Mr. Hery Purnama is an IT consultant and trainer in Bandung, Indonesia with over 20 years of experience in various IT projects. He specializes in areas like system development, data science, IoT, project management, IT service management, information security, and enterprise architecture. He holds several international certifications and provides training on topics such as CDMP (Certified Data Management Professional), COBIT, and TOGAF.
The document discusses an overview and exam requirements for the CDMP certification. It covers the 14 topics tested in the 100 question exam, including data governance, data modeling, data security, and big data. Tips are provided for exam registration and practice questions are available online.
Chapter 1: The Importance of Data AssetsAhmed Alorage
The document summarizes Chapter 1 of the DAMA-DMBOK Guide, which discusses data as a vital enterprise asset and introduces key concepts in data management. It defines data, information, and knowledge; describes the data lifecycle and data management functions; and explains that data management is a shared responsibility between data stewards and professionals. It also provides overviews of the DAMA organization and the goals and audiences of the DAMA-DMBOK Guide.
The document discusses meta-data management. It defines meta-data as "data about data" that describes other data. Meta-data management involves understanding requirements, defining architectures, implementing standards, creating and maintaining meta-data, and managing meta-data repositories. The document outlines the concepts, types, sources, and activities involved in effective meta-data management.
Data Privacy in the DMBOK - No Need to Reinvent the WheelDATAVERSITY
World wide, Data Privacy laws are increasing. Customers are increasingly aware, and concerned, about how data is processed. The Chief Privacy Officer is (or should be) a key stakeholder for many Data Governance initiatives, and new terms like “Privacy by Design” and “Privacy Engineering” are entering our conversations with peers. Non-EU organizations selling into the EU will soon have to comply with EU Data Privacy laws. However, data professionals who take a structured, principles based approach, to building their Data Privacy capabilities stand a better chance of sustainable success than those who don’t. Rather than reinventing the wheel, organizations should look at how the DMBOK framework, in conjunction with other approaches and methods, can provide a robust platform for Data Privacy initiatives in their organizations.
Peter Vennel presents on the topic of DAMA DMBOK and Data Governance. He discusses his background and certifications. He then covers some key topics in data governance including the challenges of implementing it and defining what it is. He outlines the DAMA DMBOK knowledge areas and introduces the concept of a Data Management Center of Excellence (DMCoE) to establish governance. The DMCoE would include steering committees for each knowledge area and a data governance council and team.
Business Value Through Reference and Master Data StrategiesDATAVERSITY
Data tends to pile up and can be rendered unusable or obsolete without careful maintenance processes. Reference and Master Data Management (MDM) has been a popular Data Management approach to effectively gain mastery over not just the data but the supporting architecture for processing it. This webinar presents MDM as a strategic approach to improving and formalizing practices around those data items that provide context for many organizational transactions — the master data. Too often, MDM has been implemented technology-first and achieved the same very poor track record (one-third succeeding on time, within budget, and achieving planned functionality). MDM success depends on a coordinated approach, typically involving Data Governance and Data Quality activities.
Learning Objectives:
• Understand foundational reference and MDM concepts based on the Data Management Body of Knowledge (DMBoK)
• Understand why these are an important component of your Data Architecture
• Gain awareness of reference and MDM frameworks and building blocks
• Know what MDM guiding principles consist of and best practices
• Know how to utilize reference and MDM in support of business strategy
Data Modeling, Data Governance, & Data QualityDATAVERSITY
Data Governance is often referred to as the people, processes, and policies around data and information, and these aspects are critical to the success of any data governance implementation. But just as critical is the technical infrastructure that supports the diverse data environments that run the business. Data models can be the critical link between business definitions and rules and the technical data systems that support them. Without the valuable metadata these models provide, data governance often lacks the “teeth” to be applied in operational and reporting systems.
Join Donna Burbank and her guest, Nigel Turner, as they discuss how data models & metadata-driven data governance can be applied in your organization in order to achieve improved data quality.
The document discusses data governance concepts and activities. It defines data governance as the exercise of authority and control over data asset management. It describes the key roles and organizations involved in data governance, including the data governance council, data stewardship committees, and data stewardship teams. It also outlines the main activities of a data governance function, such as developing a data strategy, policies, standards, and procedures. The document provides details on how issues are managed and how data governance interacts with and oversees data management projects.
Chapter 1: The Importance of Data AssetsAhmed Alorage
The document summarizes Chapter 1 of the DAMA-DMBOK Guide, which discusses data as a vital enterprise asset and introduces key concepts in data management. It defines data, information, and knowledge; describes the data lifecycle and data management functions; and explains that data management is a shared responsibility between data stewards and professionals. It also provides overviews of the DAMA organization and the goals and audiences of the DAMA-DMBOK Guide.
The document discusses meta-data management. It defines meta-data as "data about data" that describes other data. Meta-data management involves understanding requirements, defining architectures, implementing standards, creating and maintaining meta-data, and managing meta-data repositories. The document outlines the concepts, types, sources, and activities involved in effective meta-data management.
Data Privacy in the DMBOK - No Need to Reinvent the WheelDATAVERSITY
World wide, Data Privacy laws are increasing. Customers are increasingly aware, and concerned, about how data is processed. The Chief Privacy Officer is (or should be) a key stakeholder for many Data Governance initiatives, and new terms like “Privacy by Design” and “Privacy Engineering” are entering our conversations with peers. Non-EU organizations selling into the EU will soon have to comply with EU Data Privacy laws. However, data professionals who take a structured, principles based approach, to building their Data Privacy capabilities stand a better chance of sustainable success than those who don’t. Rather than reinventing the wheel, organizations should look at how the DMBOK framework, in conjunction with other approaches and methods, can provide a robust platform for Data Privacy initiatives in their organizations.
Peter Vennel presents on the topic of DAMA DMBOK and Data Governance. He discusses his background and certifications. He then covers some key topics in data governance including the challenges of implementing it and defining what it is. He outlines the DAMA DMBOK knowledge areas and introduces the concept of a Data Management Center of Excellence (DMCoE) to establish governance. The DMCoE would include steering committees for each knowledge area and a data governance council and team.
Business Value Through Reference and Master Data StrategiesDATAVERSITY
Data tends to pile up and can be rendered unusable or obsolete without careful maintenance processes. Reference and Master Data Management (MDM) has been a popular Data Management approach to effectively gain mastery over not just the data but the supporting architecture for processing it. This webinar presents MDM as a strategic approach to improving and formalizing practices around those data items that provide context for many organizational transactions — the master data. Too often, MDM has been implemented technology-first and achieved the same very poor track record (one-third succeeding on time, within budget, and achieving planned functionality). MDM success depends on a coordinated approach, typically involving Data Governance and Data Quality activities.
Learning Objectives:
• Understand foundational reference and MDM concepts based on the Data Management Body of Knowledge (DMBoK)
• Understand why these are an important component of your Data Architecture
• Gain awareness of reference and MDM frameworks and building blocks
• Know what MDM guiding principles consist of and best practices
• Know how to utilize reference and MDM in support of business strategy
Data Modeling, Data Governance, & Data QualityDATAVERSITY
Data Governance is often referred to as the people, processes, and policies around data and information, and these aspects are critical to the success of any data governance implementation. But just as critical is the technical infrastructure that supports the diverse data environments that run the business. Data models can be the critical link between business definitions and rules and the technical data systems that support them. Without the valuable metadata these models provide, data governance often lacks the “teeth” to be applied in operational and reporting systems.
Join Donna Burbank and her guest, Nigel Turner, as they discuss how data models & metadata-driven data governance can be applied in your organization in order to achieve improved data quality.
The document discusses data governance concepts and activities. It defines data governance as the exercise of authority and control over data asset management. It describes the key roles and organizations involved in data governance, including the data governance council, data stewardship committees, and data stewardship teams. It also outlines the main activities of a data governance function, such as developing a data strategy, policies, standards, and procedures. The document provides details on how issues are managed and how data governance interacts with and oversees data management projects.
Too often I hear the question “Can you help me with our Data Strategy?” Unfortunately, for most, this is the wrong request because it focuses on the least valuable component – the Data Strategy itself. A more useful request is this: “Can you help me apply data strategically?”Yes, at early maturity phases the process of developing strategic thinking about data is more important than the actual product! Trying to write a good (must less perfect) Data Strategy on the first attempt is generally not productive –particularly given the widespread acceptance of Mike Tyson’s truism: “Everybody has a plan until they get punched in the face.” Refocus on learning how to iteratively improve the way data is strategically applied. This will permit data-based strategy components to keep up with agile, evolving organizational strategies. This approach can also contribute to three primary organizational data goals.
In this webinar, you will learn how improving your organization’s data, the way your people use data, and the way your people use data to achieve your organizational strategy will help in ways never imagined. Data are your sole non-depletable, non-degradable, durable strategic assets, and they are pervasively shared across every organizational area. Addressing existing challenges programmatically includes overcoming necessary but insufficient prerequisites and developing a disciplined, repeatable means of improving business objectives. This process (based on the theory of constraints) is where the strategic data work really occurs, as organizations identify prioritized areas where better assets, literacy, and support (Data Strategy components) can help an organization better achieve specific strategic objectives. Then the process becomes lather, rinse, and repeat. Several complementary concepts are also covered, including:
- A cohesive argument for why Data Strategy is necessary for effective Data Governance
- An overview of prerequisites for effective strategic use of Data Strategy, as well as common pitfalls
- A repeatable process for identifying and removing data constraints
- The importance of balancing business operation and innovation
Most Common Data Governance Challenges in the Digital EconomyRobyn Bollhorst
Todays’ increasing emphasis on differentiation in the digital economy further complicates the data governance challenge. Learn about today’s common challenges and about the new adaptations that are required to support the digital era. Avoid the pitfalls and follow along on Johnson & Johnson’s journey to:
- Establish and scale a best in class enterprise data governance program
- Identify and focus on the most critical data and information to bolster incremental wins and garner executive support
- Ensure readiness for automation with SAP MDG on HANA
CDMP Overview Professional Information Management CertificationChristopher Bradley
Overview of the DAMA Certified Data Management Professional (CDMP) examination.
Session presented at DAMA Australia November 2013
chris.bradley@dmadvisors.co.uk
Introduction to DCAM, the Data Management Capability Assessment Model - Editi...Element22
DCAM stands for Data management Capability Assessment Model. DCAM is a model to assess data management capabilities within the financial industry. It was created by the EDM Council in collaboration with over 100 financial institutions. This presentation provides an overview of DCAM and how financial institutions leverage DCAM to improve or establish their data management programs and meet regulatory requirements such as BCBS 239. Also the benefits of DCAM are described as part of this presentation.
Data-Ed Webinar: Data Governance StrategiesDATAVERSITY
This webinar discusses data governance strategies and provides an overview of key concepts. It covers defining data governance and why it is important, outlining requirements for effective data governance such as accessibility, security, consistency, quality and being auditable. The presentation also discusses data governance frameworks, components, and best practices, providing examples to illustrate how data governance can be implemented and help organizations.
Chapter 8: Reference and Master Data Management Ahmed Alorage
The document discusses reference and master data management. It defines reference data as data used to classify or categorize other data, using predefined valid values. Master data provides context for business transactions and includes data about key entities like parties, products, locations. The objectives are to maintain consistent reference and master data across systems through activities like defining golden records, match rules, hierarchies and distributing reference and master data.
Data-Ed Slides: Best Practices in Data Stewardship (Technical)DATAVERSITY
In order to find value in your organization's data assets, heroic data stewards are tasked with saving the day- every single day! These heroes adhere to a data governance framework and work to ensure that data is: captured right the first time, validated through automated means, and integrated into business processes. Whether its data profiling or in depth root cause analysis, data stewards can be counted on to ensure the organization's mission critical data is reliable. In this webinar we will approach this framework, and punctuate important facets of a data steward’s role.
Learning Objectives:
- Understand the business need for a data governance framework
- Learn why embedded data quality principles are an important part of system/process design
- Identify opportunities to help drive your organization to a data driven culture
This document discusses data operations management. It defines data operations management as developing, maintaining, and supporting structured data to maximize value. Key activities include database support and data technology management. Database administrators play an important role in ensuring database availability, performance, integrity, and recoverability through activities like backups, monitoring, tuning, and setting service level agreements.
The document provides an overview of data management, including its mission, goals, functions, activities, roles, and supporting technologies. It describes the 10 main functions of data management as data governance, data architecture management, data development, data operations management, data security management, reference and master data management, data warehousing/business intelligence, document and content management, metadata management, and data quality management. For each function, it lists the core activities and sub-activities. The overview aims to cover the key processes, roles, and technologies involved in comprehensive data management.
DMBOK 2.0 and other frameworks including TOGAF & COBIT - keynote from DAMA Au...Christopher Bradley
This document provides biographical information about Christopher Bradley, an expert in information management. It outlines his 36 years of experience in the field working with major organizations. He is the president of DAMA UK and author of sections of the DAMA DMBoK 2. It also lists his recent presentations and publications, which cover topics such as data governance, master data management, and information strategy. The document promotes training courses he provides on information management fundamentals and data modeling.
Data Governance Powerpoint Presentation SlidesSlideTeam
This document discusses the need for and benefits of data governance, as well as common challenges companies face with data governance. It outlines roles and responsibilities in a data governance program, ways to establish a data governance program, and provides a data governance framework and roadmap for improvement. Specific topics covered include ensuring data consistency, guiding analytical activities, saving money, and providing clarity on conflicting data. Common challenges include lack of communication, organizational issues, cost, lack of data and application integration, and issues with data quality and migration. The document compares manual and automated approaches to data governance.
Chapter 10: Document and Content Management Ahmed Alorage
This document discusses document and content management. It covers concepts like document management, which involves storing, tracking, and controlling electronic and paper documents, and content management, which organizes and structures access to information content. The key activities covered are planning and policies for managing documents, implementing document management systems for storage, access and security, backup and recovery of documents, retention and disposition according to policies and regulations, and auditing document management. The document provides details on each of these concepts and activities.
Reference matter data management:
Two categories of structured data :
Master data: is data associated with core business entities such as customer, product, asset, etc.
Transaction data: is the recording of business transactions such as orders in manufacturing, loan and credit card payments in banking, and product sales in retail.
Reference data: is any kind of data that is used solely to categorize other data found in a database, or solely for relating data in a database to information beyond the boundaries of the enterprise .
What has changed in DMBok V2?
We have been working with DMBoK V1 for may years and it is great to finally get to read and study the changes. Did a quikc comparison between the 2 versions.
Chapter 4: Data Architecture ManagementAhmed Alorage
This document provides an overview of data architecture management. It defines data architecture as an integrated set of specifications that define data requirements, guide integration, and align data investments with business strategy. The key concepts discussed include enterprise architecture, architectural frameworks like Zachman, and the roles and activities of data architects. Data architecture management is presented as the process of defining a blueprint for managing data assets through specifications like enterprise data models and information value chain analysis.
The first step towards understanding what data assets mean for your organization is understanding what those assets mean for each other. Metadata—literally, data about data—is one of many data management disciplines inherent in good systems development, and is perhaps the most mislabeled and misunderstood out of the lot. Understanding metadata and its associated technologies as more than just straightforward technological tools can provide powerful insight, the efficiency of organizational practices, and can also enable you to combine more sophisticated data management techniques in support of larger and more complex business initiatives.
In this webinar, we will:
Illustrate how to leverage metadata in support of your business strategy
Discuss foundational metadata concepts based on the DAMA Guide to Data Management Book of Knowledge (DAMA DMBOK)
Enumerate guiding principles for and lessons previously learned from metadata and its practical uses
This introduction to data governance presentation covers the inter-related DM foundational disciplines (Data Integration / DWH, Business Intelligence and Data Governance). Some of the pitfalls and success factors for data governance.
• IM Foundational Disciplines
• Cross-functional Workflow Exchange
• Key Objectives of the Data Governance Framework
• Components of a Data Governance Framework
• Key Roles in Data Governance
• Data Governance Committee (DGC)
• 4 Data Governance Policy Areas
• 3 Challenges to Implementing Data Governance
• Data Governance Success Factors
Organizations must realize what it means to utilize data quality management in support of business strategy. This webinar will illustrate how organizations with chronic business challenges often can trace the root of the problem to poor data quality. Showing how data quality should be engineered provides a useful framework in which to develop an effective approach. This in turn allows organizations to more quickly identify business problems as well as data problems caused by structural issues versus practice-oriented defects and prevent these from re-occurring.
Data-Ed Webinar: Data Quality EngineeringDATAVERSITY
Organizations must realize what it means to utilize data quality management in support of business strategy. This webinar will illustrate how organizations with chronic business challenges often can trace the root of the problem to poor data quality. Showing how data quality should be engineered provides a useful framework in which to develop an effective approach. This in turn allows organizations to more quickly identify business problems as well as data problems caused by structural issues versus practice-oriented defects and prevent these from re-occurring.
Takeaways:
Understanding foundational data quality concepts based on the DAMA DMBOK
Utilizing data quality engineering in support of business strategy
Data Quality guiding principles & best practices
Steps for improving data quality at your organization
Too often I hear the question “Can you help me with our Data Strategy?” Unfortunately, for most, this is the wrong request because it focuses on the least valuable component – the Data Strategy itself. A more useful request is this: “Can you help me apply data strategically?”Yes, at early maturity phases the process of developing strategic thinking about data is more important than the actual product! Trying to write a good (must less perfect) Data Strategy on the first attempt is generally not productive –particularly given the widespread acceptance of Mike Tyson’s truism: “Everybody has a plan until they get punched in the face.” Refocus on learning how to iteratively improve the way data is strategically applied. This will permit data-based strategy components to keep up with agile, evolving organizational strategies. This approach can also contribute to three primary organizational data goals.
In this webinar, you will learn how improving your organization’s data, the way your people use data, and the way your people use data to achieve your organizational strategy will help in ways never imagined. Data are your sole non-depletable, non-degradable, durable strategic assets, and they are pervasively shared across every organizational area. Addressing existing challenges programmatically includes overcoming necessary but insufficient prerequisites and developing a disciplined, repeatable means of improving business objectives. This process (based on the theory of constraints) is where the strategic data work really occurs, as organizations identify prioritized areas where better assets, literacy, and support (Data Strategy components) can help an organization better achieve specific strategic objectives. Then the process becomes lather, rinse, and repeat. Several complementary concepts are also covered, including:
- A cohesive argument for why Data Strategy is necessary for effective Data Governance
- An overview of prerequisites for effective strategic use of Data Strategy, as well as common pitfalls
- A repeatable process for identifying and removing data constraints
- The importance of balancing business operation and innovation
Most Common Data Governance Challenges in the Digital EconomyRobyn Bollhorst
Todays’ increasing emphasis on differentiation in the digital economy further complicates the data governance challenge. Learn about today’s common challenges and about the new adaptations that are required to support the digital era. Avoid the pitfalls and follow along on Johnson & Johnson’s journey to:
- Establish and scale a best in class enterprise data governance program
- Identify and focus on the most critical data and information to bolster incremental wins and garner executive support
- Ensure readiness for automation with SAP MDG on HANA
CDMP Overview Professional Information Management CertificationChristopher Bradley
Overview of the DAMA Certified Data Management Professional (CDMP) examination.
Session presented at DAMA Australia November 2013
chris.bradley@dmadvisors.co.uk
Introduction to DCAM, the Data Management Capability Assessment Model - Editi...Element22
DCAM stands for Data management Capability Assessment Model. DCAM is a model to assess data management capabilities within the financial industry. It was created by the EDM Council in collaboration with over 100 financial institutions. This presentation provides an overview of DCAM and how financial institutions leverage DCAM to improve or establish their data management programs and meet regulatory requirements such as BCBS 239. Also the benefits of DCAM are described as part of this presentation.
Data-Ed Webinar: Data Governance StrategiesDATAVERSITY
This webinar discusses data governance strategies and provides an overview of key concepts. It covers defining data governance and why it is important, outlining requirements for effective data governance such as accessibility, security, consistency, quality and being auditable. The presentation also discusses data governance frameworks, components, and best practices, providing examples to illustrate how data governance can be implemented and help organizations.
Chapter 8: Reference and Master Data Management Ahmed Alorage
The document discusses reference and master data management. It defines reference data as data used to classify or categorize other data, using predefined valid values. Master data provides context for business transactions and includes data about key entities like parties, products, locations. The objectives are to maintain consistent reference and master data across systems through activities like defining golden records, match rules, hierarchies and distributing reference and master data.
Data-Ed Slides: Best Practices in Data Stewardship (Technical)DATAVERSITY
In order to find value in your organization's data assets, heroic data stewards are tasked with saving the day- every single day! These heroes adhere to a data governance framework and work to ensure that data is: captured right the first time, validated through automated means, and integrated into business processes. Whether its data profiling or in depth root cause analysis, data stewards can be counted on to ensure the organization's mission critical data is reliable. In this webinar we will approach this framework, and punctuate important facets of a data steward’s role.
Learning Objectives:
- Understand the business need for a data governance framework
- Learn why embedded data quality principles are an important part of system/process design
- Identify opportunities to help drive your organization to a data driven culture
This document discusses data operations management. It defines data operations management as developing, maintaining, and supporting structured data to maximize value. Key activities include database support and data technology management. Database administrators play an important role in ensuring database availability, performance, integrity, and recoverability through activities like backups, monitoring, tuning, and setting service level agreements.
The document provides an overview of data management, including its mission, goals, functions, activities, roles, and supporting technologies. It describes the 10 main functions of data management as data governance, data architecture management, data development, data operations management, data security management, reference and master data management, data warehousing/business intelligence, document and content management, metadata management, and data quality management. For each function, it lists the core activities and sub-activities. The overview aims to cover the key processes, roles, and technologies involved in comprehensive data management.
DMBOK 2.0 and other frameworks including TOGAF & COBIT - keynote from DAMA Au...Christopher Bradley
This document provides biographical information about Christopher Bradley, an expert in information management. It outlines his 36 years of experience in the field working with major organizations. He is the president of DAMA UK and author of sections of the DAMA DMBoK 2. It also lists his recent presentations and publications, which cover topics such as data governance, master data management, and information strategy. The document promotes training courses he provides on information management fundamentals and data modeling.
Data Governance Powerpoint Presentation SlidesSlideTeam
This document discusses the need for and benefits of data governance, as well as common challenges companies face with data governance. It outlines roles and responsibilities in a data governance program, ways to establish a data governance program, and provides a data governance framework and roadmap for improvement. Specific topics covered include ensuring data consistency, guiding analytical activities, saving money, and providing clarity on conflicting data. Common challenges include lack of communication, organizational issues, cost, lack of data and application integration, and issues with data quality and migration. The document compares manual and automated approaches to data governance.
Chapter 10: Document and Content Management Ahmed Alorage
This document discusses document and content management. It covers concepts like document management, which involves storing, tracking, and controlling electronic and paper documents, and content management, which organizes and structures access to information content. The key activities covered are planning and policies for managing documents, implementing document management systems for storage, access and security, backup and recovery of documents, retention and disposition according to policies and regulations, and auditing document management. The document provides details on each of these concepts and activities.
Reference matter data management:
Two categories of structured data :
Master data: is data associated with core business entities such as customer, product, asset, etc.
Transaction data: is the recording of business transactions such as orders in manufacturing, loan and credit card payments in banking, and product sales in retail.
Reference data: is any kind of data that is used solely to categorize other data found in a database, or solely for relating data in a database to information beyond the boundaries of the enterprise .
What has changed in DMBok V2?
We have been working with DMBoK V1 for may years and it is great to finally get to read and study the changes. Did a quikc comparison between the 2 versions.
Chapter 4: Data Architecture ManagementAhmed Alorage
This document provides an overview of data architecture management. It defines data architecture as an integrated set of specifications that define data requirements, guide integration, and align data investments with business strategy. The key concepts discussed include enterprise architecture, architectural frameworks like Zachman, and the roles and activities of data architects. Data architecture management is presented as the process of defining a blueprint for managing data assets through specifications like enterprise data models and information value chain analysis.
The first step towards understanding what data assets mean for your organization is understanding what those assets mean for each other. Metadata—literally, data about data—is one of many data management disciplines inherent in good systems development, and is perhaps the most mislabeled and misunderstood out of the lot. Understanding metadata and its associated technologies as more than just straightforward technological tools can provide powerful insight, the efficiency of organizational practices, and can also enable you to combine more sophisticated data management techniques in support of larger and more complex business initiatives.
In this webinar, we will:
Illustrate how to leverage metadata in support of your business strategy
Discuss foundational metadata concepts based on the DAMA Guide to Data Management Book of Knowledge (DAMA DMBOK)
Enumerate guiding principles for and lessons previously learned from metadata and its practical uses
This introduction to data governance presentation covers the inter-related DM foundational disciplines (Data Integration / DWH, Business Intelligence and Data Governance). Some of the pitfalls and success factors for data governance.
• IM Foundational Disciplines
• Cross-functional Workflow Exchange
• Key Objectives of the Data Governance Framework
• Components of a Data Governance Framework
• Key Roles in Data Governance
• Data Governance Committee (DGC)
• 4 Data Governance Policy Areas
• 3 Challenges to Implementing Data Governance
• Data Governance Success Factors
Organizations must realize what it means to utilize data quality management in support of business strategy. This webinar will illustrate how organizations with chronic business challenges often can trace the root of the problem to poor data quality. Showing how data quality should be engineered provides a useful framework in which to develop an effective approach. This in turn allows organizations to more quickly identify business problems as well as data problems caused by structural issues versus practice-oriented defects and prevent these from re-occurring.
Data-Ed Webinar: Data Quality EngineeringDATAVERSITY
Organizations must realize what it means to utilize data quality management in support of business strategy. This webinar will illustrate how organizations with chronic business challenges often can trace the root of the problem to poor data quality. Showing how data quality should be engineered provides a useful framework in which to develop an effective approach. This in turn allows organizations to more quickly identify business problems as well as data problems caused by structural issues versus practice-oriented defects and prevent these from re-occurring.
Takeaways:
Understanding foundational data quality concepts based on the DAMA DMBOK
Utilizing data quality engineering in support of business strategy
Data Quality guiding principles & best practices
Steps for improving data quality at your organization
In today's competitive market, many organizations are unaware of the quantity of poor-quality data in their systems. Some organizations assume that their data is of adequate quality, although they have conducted no metrical or statistical analysis to support the assumption. Others know that their performance is hampered by poor-quality data, but they cannot measure the problem.
This document discusses implementing a non-invasive enterprise data governance program. It begins by outlining some common data challenges around data quality, variety, and volume. It then proposes formalizing existing informal governance by putting structure around current practices to improve data risk management, quality, and coordination. The solution involves taking a non-invasive approach and not spending a lot of money. Several frameworks and models are presented for implementing an effective yet lightweight data governance program, including an Enterprise Information Management framework and an Enterprise Data Strategy and Design framework.
The document provides an overview of data management, including its mission, goals, functions, activities, roles, and supporting technologies. It describes the 10 main functions of data management as data governance, data architecture management, data development, data operations management, data security management, reference and master data management, data warehousing/business intelligence, document and content management, metadata management, and data quality management. For each function, it lists the core activities and sub-activities. The overview aims to cover the key processes, roles, and technologies involved in comprehensive data management.
This document discusses data architecture and governance. It describes the structure of a data architecture and governance team, including roles for data governance, data quality, business glossary, master data management, and more. It also discusses the team's mission to proactively define rules, ensure high quality data, and provide expert advice on information and data governance. Finally, it provides overviews of various topics within data architecture and governance like data quality management, metadata management, master data management, and data warehousing/business intelligence management.
This document outlines the City of Dallas' data management strategy for 2019-2022. The strategy aims to develop a business strategy to collect, store, manage, and process data in a standard way required by the City. It establishes a data governance structure and framework to help the City gain benefits from its data assets by controlling, monitoring, and protecting data use. The data management strategy is tightly coupled with IT governance and project management to create a well-planned approach to managing the City's data.
You Need a Data Catalog. Do You Know Why?Precisely
The data catalog has become a popular discussion topic within data management and data governance circles. A data catalog is a central repository that contains metadata for describing data sets, how they are defined, and where to find them. TDWI research indicates that implementing a data catalog is a top priority among organizations we survey. The data catalog can also play an important part in the governance process. It provides features that help ensure data quality, compliance, and that trusted data is used for analysis. Without an in-depth knowledge of data and associated metadata, organizations cannot truly safeguard and govern their data.
Join this on-demand webinar to learn more about the data catalog and its role in data governance efforts.
Topics include:
· Data management challenges and priorities
· The modern data catalog – what it is and why it is important
· The role of the modern data catalog in your data quality and governance programs
· The kinds of information that should be in your data catalog and why
Linking Data Governance to Business GoalsPrecisely
This document discusses linking data governance to business goals. It begins with an example of a typical governance program that loses business support over time. It then advocates taking a business-first approach to accelerate programs and increase ROI. Successful programs link governance to business goals, outcomes, stakeholders and capabilities. The document provides examples of how different business goals map to governance objectives and capabilities. It emphasizes quantifying value at strategic, operational and tactical levels. Finally, it discusses Jean-Paulotte Group's Chief Data Officer implementing a working approach driven by business value through an iterative process between a Data Management Committee and Working Groups.
Enterprise Data Governance for Financial InstitutionsSheldon McCarthy
This document discusses data governance for financial institutions. It covers topics such as metadata management, master data management, data quality management, and data privacy and security. Data governance involves planning, defining standards, assigning accountability, classifying data, and managing data quality. It helps protect sensitive information and enables more effective data use. Master data management brings together business rules, procedures, roles, and policies to research and implement controls around an organization's data. Data quality management establishes roles, responsibilities, and business rules to address existing data problems and prevent potential issues.
Oracle Application User Group sponsored Collaborate 2009 Presentation 'Building a Practical Strategy for Managing Data Quality' by Alex Fiteni CPA, CMA
The document discusses key aspects of data governance including governance, data stewardship, data quality, and master data management. It provides definitions and descriptions of these terms. For example, it defines data governance as the overall management of the availability, usability, integrity and security of enterprise data. It also notes that data stewardship, data quality, and master data management are pillars of effective data governance. The document then provides more details on each of these concepts.
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Traditional data governance programs struggle to make the connection between critical policies and processes and its impact on business value and results. This leaves data management and governance practitioners having to continually make the case for data governance to secure business adoption.
Watch this on-demand webinar to learn about the proven methods to identify the data that matters, connect governance policies to business objectives, and quickly deliver value through the life of the program.
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Objective
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Context*
Generally, on weekends people tend to spend time with their family and friends and go for outings, parties, shopping, etc. which results in an increased number of vehicles and crowds on the roads.
Over the years a rapid increase in road casualties was observed on weekends by the Government.
In the year 2005, the Government wanted to identify the impact of road safety laws, especially the speed limit restrictions in different states with the help of government records for the past 10 years (1995-2004), the objective was to introduce/revive road safety laws accordingly for all the states to reduce the increasing number of road casualties on weekends
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PyData London 2024: Mistakes were made (Dr. Rebecca Bilbro)
CDMP SLIDE TRAINER .pptx
1. CDMP - Certified Data
Management Professional
DMBOK V.2
Trainer :
Hery Purnama, SE., MM.
MCP, PMP, ITILF, CISA, CISM, CISSP, CDMP, COBIT, CTFL,
TOGAF9
2. Mr. Hery Purnama is an IT Practitioner, Lecturer and IT
Consultant in Bandung, with more than 20 years of
experience in various IT projects with specialization in
System Development, Bigdata, Data Science, Internet of
Things, ISO, Project Management, IT Service Management,
I.S Governance, InfoSec Governance, Data Governance ,
Enterprise Architect , Quality Assurance, and IT Audit
Until now he is still actively working as a consultant and also
a trainer with clients from the Government, BUMN, Mining,
Industrial Banking, Telecommunications.
Some of the international certifications he holds are:
MCP, PMP, ITILF, COBIT, CGEIT, CDMP, CISA, CISM, CISSP, CTFL,
TOGAF 9
5. 100 Questions Covers 14 Topics of DMBOK2
1. Data Management Process – 2%
2. Data Ethics – 2%
3. Data Governance – 11%
4. Data Architecture – 6%
5. Data Modelling and Design – 11%
6. Data Storage and Operations – 6%
7. Data Security – 6%
8. Data Integration and Interoperability – 6%
9. Document and Content Management – 6%
10. Master and Reference Data Management – 10%
11. Data Warehousing and Business Intelligence – 10%
12. Metadata Management – 11%
13. Data Quality – 11%
14. Big Data – 2%
9. • LET’S GET EXERCISE
•https://wato.xyz/cdmppractice1
passcode : cdmp
10. Introduction
• Data Management is the development, execution, and supervision of
plans, policies, programs, and practices that deliver, control, protect,
and enhance the value of data and information assets throughout
their lifecycles.
• Data Management Professional is any person who works in any facet
of data management
• Data is the ‘currency’, the ‘life blood’, and even the ‘new oil’ of the
information economy.
• Business Driver : Data Asset Value
• Data Management Goals
11. Essential Concept
• VARIOUS DATA DEFINITIONS :
• data emphasize its role in representing facts about the
world. (Common)
• Data information that has been stored in digital form (IT)
• Facts : Data is a mean representation Need Context
(Metadata)
12. Essential Concept
• DATA VS INFORMATION :
• DATA PYRAMID DIKW :
1.DATA (RAW) ->
2.INFORMATION (WHO,WHAT,WHEN, WHERE) ->
3.KNOWLEDGE (HOW) ->
4.WISDOM (WHY)
KNOWLEDGE & WISDOM
DATA & INFORMATION GOALS
DKIW
Example Data vs Information
(“Here is a sales report for the last quarter [information]. It is based on
data from our data warehouse [data]. Next quarter these results [data]
will be used to generate our quarter-over-quarter performance measures
[information]”)
13. Essential Concept
• Data as an Organizational Asset (Economic
Resources :
shows up as an item on the Profit and Loss
Statement (P&L) ,
& to make more effective decisions and to
operate more efficiently
• Data Management Principles >
• Data Management Challenges (Differs,
Valuation, Quality, Planning for Better Data,
Metadata and Meta management , Cross
functionality..)
15. The focus of data management on the data lifecycle
IMPLICATIONS :
•Creation and usage are the most critical points in the data lifecycle
•Data Quality must be managed throughout the data lifecycle
•Metadata Quality must be managed through the data lifecycle
•Data Security must be managed throughout the data lifecycle
•Data Management efforts should focus on the most critical data
16. Data and Risk
• Low Quality Data (Inaccurate, Incomplete, Out of Date)
• Missunderstood,Missused
“Information Gaps : the difference between what we know and what
we need to know to make an effective decision.
Information gaps represent enterprise liabilities with potentially
profound impacts on operational effectiveness and profitability. “
• The increased role of information as an organizational asset across all
sectors has led to an increased focus by regulators and legislators on
the potential uses and abuses of information
17. Data Management Strategy
The components of a data management strategy :
•A compelling vision for data management
•A summary business case for data management, with selected examples
•Guiding principles, values, and management perspectives
•The mission and long-term directional goals of data management
•Proposed measures of data management success
•Short-term (12-24 months) Data Management program objectives that are SMART
(specific, measurable,actionable, realistic, time-bound)
•Descriptions of data management roles and organizations, along with a summary of their
responsibilitiesand decision rights
•Descriptions of Data Management program components and initiatives
•A prioritized program of work with scope boundaries
•A draft implementation roadmap with projects and action items
33. • LET’S GET PRACTICE
•https://wato.xyz/cdmppractice2
passcode : cdmp
34. Introduction
• Data handling ethics are concerned with how to procure, store,
manage, use, and dispose of data in ways that are aligned with ethical
principles.
• Handling data in an ethical manner is necessary to the long-term
success of any organization that wants to get value from its data.
35. Core Concept
• Impact on people: Because data represents characteristics of
individuals and is used to make decisions that affect people’s lives,
there is an imperative to manage its quality and reliability.
• Potential for misuse: Misusing data can negatively affect people and
organizations, so there is an ethical imperative to prevent the misuse
of data.
• Economic value of data: Data has economic value. Ethics of data
ownership should determine how that value can be accessed and by
whom.
36. Context Diagram >
• There is an ethical
imperative not only to
protect data, but also to
manage quality
37. Business Driver
• Ethical data handling can increase the trustworthiness of an
organization and the organization’s data and process outcomes.
• This can create better relationships between the organization and its
stakeholders.
“The emerging roles of Chief Data Officer, Chief Risk Officer, Chief Privacy Officer,
and Chief Analytics Officer are focused on controlling risk by establishing
acceptable practices for data handling “
38. Ethical Principles for Data
• Respect for Person : respects their dignity and autonomy as
human individuals
• Beneficence : do not harm; maximize possible benefits and
minimize possible harms.
• Justice : fair and equitable treatment of people
40. Risks of Unethical Data Handling Practices
• Timing
• Misleading Visualizations
• Unclear Definitions or Invalid Comparisons
• Bias ( Data Collection for pre-defined result, Biased use of data collected,
Hunch and search, Biased sampling methodology, Context and Culture )
• Transforming and Integrating Data ( Limited knowledge of data’s origin
and lineage, Data of poor quality, Unreliable Metadata, No
Documentation)
• Obfuscation / Redaction of Data ( Data aggregation, Data Marking, Data
Masking)
41. Establishing an Ethical Data Culture
STEPS :
• Review Current State Data Handling
Practices
• Identify Principles, Practices, and Risk
Factors
• Create an Ethical Data Handling Strategy
and Roadmap
• Adopt a Socially Responsible Ethical Risk
Model
43. Data Ethics and Governance
• Oversight for the appropriate handling of data falls under both data
governance and legal counsel.
• Keep up-to-date on legal changes
• CDMP formal code of ethics
44. • LET’S GET PRACTICE
•https://wato.xyz/cdmppractice2
passcode : cdmp
46. • LET’S GET PRACTICE
•https://wato.xyz/cdmppractice3
passcode : cdmp
47.
48. GOVERNANCE VS MANAGEMENT
DATA GOVERNANCE = Ensure The Data Managed Properly
DATA MANAGEMENT = ensure an organization gets value out of its data
“ SCOPE & FOCUS DATA GOVERNANCE PROGRAMS
OREGANITATION NEEDS “
49.
50. Context Diagram
DATA GOVERNANCE PROGRAMS :
• Strategy: Defining, communicating, and driving execution of Data Strategy and Data
Governance Strategy
• •Policy: Setting and enforcing policies related to data and Metadata management, access,
usage, security, and quality
• •Standards and quality: Setting and enforcing Data Quality and Data Architecture standards
• •Oversight: Providing hands-on observation, audit, and correction in key areas of quality,
policy, and data management (often referred to as stewardship)
• •Compliance: Ensuring the organization can meet data-related regulatory compliance
requirements
• •Issue management: Identifying, defining, escalating, and resolving issues related to data
security, data access, data quality, regulatory compliance, data ownership, policy, standards,
terminology, or data governance procedures
• •Data management projects: Sponsoring efforts to improve data management practices
• •Data asset valuation: Setting standards and processes to consistently define the business
value of data assets
51.
52. Goals
1. Enable an organization to
manage its data as an asset.
2. Define, approve,
communicate, and implement
principles, policies, procedures,
metrics, tools, and
responsibilities for data
management.
3. Monitor and guide policy
compliance, data usage, and
management activities.
53.
54. DG Business Driver
• Common Driver : regulatory compliance, especially for heavily
regulated industries
• Focus on reducing risks or improving processes :
Reducing Risk : General risk management, Data security , Privacy
Improving Processes: Regulatory compliance, Data quality improvement,
Metadata Management. Efficiency in development projects (SDLC) , Vendor
management
58. Data Governance vs IT Governance
Data governance is separate from IT governance.
• IT governance makes decisions about IT investments, the IT
application portfolio, and the IT project portfolio – in other words,
hardware, software, and overall technical architecture.
• IT governance aligns the IT strategies and investments with enterprise
goals and strategies.
• The COBIT (Control Objectives for Information and Related
Technology) framework provides standards for IT governance,
59.
60. DG Goals and Principles
Data Governance is to enable an organization to manage data as an
asset. DG Program must be :
61.
62. DG Essential Concept
Data governance represents an inherent separation of duty between
oversight and execution
68. Data Stewardship
• Data Stewardship is the most common label to describe
accountability and responsibility for data and processes that ensure
effective control and use of data assets
• Core activities : Creating and managing core Metadata,
Documenting rules and standards, Managing data quality issues,
Executing operational data governance activities
76. • LET’S GET PRACTICE
•https://wato.xyz/cdmppractice4
passcode : cdmp
77.
78. What is Architecture ?
• Architecture refers to an organized arrangement of component
elements intended to optimize the function, performance, feasibility,
cost, and aesthetics of an overall structure or system
79. Data Architecture Perspective
Data Architecture will be considered from the following perspectives:
•Data Architecture outcomes
•Data Architecture activities
•Data Architecture behavior
Together, these three form the essential components of Data
Architecture.
80.
81. Introduction
• The most detailed Data Architecture design document is a formal
enterprise data model, containing data names, comprehensive data
and Metadata definitions, conceptual and logical entities and
relationships, and business rules.
• Physical data models are included, but as a product of data modeling
and design, rather than Data Architecture.
85. Goals
1. Identify data storage and
processing requirements.
2. Design structures and plans to
meet the current and long-term
data requirements of the
enterprise.
3. Strategically prepare
organizations to quickly evolve
their products, services, and data
to take advantageof business
opportunities inherent in
emerging technologies.
89. Zachman’ Columns
• What (the inventory column): Entities used to build the architecture
• How (the process column): Activities performed
• Where (the distribution column): Business location and technology
location
• Who (the responsibility column): Roles and organizations
• When (the timing column): Intervals, events, cycles, and schedules
• Why (the motivation column): Goals, strategies, and means
93. Enterprise Data Architecture
Enterprise Data Architecture defines standard terms and designs
for the elements that are important to the organization.
• Enterprise Data Model (EDM): The EDM is a holistic, enterprise-level,
implementation-independentconceptual or logical data model
providing a common consistent view of data across the enterprise.
• Data Flow Design: Defines the requirements and master blueprint for
storage and processing acrossdatabases, applications, platforms, and
networks (the components).
94.
95.
96. Project Development Method
• Waterfall methods: Understand the requirements and construct
systems in sequential phases as part ofan overall enterprise design.
• •Incremental methods: Learn and construct in gradual steps (i.e.,
mini-waterfalls). This method createsprototypes based on vague
overall requirements. The initiation phase is crucial;
• •Agile, iterative, methods: Learn, construct, and test in discrete
delivery packages (called ‘sprints’)that are small enough that if work
needs to be discarded, not much is lost.
107. Goals and Principles
• The goal of data modeling is to confirm and document understanding of
different perspectives, which leads to applications that more closely align
with current and future business requirements, and creates a foundation
to successfully complete broad-scoped initiatives such as Master Data
Management and data governance programs.
• Confirming and documenting understanding of different perspectives
facilitates :
- Formalization
- Scope Definition
- Knowledge retention/documentation
108.
109. Types of Data that are Modeled
• Category information: Data used to classify and assign types to
things.
• Resource information: Basic profiles of resources needed conduct
operational processes such asProduct, Customer, Supplier, Facility,
Organization, and Account.
• Business event information: Data created while operational
processes are in progress.
• Detail transaction information: Detailed transaction information is
often produced through point-of-sale systems (either in stores or
online).
• Data at Rest
116. Relational data in model Scheme
• A foreign key is used in physical and sometimes logical
relational data modeling schemes to represent a
relationship
126. • LET’S GET PRACTICE
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127.
128. Introduction
• Data Storage and Operations includes the design, implementation,
and support of stored data, to maximize its value throughout its
lifecycle, from creation/acquisition to disposal.
• Data Storage and Operations includes two sub-activities:
1. Database Support
2. Database Support Technology
• Play Key Roles : DBA
129.
130. Context Diagram
The goals of data storage
and operations include:
• Managing the
availability of data
throughout the data
lifecycle
• Ensuring the integrity
of data assets
• Managing the
performance of data
transactions
131.
132. SLA
Service Level Agreement Principles Practice:
• The Service Level Agreement(SLA) can reflect DBA-recommended and
developer-accepted methods of ensuring data integrity and data
security. The SLA should reflect the transfer of responsibility from the
DBAs to the development team if the development team will be
coding their own database update procedures or data accesslayer.
• This prevents an ‘all or nothing’ approach to standards.
133.
134. Procedural and Development DBAs
Procedural DBAs :
• Lead the review and administration of procedural database objects.
• Specializes in development and support of procedural logic controlled
and execute by the DBMS:
• Development DBAs focus on data design activities including creating
and managing special use databases
137. Database Processing Types
CAP (BREWER’S THEOREM ) Consistency, Availability and Partition
How Distribution System Closely match with :
• ACID (Atomicity, Consistency, Isolation, Durability)
• BASE (Basically Available, Soft State, Eventual Consistency)
145. Introduction
• Data Security includes the planning, development, and execution of
security policies and procedures to provide proper authentication,
authorization, access, and auditing of data and information assets.
157. Assess Current Security Risks
• The sensitivity of the data stored or in transit
• The requirements to protect that data, and
• The current security protections in place
158. Other Concerns
• Regulatory Requirements
• Data Security Standards
• Data Security Roles
• Tools & Technique
• Guidelines
159. Chapter 8 : Data Integration and
Interoperability
160.
161. Introduction
Data Integration and Interoperability (DII) describes processes related to the movement
and consolidation of data within and between data stores, applications and organizations.
Org. Data Management Function Depend Data Management Area Depend
• Data migration and conversion
• Data consolidation into hubs or marts
• Integration of vendor packages into an
organization’s application portfolio
• Data sharing between applications and across
organizations
• Distributing data across data stores and data
centers
• Archiving data
• Managing data interfaces
• Obtaining and ingesting external data
• Integrating structured and unstructured data
• Providing operational intelligence and
management decision support
• Data Governance
• Data Architecture:
• Data Security:
• Metadata:
• Data Storage and Operations
• Data Modeling and Design
162.
163.
164.
165. Essential Concepts
• Extract, Transform, and Load (ETL)
• Extract, Transform, and Load (ELT)
• LATENCY, REPLICATION …
169. Interaction Model
• Point-to-point (Pass Data Directly )
• Hub-and-spoke (Consolidates share data)
• Publish - Subscribe (System push data – Other System Pull data –
Distributed to subscriber)
173. Introduction
• Document and Content Management entails controlling the capture,
storage, access, and use of data and information stored outside
relational databases
• In some Organizations unstructured data has a direct relationship to
structured data.
• Management decisions about such content should be applied
consistently.
174.
175. Business Driver
• Regulatory compliance
• the ability to respond to litigation and e-discovery requests, and
business continuity requirements.
• Good records management can also help organizations become more
efficient
• Well-organized, searchable websites that result from effective
management of ontologies
• E-discovery is the process of finding electronic records that might
serve as evidence in a legal action.
178. ARMA International Principles - 2009
Generally Acceptable Recordkeeping Principles® (GARP)
• Principle of Accountability
• Principle of Integrity
• Principle of Protection
• Principle of Compliance
• Principle of Availability
• Principle of Retention
• Principle of Disposition
• Principle of Transparency
179.
180. Essential Concepts
• Content : document is to content what a bucket is to water: a container. Content refers
to the data and information inside the file, document, or website.
• Controlled Vocabularies : is a defined list of explicitly allowed terms used to index,
categorize, tag, sort, and retrieve content through browsing and searching. : ,
• Documents and Records : Documents are electronic or paper objects that contain
instructions for tasks, requirements for how and when to perform a task or function,
and logs of task execution and decisions. Documents can communicate and share
information and knowledge. Examples of documents include procedures, protocols,
methods, and specifications. ,
• Data Map : is an inventory of all ESI data sources, applications, and IT environments
that includes the owners of the applications, custodians, relevant geographical
locations, and data types
• E-Discovery , etc.
186. Activities - Plan for Record Management
• Records management starts with a clear definition of what
constitutes a record.
• Managing electronic records requires decisions about where to store
current, active records and how to archive older records
187.
188. Activities - Manage the Lifecycle
• Capture Records and Content : Capturing content is the first step to
managing it. Electronic content is often already in a format to be stored in
electronic repositories.
• Manage Versioning and Control : Formal, Revision, Custody
• Backup and Recovery : The document / record management system needs
to be included in the organization’s overall corporate backup and recovery
activities, including business continuity and disaster recovery planning.
• Manage Retention and Disposal : Effective document / records
management requires clear policies and procedures, especially regarding
retention and disposal of records.
• Audit Documents / Records : Document / records management requires
periodic auditing to ensure that the right information is getting to the right
people at the right time for decision-making or performing operational
activities
189. Manage Versioning and Control
ANSI Standard 859 has three levels of control of data:
• •Formal control requires formal change initiation, thorough
evaluation for impact, decision by achange authority, and full status
accounting of implementation and validation to stakeholders
• •Revision control is less formal, notifying stakeholders and
incrementing versions when a change isrequired
• •Custody control is the least formal, merely requiring safe storage
and a means of retrieval
191. • LET’S TRY OUT (90 questions in 80 minutes )
•https://wato.xyz/cdmptryout
passcode : cdmp
192.
193. Introduction
• In any organization, certain data is required across business areas,
processes, and systems.
• The overall organization and its customers benefit if this data is
shared and all business units can access the same customer lists,
geographic location codes, business unit lists, delivery options, part
lists, accounting cost center codes, governmental tax codes, and
other data used to run the business.
194.
195.
196. Business Driver
Master Data Management
• Meeting organizational data requirements
• Managing data quality
• Managing the costs of data integration
• Reducing risk
The drivers for managing Reference Data are similar. Centrally managed
Reference Data enables organizations to:
• Meet data requirements for multiple initiatives and reduce the risks and
costs of data integration through use of consistent Reference Data
• Manage the quality of Reference Data
197.
198. Differences Between Master and Reference Data
• Different types of data play different roles within an organization. They
also have different management requirements.
• Six-layer taxonomy of data that includes Metadata, Reference Data,
enterprise structure data, transaction structure data, transaction
activity data, and transaction audit data (Chisholm, 2008; Talburt and
Zhou, 2015).
• Master Data as an aggregation of Reference Data, enterprise structure
data, and transaction structure data
199.
200. Master Data - Trusted Source, Golden Record
• A Trusted Source is recognized as the ‘best version of the truth’ based
on a combination of automated rules and manual stewardship of data
content.
• A trusted source may also be referred to as a Single View, 360° View.
• Any MDM system should be managed so that it is a trusted source.
Within a trusted source, records that represent the most accurate
data about entity instances can be referred to as Golden Records.
• ‘Golden Record’ does not mean that it is always a 100% complete and
100% accurate representation of all the entities within the
organization (especially in organizations that have multiple SOR’s
supplying data to the Master Data environment).
201.
202. Data Sharing Architecture
Three basic approaches to implementing a Master Data hub
environment :
• A Registry
• In a Transaction Hub
• A Consolidated
203.
204. Party Master Data
• Party Master Data includes data about individuals, organizations, and
the roles they play in business relationships.
• In the commercial environment, parties include customers,
employees, vendors, partners, and competitors.
• In the public sector, parties are usually citizens
• Customer Relationship Management (CRM) systems manage Master
Data about customers. The goal of CRM is to provide complete and
accurate information about each and every customer.
205.
206. Master Data Management Key Processing Steps
• Key processing steps for MDM includes data model management;
data acquisition; data validation, standardization, and enrichment;
entity resolution; and stewardship and sharing.
207.
208. • Product Master Data can focus on an organization’s internal
products and services or on industry-wide (including competitor)
products and services.
• Different types of product Master Data solutions support different
business functions.
209.
210. Entity Resolution and Identifier Management
Entity resolution is the process of determining
whether two references to real world objects refer
to the same object or to different objects (Talburt,
2011).
Entity resolution is a decision-making process
211. Entity Resolution and Identifier Management
Matching, or candidate identification, is the process of identifying how
different records may relate to a single entity. The risks with this
process are:
• False positives: Two references that do not represent the same entity
are linked with a single identifier. This results in one identifier that
refers to more than one real-world entity instance.
• False negatives: Two references represent the same entity but they
are not linked with a single identifier. This results in multiple
identifiers that refer to the same real-world entity when each
instanceis expected to have one-and-only-one identifier.