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.
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 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 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 document discusses data development and data modeling concepts. It describes data development as defining data requirements, designing data solutions, and implementing components like databases, reports, and interfaces. Effective data development requires collaboration between business experts, data architects, analysts and developers. It also outlines the key activities in data modeling including analyzing information needs, developing conceptual, logical and physical data models, designing databases and information products, and implementing and testing the data solution.
This document discusses data security management. It outlines key concepts and activities including understanding business and regulatory requirements, defining security policies, standards, controls and procedures, managing users, passwords and permissions. The goal is to protect information through proper authentication, authorization, access and auditing in alignment with privacy needs and regulations.
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.
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.
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.
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 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 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 document discusses data development and data modeling concepts. It describes data development as defining data requirements, designing data solutions, and implementing components like databases, reports, and interfaces. Effective data development requires collaboration between business experts, data architects, analysts and developers. It also outlines the key activities in data modeling including analyzing information needs, developing conceptual, logical and physical data models, designing databases and information products, and implementing and testing the data solution.
This document discusses data security management. It outlines key concepts and activities including understanding business and regulatory requirements, defining security policies, standards, controls and procedures, managing users, passwords and permissions. The goal is to protect information through proper authentication, authorization, access and auditing in alignment with privacy needs and regulations.
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.
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.
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.
Tackling Data Quality problems requires more than a series of tactical, one-off improvement projects. By their nature, many Data Quality problems extend across and often beyond an organization. Addressing these issues requires a holistic architectural approach combining people, process, and technology. Join Nigel Turner and Donna Burbank as they provide practical ways to control Data Quality issues in your organization.
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.
Chapter 9: Data Warehousing and Business Intelligence ManagementAhmed Alorage
The document discusses concepts related to data warehousing and business intelligence management. It provides an overview of key terms and components, including Inmon and Kimball's approaches to data warehouse architecture. Inmon defined the classic characteristics of a data warehouse and his "Corporate Information Factory" model, which includes raw operational data, an operational data store, data warehouse, and data marts. Kimball emphasized dimensional modeling and his "DW chess pieces" components to structure data for analysis. The document then covers typical activities involved in data warehousing and business intelligence management.
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
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 .
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.
To take a “ready, aim, fire” tactic to implement Data Governance, many organizations assess themselves against industry best practices. The process is not difficult or time-consuming and can directly assure that your activities target your specific needs. Best practices are always a strong place to start.
Join Bob Seiner for this popular RWDG topic, where he will provide the information you need to set your program in the best possible direction. Bob will walk you through the steps of conducting an assessment and share with you a set of typical results from taking this action. You may be surprised at how easy it is to organize the assessment and may hear results that stimulate the actions that you need to take.
In this webinar, Bob will share:
- The value of performing a Data Governance best practice assessment
- A practical list of industry Data Governance best practices
- Criteria to determine if a practice is best practice
- Steps to follow to complete an assessment
- Typical recommendations and actions that result from an assessment
Glossaries, Dictionaries, and Catalogs Result in Data GovernanceDATAVERSITY
Data catalogs, business glossaries, and data dictionaries house metadata that is important to your organization’s governance of data. People in your organization need to be engaged in leveraging the tools, understanding the data that is available, who is responsible for the data, and knowing how to get their hands on the data to perform their job function. The metadata will not govern itself.
Join Bob Seiner for the webinar where he will discuss how glossaries, dictionaries, and catalogs can result in effective Data Governance. People must have confidence in the metadata associated with the data that you need them to trust. Therefore, the metadata in your data catalog, business glossary, and data dictionary must result in governed data. Learn how glossaries, dictionaries, and catalogs can result in Data Governance in this webinar.
Bob will discuss the following subjects in this webinar:
- Successful Data Governance relies on value from very important tools
- What it means to govern your data catalog, business glossary, and data dictionary
- Why governing the metadata in these tools is important
- The roles necessary to govern these tools
- Governance expected from metadata in catalogs, glossaries, and dictionaries
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
Building a Data Strategy – Practical Steps for Aligning with Business GoalsDATAVERSITY
Developing a Data Strategy for your organization can seem like a daunting task – but it’s worth the effort. Getting your Data Strategy right can provide significant value, as data drives many of the key initiatives in today’s marketplace – from digital transformation, to marketing, to customer centricity, to population health, and more. This webinar will help demystify Data Strategy and its relationship to Data Architecture and will provide concrete, practical ways to get started.
Data Management vs. Data Governance ProgramDATAVERSITY
This document contains a presentation by Peter Aiken on data programs, specifically distinguishing between data management and data governance. Some key points:
- Data management focuses on understanding current and future data needs and making data effective and efficient for business activities. Data governance establishes authority and control over data management.
- Both data management and governance are needed for success. Data management executes practices while data governance provides oversight and guidance.
- Messaging should emphasize the critical importance of data and having a singular focus on improving data's role in achieving organizational strategy.
- A data strategy should define each practice area's relationship and focus on continuous improvement over multiple iterations.
Chapter 13: Professional DevelopmentAhmed Alorage
This document discusses professional development for data management professionals. It covers characteristics of a profession including certification, continuing education, ethics, and notable professionals. Specifically, it outlines the Certified Data Management Professional (CDMP) certification process, including required exams in core IS and data specialty areas. It also discusses ways to prepare for exams, accepted substitute vendor certifications, continuing education requirements to maintain certification, and emphasizes the importance of maintaining high ethical standards when working with data.
How to Build & Sustain a Data Governance Operating Model DATUM LLC
Learn how to execute a data governance strategy through creation of a successful business case and operating model.
Originally presented to an audience of 400+ at the Master Data Management & Data Governance Summit.
Visit www.datumstrategy.com for more!
Data Management and Data Governance are the same thing! Aren’t they? Most people would say that this line of thinking is absurd – or even worse. There is NO WAY that they are the same thing. Or are they?
Join Bob Seiner and Anthony Algmin for a lively, interactive, and entertaining discussion targeted at providing attendees ways to consider relating these two disciplines. You’ve never attended a session like this.
In this session, Bob and Anthony will discuss:
- The similarities between Data Management and Data Governance
- The differences between the two
- How to use Data Management to sell Data Governance … and the other way around
- Deciding if the two disciplines are the same … or different
Data Governance — Aligning Technical and Business ApproachesDATAVERSITY
Data Governance can have a varied definition, depending on the audience. To many, data governance consists of committee meetings and stewardship roles. To others, it focuses on technical data management and controls. Holistic data governance combines both of these aspects, and a robust data architecture and associated diagrams can be the “glue” that binds business and IT governance together. Join this webinar for practical tips and hands-on exercises for aligning data architecture & data governance for business and IT success.
Chapter 12: Data Quality ManagementAhmed Alorage
This document discusses data quality management (DQM). It covers DQM concepts and activities, including developing data quality awareness, defining data quality requirements, profiling and assessing data quality, and defining metrics. The key DQM approach is the Deming cycle of planning, deploying, monitoring, and acting to continuously improve data quality. Data quality requirements are identified by reviewing business policies and rules to understand dimensions like accuracy, completeness, consistency and more.
DAS Slides: Data Governance and Data Architecture – Alignment and SynergiesDATAVERSITY
Data Governance can have a varied definition, depending on the audience. To many, Data Governance consists of committee meetings and stewardship roles. To others, it focuses on technical Data Management and controls. Holistic Data Governance combines both of these aspects, and a robust Data Architecture and associated diagrams can be the “glue” that binds business and IT governance together. Join this webinar for practical tips and hands-on exercises for aligning Data Architecture and Data Governance for business and IT success.
Data protection and privacy regulations such as the EU’s General Data Protection Regulation (GDPR), the California Consumer Privacy Act (CCPA), and Singapore’s Personal Data Protection Act (PDPA) have been major drivers for data governance initiatives and the emergence of data catalog solutions. Organizations have an ever-increasing appetite to leverage their data for business advantage, either through internal collaboration, data sharing across ecosystems, direct commercialization, or as the basis for AI-driven business decision-making. This requires data governance and especially data asset catalog solutions to step up once again and enable data-driven businesses to leverage their data responsibly, ethically, compliantly, and accountably.
This presentation explores how data catalog has become a key technology enabler in overcoming these challenges.
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 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 an overview of the DAMA organization and their development of the DAMA-DMBOK Guide to establish a standard body of knowledge for the emerging data management profession.
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.
Tackling Data Quality problems requires more than a series of tactical, one-off improvement projects. By their nature, many Data Quality problems extend across and often beyond an organization. Addressing these issues requires a holistic architectural approach combining people, process, and technology. Join Nigel Turner and Donna Burbank as they provide practical ways to control Data Quality issues in your organization.
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.
Chapter 9: Data Warehousing and Business Intelligence ManagementAhmed Alorage
The document discusses concepts related to data warehousing and business intelligence management. It provides an overview of key terms and components, including Inmon and Kimball's approaches to data warehouse architecture. Inmon defined the classic characteristics of a data warehouse and his "Corporate Information Factory" model, which includes raw operational data, an operational data store, data warehouse, and data marts. Kimball emphasized dimensional modeling and his "DW chess pieces" components to structure data for analysis. The document then covers typical activities involved in data warehousing and business intelligence management.
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
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 .
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.
To take a “ready, aim, fire” tactic to implement Data Governance, many organizations assess themselves against industry best practices. The process is not difficult or time-consuming and can directly assure that your activities target your specific needs. Best practices are always a strong place to start.
Join Bob Seiner for this popular RWDG topic, where he will provide the information you need to set your program in the best possible direction. Bob will walk you through the steps of conducting an assessment and share with you a set of typical results from taking this action. You may be surprised at how easy it is to organize the assessment and may hear results that stimulate the actions that you need to take.
In this webinar, Bob will share:
- The value of performing a Data Governance best practice assessment
- A practical list of industry Data Governance best practices
- Criteria to determine if a practice is best practice
- Steps to follow to complete an assessment
- Typical recommendations and actions that result from an assessment
Glossaries, Dictionaries, and Catalogs Result in Data GovernanceDATAVERSITY
Data catalogs, business glossaries, and data dictionaries house metadata that is important to your organization’s governance of data. People in your organization need to be engaged in leveraging the tools, understanding the data that is available, who is responsible for the data, and knowing how to get their hands on the data to perform their job function. The metadata will not govern itself.
Join Bob Seiner for the webinar where he will discuss how glossaries, dictionaries, and catalogs can result in effective Data Governance. People must have confidence in the metadata associated with the data that you need them to trust. Therefore, the metadata in your data catalog, business glossary, and data dictionary must result in governed data. Learn how glossaries, dictionaries, and catalogs can result in Data Governance in this webinar.
Bob will discuss the following subjects in this webinar:
- Successful Data Governance relies on value from very important tools
- What it means to govern your data catalog, business glossary, and data dictionary
- Why governing the metadata in these tools is important
- The roles necessary to govern these tools
- Governance expected from metadata in catalogs, glossaries, and dictionaries
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
Building a Data Strategy – Practical Steps for Aligning with Business GoalsDATAVERSITY
Developing a Data Strategy for your organization can seem like a daunting task – but it’s worth the effort. Getting your Data Strategy right can provide significant value, as data drives many of the key initiatives in today’s marketplace – from digital transformation, to marketing, to customer centricity, to population health, and more. This webinar will help demystify Data Strategy and its relationship to Data Architecture and will provide concrete, practical ways to get started.
Data Management vs. Data Governance ProgramDATAVERSITY
This document contains a presentation by Peter Aiken on data programs, specifically distinguishing between data management and data governance. Some key points:
- Data management focuses on understanding current and future data needs and making data effective and efficient for business activities. Data governance establishes authority and control over data management.
- Both data management and governance are needed for success. Data management executes practices while data governance provides oversight and guidance.
- Messaging should emphasize the critical importance of data and having a singular focus on improving data's role in achieving organizational strategy.
- A data strategy should define each practice area's relationship and focus on continuous improvement over multiple iterations.
Chapter 13: Professional DevelopmentAhmed Alorage
This document discusses professional development for data management professionals. It covers characteristics of a profession including certification, continuing education, ethics, and notable professionals. Specifically, it outlines the Certified Data Management Professional (CDMP) certification process, including required exams in core IS and data specialty areas. It also discusses ways to prepare for exams, accepted substitute vendor certifications, continuing education requirements to maintain certification, and emphasizes the importance of maintaining high ethical standards when working with data.
How to Build & Sustain a Data Governance Operating Model DATUM LLC
Learn how to execute a data governance strategy through creation of a successful business case and operating model.
Originally presented to an audience of 400+ at the Master Data Management & Data Governance Summit.
Visit www.datumstrategy.com for more!
Data Management and Data Governance are the same thing! Aren’t they? Most people would say that this line of thinking is absurd – or even worse. There is NO WAY that they are the same thing. Or are they?
Join Bob Seiner and Anthony Algmin for a lively, interactive, and entertaining discussion targeted at providing attendees ways to consider relating these two disciplines. You’ve never attended a session like this.
In this session, Bob and Anthony will discuss:
- The similarities between Data Management and Data Governance
- The differences between the two
- How to use Data Management to sell Data Governance … and the other way around
- Deciding if the two disciplines are the same … or different
Data Governance — Aligning Technical and Business ApproachesDATAVERSITY
Data Governance can have a varied definition, depending on the audience. To many, data governance consists of committee meetings and stewardship roles. To others, it focuses on technical data management and controls. Holistic data governance combines both of these aspects, and a robust data architecture and associated diagrams can be the “glue” that binds business and IT governance together. Join this webinar for practical tips and hands-on exercises for aligning data architecture & data governance for business and IT success.
Chapter 12: Data Quality ManagementAhmed Alorage
This document discusses data quality management (DQM). It covers DQM concepts and activities, including developing data quality awareness, defining data quality requirements, profiling and assessing data quality, and defining metrics. The key DQM approach is the Deming cycle of planning, deploying, monitoring, and acting to continuously improve data quality. Data quality requirements are identified by reviewing business policies and rules to understand dimensions like accuracy, completeness, consistency and more.
DAS Slides: Data Governance and Data Architecture – Alignment and SynergiesDATAVERSITY
Data Governance can have a varied definition, depending on the audience. To many, Data Governance consists of committee meetings and stewardship roles. To others, it focuses on technical Data Management and controls. Holistic Data Governance combines both of these aspects, and a robust Data Architecture and associated diagrams can be the “glue” that binds business and IT governance together. Join this webinar for practical tips and hands-on exercises for aligning Data Architecture and Data Governance for business and IT success.
Data protection and privacy regulations such as the EU’s General Data Protection Regulation (GDPR), the California Consumer Privacy Act (CCPA), and Singapore’s Personal Data Protection Act (PDPA) have been major drivers for data governance initiatives and the emergence of data catalog solutions. Organizations have an ever-increasing appetite to leverage their data for business advantage, either through internal collaboration, data sharing across ecosystems, direct commercialization, or as the basis for AI-driven business decision-making. This requires data governance and especially data asset catalog solutions to step up once again and enable data-driven businesses to leverage their data responsibly, ethically, compliantly, and accountably.
This presentation explores how data catalog has become a key technology enabler in overcoming these challenges.
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 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 an overview of the DAMA organization and their development of the DAMA-DMBOK Guide to establish a standard body of knowledge for the emerging data management profession.
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.
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.
This is a slide deck that was assembled as a result of months of Project work at a Global Multinational. Collaboration with some incredibly smart people resulted in content that I wish I had come across prior to having to have assembled this.
The document discusses an overview of enterprise data governance. It describes the goals of data governance as making data usable, consistent, open, available and reliable across an organization. It outlines the roles and responsibilities involved in data governance including an oversight committee, data stewards, data custodians and various initiatives around master data management, data quality, naming conventions, metadata management and more. The document also discusses why organizations implement data governance and how to effectively implement a data governance program.
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
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.
The document discusses document and content management. It defines document management as the control over electronic and paper documents, including their storage, inventory and access. Content management is defined as organizing, categorizing and structuring access to information content to enable effective retrieval and reuse. The document outlines key concepts and activities for both document and content management, including planning, implementing systems, backup/recovery, retention, auditing and governance to ensure quality.
The document discusses data governance concepts and activities. It defines data governance as the exercise of authority and control over data asset management. The key activities of data governance include developing a data strategy and policies, overseeing data architecture and standards, ensuring regulatory compliance, managing issues, overseeing data management projects, and communicating guidelines. Data governance involves both business and technical roles working together through committees, councils and teams to effectively manage an organization's data assets.
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.
You Need a Data Catalog. Do You Know Why?Precisely
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2. Chapter 2 Objectives:
• Provide a detailed overview of data management that
includes:
• Introduction to the mission, goals, and business benefits of
data management.
• A process model for data management, identifying ten
functions and the components activities of each function.
• An overview of the format used in the context diagrams
that describe each function.
• An overview of the roles involved in activities across all ten
data management functions.
• An overview of the general classes of technology that
support data management.
• This Chapter will cover process, people, and technology as it
relates to overall data management.
3. 2.1 Introduction
• Provide evident Definition for “Data Management” as :
• The Planning and execution of Policies, Practices, and
Projects that Acquire, control, protect, deliver, and
enhance the value of data and information assets.
5. 2.2 Mission and Goals of Data management
• Mission:
• meet and exceed the information needs of all stakeholders in the
enterprise in terms of information availability, security and quality.
• Goals:
• should be Updated and vary from year to year and follow the
SMART Goals plan: “Specific, measurable, achievable, relevant, and
time-bound”, and include Strategic and non-Strategic Goals.
6. 2.2 Mission and Goals “Strategic Goals”
1. To understand the information needs of the enterprise and all its
stakeholders.
2. To capture, store, protect, and ensure the integrity of data assets.
3. To continually improve the quality of data and information, including:
• Data accuracy
• Data integrity
• Data integration
• The timeliness of data capture and presentation
• The relevance and usefulness of data
• The clarity and shared acceptance of data definitions.
4. To ensure privacy and confidentiality, and to prevent unauthorized or
inappropriate use of data and information.
5. To maximize the effective use and value of data and information assets.
7. 2.2 Mission and Goals “Non-Strategic Goals”
6. To control the cost of data management.
7. To promote a wider and deeper understanding of the
value of data assets
8. To manage information consistently across the
enterprise.
9. To align data management efforts and technology with
business needs.
8. 2.3 Guiding Principles
• Overall and general data management principles include:
• Data and information are valuable enterprise assets.
• Manage data and information carefully, like any other assets, by
ensuring adequate quality, security, integrity, protection, availability,
understanding, and effective use.
• Share responsibility for data management between business data
stewards and data management professionals
• Data management is a business function and a set of related disciplines.
• Data management is also and emerging and maturing profession within
the IT field.
9. 2.4 Functions and Activities
• Data management is a process of Functions and Activities:
• Data Governance: Considers as “High-Level planning and control over
data management”
• Data Architecture Management: Defining data needs of the enterprise
and designing the master blueprint “The main plan” to meet this
needs. Include “ enterprise data architecture” and related with
application system solutions and projects that implement enterprise
architecture.
• Data development: Designing, implementing and maintaining
solutions to meet the data needs of the enterprise. The data-focused
activities within “SDLC”, including data modeling, data requirements
analysis, and design, implementation and maintenance of database’
date-related components.
10. 2.4 Functions and Activities
• Data Operations Management: Planning, Control, and Support for
structured data assets across the data lifecycle, from Creation and
acquisition through archival and purge.
• Data Security Management: Planning, development, and execution of
security policies and procedures to provide proper authentication,
authorization, access and auditing of data and information.
• Reference and Master Data Management: Planning, implementation, and
control activities to ensure consistency with a “golden version” of
contextual data values.
• Data Warehousing and Business Intelligence Management: Planning,
implementation, and control processes to provide decision support data
and support for knowledge workers engaged in reporting, query and
analysis.
11. 2.4 Functions and Activities
• Document and Content Management: Planning, implementation and
control activities to store, protect and access data found within
electronic files and physical records ( including text, graphics, images
audio, and video)
• Meta-data Management: planning, implementation, and control
activities to enable easy access to high quality, integrated meta-data.
• Data Quality Management: planning, implementation, and control
activities that apply quality management techniques to measure,
improve and ensure the fitness of data for use.
12. 2.4 Functions and Activities
• Most of data management Activities overlap in scope with other within
and outside IT.
• Not all Data management activities Performed in every enterprise. A few
organization have plans, policies and programs in each of the ten
functions, Therefore.
• Each organization must determine an implementation approach consistent
with its size, goals , resources, and complexity depending on the nature
and the fundamental principles of data management
13. 2.4.1 Data Management Activities
Data Management Functions Activities Sub-Activities
Data Governance Data Management Planning • Understand Strategic Enterprise Data Needs
• Develop and Maintain the data Strategy
• Establish Data Professional Roles and Organizations
• Identify and Appoint Data Stewards
• Establish Data Governance and Stewardship Organizations
• Develop and Approve Data Policies, Standards and Procedures
• Review and Approve Data Architecture
• Plan and Sponsor Data Management Projects and Services
• Estimate data asset value and associated costs
Data Management Control • Supervise Data Professional Organizations and Staff
• Coordinate Data Governance Activities
• Manage and Resolve Data Related Issues
• Monitor and Ensure Regulatory Compliance
• Monitor and Enforce Conformance with Data Policies, Standards and
Architecture
• Oversee Data Management Projects and Services
• Communicate and Promote the Value of Data Assets
14. 2.4.1 Data Management Activities
Data Management Functions Activities
Data Architecture Management • Understand Enterprise Information Needs
• Develop and Maintain the Enterprise Data Model
• Analyze and Align with Other Business Model
• Define and Maintain the Database Architecture (same as 4.2.2)
• Define and Maintain The Data Integration Architecture (same as 6.3)
• Define and Maintain Enterprise Taxonomies and Namespeces (same as 8.2.1)
• Define and Maintain the Meta-data Architecture (same as 9.2)
Data Development Data Modeling, Analysis and Solution Design • Analyze Information Requirements
• Develop and Maintain Conceptual Data Models
• Develop and Maintain Logical Data Models
• Develop and Maintain Physical Data Models
Detailed Data Design • Design Physical Database
• Design Information Products
• Design Data Access Services
• Design Data Integration Services
15. 2.4.1 Data Management Activities
Data Management Functions Activities
Data Development Data Model and Design
Quality Management
• Develop Data Modeling and Design Standards
• Review Data Model and Database Design Quality
• Manage Data Model Versioning and Integration
Data Implementation • Implement Development/Test Database Changes
• Create and Maintain Test Data
• Migrate and Convert Data
• Build and Test Information Products
• Build and Test Data Access Services
• Validate Information Requirements
• Prepare for Data Deployment
Data Operations Management Database Support • Implement and Control Database Environments
• Acquire Externally Sourced Data
• Plan for Data Recovery
• Backup and Recover Data
• Set Database Performance Service Levels
• Monitor and Tune Database Performance
• Plan for Data Retention
• Archive, Retain, and Purge Data
• Support Specialized Databases
16. 2.4.1 Data Management Activities
Data Management Functions Activities
Data Operations Management Data Technology
Management
• Understand Data Technology Requirements
• Define The Data Technology Architecture (same as 2.4)
• Evaluate Data Technology
• Install and Administer Data Technology
• Inventory and Track Data Technology Licenses
• Support Data Technology Usage and Issues
Data Security Management • Understand Data Security Needs and Regulatory Requirements
• Define Data Security Policy
• Define Data Security Standards
• Define Data Security Controls and Procedures
• Manage Data Access Views and Permissions
• Monitor User Authentication and Access Behavior
• Classify Information Confidentiality
• Audit Data Security
Reference and Master Data
Management
• Understand Reference and Master Data Integration Needs
• Identify Master and Reference Data Sources and Contributors
• Define and Maintain the Data Integration Architecture (same as 2.5)
• Implement Reference and Master Data Management Solutions
• Define and Maintain Match Rules
• Establish “Golden” Records
17. 2.4.1 Data Management Activities
Data Management Functions Activities
Reference and Master Data
Management
• Define and Maintain Hierarchies and Affiliations
• Plan and Implement Integration of New Data Sources
• Replicate and Distribute Reference and Master Data
• Manage Changes to Reference and Master Data
Document and Content
Management
• Documents / Records
Management
• Plan for Managing Documents / Records
• Implement Documents / Records Management Systems for
Acquisition, Storage, Access, and Security Controls
• Backup and Recover Documents/ Records
• Audit Documents/ Records Management
• Content Management • Define and Maintain Enterprise Taxonomies (same as 2.7)
• Document/Index Information Content Meta-data
• Provide Content Access and Retrieval
• Govern for Quality Content
Meta-data Management • Understand Meta-data Requirements
• Define the Meta-data Architecture (same as 2.8)
• Develop and Maintain Meta-data Standards
• Implement a Managed Meta-data Environment
• Create and Maintain Meta-data
• Integrate Meta-data
• Manage Meta-data Repositories
• Distribute and Deliver Meta-data
• Query, Report, and Analyze Meta-data
18. 2.4.1 Data Management Activities
Data Management Functions Activities
Data Quality Management • Develop and Promote Data Quality Awareness
• Define Data Quality Requirement
• Profile, Analyze and Assess Data Quality
• Define Data Quality Metrics
• Define Data Quality Business Rules
• Test and Validate Data Quality Requirements
• Set and Evaluate Data Quality Service Levels
• Continuously Measure and Monitor Data Quality
• Manage Data Quality Issues
• Clean and Correct Data Quality Defects
• Design and Implement Operational DQM Procedures
• Monitor Operational DQM Procedures and Performance
19. 2.4.2 Activity Groups
• Each Data Management Activity fits into one or more data management
activity group.
• Previous Activities should belong to one the following Activity Groups:
• Planning Activities (P): Strategic and Tactical course for DM
Activities “Continually”
• Development Activities (D): “undertaken within implementation”,
part of (SDLC) creating data deliverables through analysis, design,
building, testing, preparation and deployment
• Control Activities (C): Supervisory activities performed in continual
way “On-Going basis”
• Operational Activities (O): Service and Support Activities performed
on an on-going basis. “Continually”
20. 2.5 Context Diagram Overview
• Through This Section, an overall definitions of The Context Diagram elements “Slide 4,
Figure 2.1”
• Begins from a definition and a list of goals at the top and the center of each diagram is
a blue box “DM Functions Activities” and How each chapter of the Book describes
these activities and sub-activities in depth.
• The third description of the section as called “The Lists Surrounding each center
activity box”:
• The Lists flowing into the activities: Inputs, Suppliers, Participants
• The Lists flowing out of the activities: Primary Deliverables, Consumers, Metrics
21. 2.5.1 Suppliers
• Responsible for supplying inputs for the activities.
• Related to multiple data management functions.
• Suppliers for data management in general include:
• Executives
• Data Creators
• External Sources
• Regulatory Bodies.
22. 2.5.2 Inputs
• Considers as Tangible things that each function needs to
initiate the activities.
• Several inputs are used by multiple functions.
• Include:
• Business Strategy
• Business Activity
• IT Activity, and
• Data Issues.
23. 2.5.3 Participants
• Includes :
• Data Creators,
• Information Consumers,
• Data Stewards, Data Professionals, and Executives.
• Involved in the data management process.
• Not necessarily directly or with accountability.
• Multiple participants may be involved in multiple functions.
24. 2.5.4 Tools
• To perform Activities in DM functions. Several tools are used by
multiple functions.
• In General, Includes:
• Data Modeling Tools
• Database Management Systems
• Data Integration and Quality Tools
• Business Intelligence Tools
• Document Management Tools
• Meta-data Repository Tools
25. 2.5.5 Primary Deliverables
• The responsibility of each function is Creating Primary Deliverables. Include:
• Data Strategy
• Data Architecture
• Data Services
• Database
• Data
• Information
• Knowledge
• Wisdom
• The ten Functions would have to cooperate to provide only eight deliverables.
26. 2.5.6 Consumers
• Consumers those who benefits from the primary deliverables
• Several Consumers benefit from multiple functions. Include:
• Clerical Workers
• Knowledge Workers
• Managers
• Executives
• Customers
27. 2.5.7 Metrics
• Metrics are the measurable things that each function is
responsible for creating.
• Several metrics measure multiple functions.
• Include:
• Data Value Metrics
• Data Quality Metrics
• Data Management Program Metrics
28. 2.6 Roles
• Each Company has a different approach to organizations, and individual
Roles and Responsibilities.
• An overview of some of the most common organizational categories and
individual roles.
• It is possible to outline the high-level types of organizations and
individual roles.
• This Sections will concentrate about the Types of Organizations and
Individuals “Job Titles and Roles Positions” in DM Boundaries.
29. 2.6.1 Types of Organizations
Types of DM Organizations Description
DM Services Organizations • Responsible for DM within IT
• Sometime refer as “Enterprise information Management (EIM)”, Center of
Excellence (COE)
• Members: DM executive, DM managers, Data Architects, Data Analysts, Data
Quality Analysts, DBA, Meta-Data Specialist, Data Model Administrators, Data
Warehouse Architects, Data Integration Architects, BI Analysts.
Data Governance Council • The Primary and highest authority organization for data Governance in
Organization.
• Members: executive data Stewards, DM leader, CIO, Chair the Council “Chief Data
Steward” Business Executive, Facilitators responsible for Council participation,
Communication, meeting preparation, meeting agendas, issues. Ets.
Data Stewardship Steering Committees • Cross-Functional Group ,Responsible for Support and oversight of a particular DM
initiative launched by Data Governance Council, such as “Enterprise Data
Architecture, Master Data Management, Meta-data Management”
• may delegate responsibilities to one or more committees
30. 2.6.1 Types of Organizations
Types of DM Organizations Description
Data Stewardship Teams • Group of business data stewards collaborating on data modeling, data definition, data
Quality requirement specification and data quality improvement
• Typically, in specified area of data Management
Data Governance Office (DGO) • A staff members in large enterprises supporting the efforts of the other organizations
types.
• May within or outside IT organization.
• Members: Data Stewardship Facilitators who enable Stewardship Activities performed by
business data stewards
31. 2.6.2 Types of Individual Roles
• In this sections the book identified different individuals' titles and there roles
according to DM matters.
• The Roles and Titles Start from the more Responsibilities and Top
Management, and Coordination. Extending to more specific area “such as
Architecture and Integration” or job in DM environment.
• Individual Roles such as:
• Business Data Stewards
• Coordinating Data Steward
• Executive Data Steward
• Data Stewardship Facilitator
• Data management Executive
• Data Architect / Enterprise Data Architect
• See Table 2.3. Page 34
32. 2.7 Technology
• Represent the Technology Related to Data
management
• Technology is covered in each chapter
• Categorized into two types:
• Software product Classes
• Specialized Hardware
33. 2.7.1 Software Product Classes
• Considers as The Metrics Mentioned in 2.5.7
• Several metrics measure multiple functions.
• Include:
• Data Value Metrics
• Data Quality Metrics
• Data Management Program Metrics
34. 2.7.2 Specialized Hardware
• Refers to Specialized hardware used to support unique data management
requirement.
• Types of specialized hardware include:
• Parallel Processing Computers: Often used to support vary large
databases “VLDB”. There are two common parallel Processing
architecture:
• SMP “Symmetrical multi-processing”
• MPP “Massive Parallel Processing”
• Data appliances: Servers built specifically for data transformation and
distribution. These Servers integrate with existing infrastructure
either directly as a plug in, or peripherally as a network connection.
35. Summery
• In first Section of this Chapter, Data Management Provide a consistent and
evident definition that Clear the Way in Several words “DM is The Planning and
execution of Policies, Practices, and Projects that Acquire, control, protect, deliver, and enhance
the value of data and information assets”
• As well, The Chapter Provide Context Diagram, Started with missions and Goals.
Thereafter, represent The methodologies of DM Functions ”The Blue Box”
Surrounding with Several Elements “Narrow In“ such as “Inputs, Suppliers,
Participants”, and “Narrow Out” which represent the results such as “Primary
Deliverables, Consumers and Metrics”, Along with Tools used in the middle.
• Thereafter, Chapter represent Activities that used in each function and assigned
to Group “Activities Groups”.
• Finally, Each elements of the Context Diagram have been Described and Defined
which present clear picture of The Suppliers, Inputs, Participants, Tools ,Primary
Deliverables, Consumers, Roles, Metrics and Technologies
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