifecycle Management?
Establishing recovery and backup rules
Reconciling and consolidating data
Identifying multiple instances of the same entity
Identifying improperly matched or merged instances of data
Maintaining cross-references to enable information integration
Master Data Management (MDM) lifecycle management encompasses the processes and practices involved in managing master data throughout its lifecycle, from creation to retirement. It ensures that master data remains accurate, consistent, and usable.
Reconciling and Consolidating Data:
This process involves merging data from multiple sources to create a single, unified view of each master data entity.
It ensures that duplicate records are identified and consolidated, maintaining data consistency.
Identifying Multiple Instances of the Same Entity:
This involves detecting and resolving duplicate records to ensure that each master data entity is uniquely represented.
Tools and algorithms are used to identify potential duplicates based on matching criteria.
Identifying Improperly Matched or Merged Instances of Data:
This step involves reviewing and correcting any errors that occurred during the matching or merging process.
Ensures that data integrity is maintained and that merged records accurately represent the underlying entities.
Maintaining Cross-References to Enable Information Integration:
Cross-references link related data entities across different systems, enabling seamless information integration.
This ensures that data can be consistently accessed and used across the organization.
Establishing Recovery and Backup Rules (NOT part of MDM Lifecycle Management):
While important for overall data management, recovery and backup rules pertain more to data protection and disaster recovery rather than the specific processes of MDM lifecycle management.
Business entities are represented by entity instances:
In the form technical capabilities
In the form of business capabilities
In the form of files
in the form of data/records
In the form of domains
Business entities are represented within an organization through various forms, primarily as data or records within information systems.
Technical Capabilities:
While technical capabilities support the management and usage of business entities, they are not the representation of the entities themselves.
Business Capabilities:
Business capabilities describe the functions and processes that an organization can perform, but they do not represent individual business entities.
Files:
Files can contain data or records, but they are not the direct representation of business entities.
Data/Records:
Business entities are captured and managed as data or records within databases and information systems.
These records contain the attributes and details necessary to uniquely identify and describe each business entity.
Domains:
Domains refer to specific areas of knowledge or activity but are not the direct representation of business entities.
What is a trait of a Consolidated style MDM approach?
Access by index
Complex queries
None of these
System of record
Data latency
In a Consolidated style MDM (Master Data Management) approach, data from multiple source systems is integrated into a single consolidated repository. This consolidated repository acts as the authoritative source for master data, often referred to as the "system of record." The system of record maintains the most accurate, up-to-date, and comprehensive view of master data. Key traits of this approach include:
Centralization: All master data is centralized in one repository, which simplifies data management and governance.
Consistency: Ensures that all users and systems access the same consistent set of master data.
Data Quality: Enhances data quality through data cleansing, deduplication, and validation processes.
Single Source of Truth: Serves as the definitive source for master data, reducing discrepancies and inconsistencies across the organization.
References:
DAMA-DMBOK: Data Management Body of Knowledge, 2nd Edition.
"Master Data Management and Data Governance" by Alex Berson and Larry Dubov.
What statement is most accurate about master data metadata?
Includes a sample of content
Does little to improve fit-for-purpose choices on when and where to apply the ' data
Secures the content
Provides the who. what, and where context about master data content
Can either be related to technical or business perspectives of content, but not
Master data metadata provides crucial information about the master data, offering context and supporting its management and use within the organization.
Who, What, and Where Context:
Metadata provides descriptive information about the master data, including details about who created or modified the data, what the data represents, and where it is used.
This contextual information is essential for understanding the origins, purpose, and usage of the master data.
Includes a Sample of Content:
While metadata might include examples or samples of the data, this is not its primary purpose.
Improving Fit-for-Purpose Choices:
Metadata helps improve the application and governance of master data by providing context and supporting data management decisions.
Securing the Content:
Metadata itself is not primarily focused on security, though it can support data governance and access control processes.
Technical or Business Perspectives:
Metadata can encompass both technical and business perspectives, providing a holistic view of the data's context and usage.
Why is a historical perspective ofMaster Data important?
Provides an audit trail
May be required in litigation cases
Attributes about Master Data subjects evolve over time
Enables business analytics to determine the root cause of behavioral changes
All of the above
Historical Perspective of Master Data:Maintaining historical data about master data objects is crucial for various reasons.
Reasons for Importance:
Provides an audit trail:Keeping historical data allows organizations to track changes and understand the evolution of data over time, which is essential for auditing purposes.
May be required in litigation cases:Historical data can serve as evidence in legal disputes, demonstrating the state of data at specific points in time.
Attributes about Master Data subjects evolve over time:As entities change, such as customers moving or changing names, maintaining historical data allows for accurate tracking of these changes.
Enables business analytics to determine the root cause of behavioral changes:Historical data can help in analyzing trends and identifying reasons for changes in business metrics or customer behavior.
Conclusion:All the provided reasons collectively highlight the importance of maintaining a historical perspective of master data.
References:
DMBOK Guide, sections on Master Data Management and Data Governance.
CDMP Examination Study Materials.
Bringing order to your Master Data would solve what?
20 40% of the need to buy new servers
Distributing data across the enterprise
The need for a metadata repository
60-80% of the most critical data quality problems
Provide a place to store technical data elements
Definitions and Context:
Master Data Management (MDM): MDM involves the processes and technologies for ensuring the uniformity, accuracy, stewardship, semantic consistency, and accountability of an organization’s official shared master data assets.
Data Quality Problems: These include issues such as duplicates, incomplete records, inaccurate data, and data inconsistencies.
Explanation:
Bringing order to your master data, through processes like MDM, aims to resolve data quality issues by standardizing, cleaning, and governing data across the organization.
Effective MDM practices can address and mitigate a significant proportion of data quality problems, as much as 60-80%, because master data is foundational and pervasive across various systems and business processes.
References:
DAMA-DMBOK: Data Management Body of Knowledge, 2nd Edition, Chapter 11: Master and Reference Data Management.
Gartner Research, "The Impact of Master Data Management on Data Quality."
An organization chart where a high level manager has department managers with staff and non-managers without staff as direct reports would best be maintained in which of the following?
A fixed level hierarchy
A ragged hierarchy
A reference file
A taxonomy
A data dictionary
A ragged hierarchy is an organizational structure where different branches of the hierarchy can have varying levels of depth. This means that not all branches have the same number of levels. In the given scenario, where a high-level manager has department managers with staff and non-managers without staff as direct reports, the hierarchy does not have a uniform depth across all branches. This kind of structure is best represented and maintained as a ragged hierarchy, which allows for flexibility in representing varying levels of managerial relationships and reporting structures.
References:
DAMA-DMBOK2 Guide: Chapter 7 – Data Architecture Management
"Master Data Management and Data Governance" by Alex Berson, Larry Dubov
Is there a standard tor defining and exchanging Master Data?
Yes, ISO 22745
No. every corporation uses their own method
Yes. it is called ETL
No. there are no standards because not everyone uses Master Data
ISO 22745 is an international standard for defining and exchanging master data.
ISO 22745:
This standard specifies the requirements for the exchange of master data, particularly in industrial and manufacturing contexts.
It includes guidelines for the structured exchange of information, ensuring that data can be shared and understood across different systems and organizations.
Standards for Master Data:
Standards like ISO 22745 help ensure consistency, interoperability, and data quality across different platforms and entities.
They provide a common framework for defining and exchanging master data, facilitating smoother data integration and management processes.
Other Options:
ETL:Refers to the process of Extract, Transform, Load, used in data integration but not a standard for defining master data.
Corporation-specific Methods:Many organizations may have their own methods, but standardized frameworks like ISO 22745 provide a common foundation.
No Standards:While not all organizations use master data, standards do exist for those that do.
Which of the following reasons is a reason why MDM programs are often not successful?
Too much emphasis on technology rather than people and process components
All of the above
Poor positioning of MDM program responsibility within the IT organization
Not enough business commitment and engagement
MDM initiative is run as a project rather than a program
MDM programs often face challenges and can fail due to a combination of factors. Here’s a detailed explanation:
Emphasis on Technology:
Technology-Centric Approach: Overemphasis on technology solutions without addressing people and process components can lead to failure. Successful MDM programs require balanced attention to technology, people, and processes.
Positioning within IT:
IT Focus: Poor positioning of the MDM program within the IT organization can lead to it being seen as a purely technical initiative, missing the necessary business alignment and support.
Business Commitment and Engagement:
Lack of Engagement: Insufficient commitment and engagement from the business side can result in inadequate support, resources, and buy-in, leading to failure.
Program vs. Project:
Long-Term Perspective: Treating MDM as a one-time project rather than an ongoing program can limit its effectiveness. MDM requires continuous improvement and adaptation to evolving business needs.
References:
Data Management Body of Knowledge (DMBOK), Chapter 7: Master Data Management
DAMA International, "The DAMA Guide to the Data Management Body of Knowledge (DMBOK)"
Master and Reference Data are forms of:
Data Mapping
Data Quality
Data Architecture
Data Integration
Data Security
Master and Reference Data are forms of Data Architecture. Here’s why:
Data Architecture Definition:
Structure and Design: Data architecture involves the structure and design of data systems, including how data is organized, stored, and accessed.
Components: Encompasses various components, including data models, data management processes, and data governance frameworks.
Role of Master and Reference Data:
Core Components: Master and Reference Data are integral components of an organization’s data architecture, providing foundational data elements used across multiple systems and processes.
Organization and Integration: They play a critical role in organizing and integrating data, ensuring consistency and accuracy.
References:
Data Management Body of Knowledge (DMBOK), Chapter 7: Master Data Management
DAMA International, "The DAMA Guide to the Data Management Body of Knowledge (DMBOK)"
Which of the following best describes Mister Data?
Master Data is another name for Reference Data
Master Data is data thatis mastered by business users
Master Data is data about business entities that provide visibility into organizational functions
Master Data is data about business entities that provide context for business transactions and analysis
Master Data is data about technical entities that provide context for transactions
Master data represents the critical business information that is used across the organization. It provides context and structure for business transactions and analytical processes.
Data about Business Entities:
Master data typically includes key entities such as customers, products, suppliers, employees, and locations.
These entities are fundamental to business operations and provide the necessary context for transactions and analysis.
Providing Context for Business Transactions:
Master data provides the foundational information required to conduct business transactions.
For example, customer master data is used in sales transactions, while product master data is used in inventory management.
Supporting Business Analysis:
Master data is critical for business intelligence and analytics, providing a consistent and accurate view of the core business entities.
It enables effective reporting, analysis, and decision-making by ensuring that the data used in these processes is reliable and standardized.
Other Options:
A: Master data and reference data are distinct; reference data is used to categorize master data.
B: Master data is not necessarily mastered by business users but involves collaboration between IT and business stakeholders.
C: Provides visibility but also context for transactions and analysis.
E: Master data is about business entities, not technical entities.
Which of the following Is a characteristic of a probabilistic matching algorithm?
A score is assigned based on weight and degree of match
Each variable to be matched is assigned a weight based on its discriminating power
Individual attribute matching scores arc used to create a match probability percentage.
All answers are correct
Following the matching process there are typically records requiring manual review and decisioning.
Probabilistic matching algorithms assign a score based on the weight and degree of match, assign weights to variables based on their discriminating power, and use individual attribute matching scores to create a match probability percentage. Additionally, after the matching process, some records typically require manual review and decisioning to ensure accuracy. Therefore, all provided characteristics describe the nature of probabilistic matching algorithms accurately.
References:
DAMA-DMBOK: Data Management Body of Knowledge (2nd Edition), Chapter 11: Reference and Master Data Management.
"Master Data Management and Data Governance" by Alex Berson and Larry Dubov
A global identifier is used to:
Link two or more equivalent references to the same entity
Link two or more equivalent columns to the same report
Link two or more non-equivalent references to the same entity
Link two or more systems by the same identifier
Link two or more equivalent references to the same system or database
A global identifier is used to link multiple references to the same entity across different systems or datasets. Here’s why:
Purpose of Global Identifier:
Unique Identification: Provides a unique identifier that can be used to recognize the same entity across disparate systems and datasets.
Consistency: Ensures that different references or records pointing to the same entity are consistently identified and managed.
Linking Equivalent References:
Equivalent References: Global identifiers link references that are equivalent, meaning they represent the same real-world entity even if the data is stored differently in various systems.
Entity Resolution: Helps in resolving different records to a single entity, ensuring data consistency and accuracy.
Example:
Customer Records: A customer might be listed in different systems (CRM, billing, support) with slightly different details. A global identifier links these records to recognize them as the same customer.
References:
Data Management Body of Knowledge (DMBOK), Chapter 7: Master Data Management
DAMA International, "The DAMA Guide to the Data Management Body of Knowledge (DMBOK)"
Should both in-house and commercial tools meet ISO standards for metadata?
Yes. at the very least they should provide guidance
No. each organization needs to develop their own standards based on needs
Adhering to ISO standards for metadata is important for both in-house and commercial tools for the following reasons:
Standardization:
Uniformity: ISO standards ensure that metadata is uniformly described and managed across different tools and systems.
Interoperability: Facilitates interoperability between different tools and systems, enabling seamless data exchange and integration.
Guidance and Best Practices:
Structured Approach: Provides a structured approach for defining and managing metadata, ensuring consistency and reliability.
Compliance and Quality: Ensures compliance with internationally recognized best practices, enhancing data quality and governance.
References:
ISO/IEC 11179: Information technology - Metadata registries (MDR)
Data Management Body of Knowledge (DMBOK), Chapter 7: Master Data Management
DAMA International, "The DAMA Guide to the Data Management Body of Knowledge (DMBOK)"
What MDM style allows data to be authored anywhere?
Consolidation
Centralized style
Persistent
Registry style
Coexistence
Master Data Management (MDM) styles define how and where master data is managed within an organization. One of these styles is the "Coexistence" style, which allows data to be authored and maintained across different systems while ensuring consistency and synchronization.
Coexistence Style:
The coexistence style of MDM allows master data to be created and updated in multiple locations or systems within an organization.
It supports the integration and synchronization of data across these systems to maintain a single, consistent view of the data.
Key Features:
Data Authoring: Data can be authored and updated in various operational systems rather than being confined to a central hub.
Synchronization: Changes made in one system are synchronized across other systems to ensure data consistency and accuracy.
Flexibility: This style provides flexibility to organizations with complex and distributed IT environments, where different departments or units may use different systems.
Benefits:
Enhances data availability and accessibility across the organization.
Supports operational efficiency by allowing data updates to occur where the data is used.
Reduces the risk of data silos and inconsistencies by ensuring data synchronization.
Information Governance is a concept that covers the 'what', how', and why' pertaining to the data assets of an organization. The 'what', 'how', and 'why' are respectively handled by the following functional areas:
Data Management. Information Technology, and Compliance
Customer Experience. Information Security, and data Governance
Data Governance. Information Technology, and Customer Experience
Data Governance. Information Security, and Compliance
Data Management, Information Security, and Customer Experience
Information Governance involves managing and controlling the data assets of an organization, addressing the 'what', 'how', and 'why'.
'What' pertains to Data Governance, which defines policies and procedures for data management.
'How' relates to Information Security, ensuring that data is protected and secure.
'Why' is about Compliance, ensuring that data management practices meet legal and regulatory requirements.
References:
DAMA-DMBOK: Data Management Body of Knowledge (2nd Edition), Chapter 1: Data Governance.
"Information Governance: Concepts, Strategies, and Best Practices" by Robert F. Smallwood.
The concept of tracking the number of MDM subject areas and source system attributes Is referred to as:
Publish and Subscribe
Hub and Spoke
Mapping and Integration
Subject Area and Attribute
Scope and Coverage
Tracking the number of MDM subject areas and source system attributes refers to defining the scope and coverage of the subject areas and attributes involved in an MDM initiative. This process includes identifying all the data entities (subject areas) and the specific attributes (data elements) within those entities that need to be managed across the organization. By establishing a clear scope and coverage, organizations can ensure that all relevant data is accounted for and appropriately managed.
References:
DAMA-DMBOK2 Guide: Chapter 10 – Master and Reference Data Management
"Master Data Management and Data Governance" by Alex Berson, Larry Dubov
Reference Data Dictionaries are authoritative listings of:
Master Data entities
External sources of data
Master Data sources
Master Data systems of record
Semantic rules
Definitions and Context:
Reference Data Dictionaries: These are authoritative resources that provide standardized definitions and classifications for data elements.
External Sources of Data: These are data sources that come from outside the organization and are used for various analytical and operational purposes.
Explanation:
Reference Data Dictionaries often contain listings and definitions for data that are used across different organizations and systems, ensuring consistency and interoperability.
They typically include external data sources, which need to be standardized and understood in the context of the organization’s own data environment.
References:
DAMA-DMBOK: Data Management Body of Knowledge, 2nd Edition, Chapter 11: Master and Reference Data Management.
ISO/IEC 11179-3:2013, Information technology - Metadata registries (MDR) - Part 3: Registry metamodel and basic attributes.