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.
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.
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.
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.
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.
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 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.
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.
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.
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.
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.
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 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.
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.
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 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.
RWDG Slides: What is a Data Steward to do?DATAVERSITY
Most people recognize that Data Stewards play an essential role in their Data Governance and Information Governance programs. However, the manner in which Data Stewards are used is not the same from organization to organization. How you use Data Stewards depends on your goals for Data Governance.
Join Bob Seiner for this month’s RWDG webinar where he will share different ways to activate Data Stewards based on the purpose of your program. Bob will talk about options to extend existing Data Steward activity and how to build new functionality into the role of your Data Stewards.
In this webinar, Bob will discuss:
- The crucial role of the Data Steward in Data Governance
- Different types of Data Stewards and what they do
- Aligning Data Steward activities with program goals
- Improving existing Data Steward actions
- Finding new ways to use your Data Stewards
The document discusses how data modeling and data governance are related. It defines key terms like data modeling, data governance, and data stewardship. Data modeling requires business involvement, formal accountability, and attention to metadata - which are also traits of solid data governance programs. Therefore, data modeling can be considered a form of data governance. The document also outlines the role of the data modeler in a governance program and how data modeling best practices align with governance best practices. Finally, it discusses how the data model itself can be leveraged as a governance artifact.
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
Wallchart - Data Warehouse Documentation RoadmapDavid Walker
This document outlines the key components and processes involved in planning, designing, building, implementing and managing a data warehouse architecture. It includes sections on business requirements, data requirements, technical architecture, data modeling, ETL processes, testing, implementation, project management and documentation. The document provides a roadmap to guide an organization through each stage of developing an enterprise data warehouse.
Data Lake Architecture – Modern Strategies & ApproachesDATAVERSITY
Data Lake or Data Swamp? By now, we’ve likely all heard the comparison. Data Lake architectures have the opportunity to provide the ability to integrate vast amounts of disparate data across the organization for strategic business analytic value. But without a proper architecture and metadata management strategy in place, a Data Lake can quickly devolve into a swamp of information that is difficult to understand. This webinar will offer practical strategies to architect and manage your Data Lake in a way that optimizes its success.
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.
Data Governance Takes a Village (So Why is Everyone Hiding?)DATAVERSITY
Data governance represents both an obstacle and opportunity for enterprises everywhere. And many individuals may hesitate to embrace the change. Yet if led well, a governance initiative has the potential to launch a data community that drives innovation and data-driven decision-making for the wider business. (And yes, it can even be fun!). So how do you build a roadmap to success?
This session will gather four governance experts, including Mary Williams, Associate Director, Enterprise Data Governance at Exact Sciences, and Bob Seiner, author of Non-Invasive Data Governance, for a roundtable discussion about the challenges and opportunities of leading a governance initiative that people embrace. Join this webinar to learn:
- How to build an internal case for data governance and a data catalog
- Tips for picking a use case that builds confidence in your program
- How to mature your program and build your data community
This document discusses data governance and data architecture. It introduces data governance as the processes for managing data, including deciding data rights, making data decisions, and implementing those decisions. It describes how data architecture relates to data governance by providing patterns and structures for governing data. The document presents some common data architecture patterns, including a publish/subscribe pattern where a publisher pushes data to a hub and subscribers pull data from the hub. It also discusses how data architecture can support data governance goals through approaches like a subject area data model.
DAS Slides: Enterprise Architecture vs. Data ArchitectureDATAVERSITY
Enterprise Architecture (EA) provides a visual blueprint of the organization, and shows key inter-relationships between data, process, applications, and more. By abstracting these assets in a graphical view, it’s possible to see key interrelationships, particularly as they relate to data and its business impact across the organization. Join us for a discussion on how Data Architecture is a key component of an overall enterprise architecture for enhanced business value and success.
This document reviews several existing data management maturity models to identify characteristics of an effective model. It discusses maturity models in general and how they aim to measure the maturity of processes. The document reviews ISO/IEC 15504, the original maturity model standard, outlining its defined structure and relationship between the reference model and assessment model. It discusses how maturity levels and capability levels are used to characterize process maturity. The document also looks at issues with maturity models and how they can be improved.
Activate Data Governance Using the Data CatalogDATAVERSITY
This document discusses activating data governance using a data catalog. It compares active vs passive data governance, with active embedding governance into people's work through a catalog. The catalog plays a key role by allowing stewards to document definition, production, and usage of data in a centralized place. For governance to be effective, metadata from various sources must be consolidated and maintained in the catalog.
The document discusses data governance and why it is an imperative activity. It provides a historical perspective on data governance, noting that as data became more complex and valuable, the need for formal governance increased. The document outlines some key concepts for a successful data governance program, including having clearly defined policies covering data assets and processes, and establishing a strong culture that values data. It argues that proper data governance is now critical to business success in the same way as other core functions like finance.
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 .
Data Architecture Strategies: Data Architecture for Digital TransformationDATAVERSITY
MDM, data quality, data architecture, and more. At the same time, combining these foundational data management approaches with other innovative techniques can help drive organizational change as well as technological transformation. This webinar will provide practical steps for creating a data foundation for effective digital transformation.
Data Architecture - The Foundation for Enterprise Architecture and GovernanceDATAVERSITY
Organizations are faced with an increasingly complex data landscape, finding themselves unable to cope with exponentially increasing data volumes, compounded by additional regulatory requirements with increased fines for non-compliance. Enterprise architecture and data governance are often discussed at length, but often with different stakeholder audiences. This can result in complementary and sometimes conflicting initiatives rather than a focused, integrated approach. Data governance requires a solid data architecture foundation in order to support the pillars of enterprise architecture. In this session, IDERA’s Ron Huizenga will discuss a practical, integrated approach to effectively understand, define and implement an cohesive enterprise architecture and data governance discipline with integrated modeling and metadata management.
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.
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.
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 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.
RWDG Slides: What is a Data Steward to do?DATAVERSITY
Most people recognize that Data Stewards play an essential role in their Data Governance and Information Governance programs. However, the manner in which Data Stewards are used is not the same from organization to organization. How you use Data Stewards depends on your goals for Data Governance.
Join Bob Seiner for this month’s RWDG webinar where he will share different ways to activate Data Stewards based on the purpose of your program. Bob will talk about options to extend existing Data Steward activity and how to build new functionality into the role of your Data Stewards.
In this webinar, Bob will discuss:
- The crucial role of the Data Steward in Data Governance
- Different types of Data Stewards and what they do
- Aligning Data Steward activities with program goals
- Improving existing Data Steward actions
- Finding new ways to use your Data Stewards
The document discusses how data modeling and data governance are related. It defines key terms like data modeling, data governance, and data stewardship. Data modeling requires business involvement, formal accountability, and attention to metadata - which are also traits of solid data governance programs. Therefore, data modeling can be considered a form of data governance. The document also outlines the role of the data modeler in a governance program and how data modeling best practices align with governance best practices. Finally, it discusses how the data model itself can be leveraged as a governance artifact.
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
Wallchart - Data Warehouse Documentation RoadmapDavid Walker
This document outlines the key components and processes involved in planning, designing, building, implementing and managing a data warehouse architecture. It includes sections on business requirements, data requirements, technical architecture, data modeling, ETL processes, testing, implementation, project management and documentation. The document provides a roadmap to guide an organization through each stage of developing an enterprise data warehouse.
Data Lake Architecture – Modern Strategies & ApproachesDATAVERSITY
Data Lake or Data Swamp? By now, we’ve likely all heard the comparison. Data Lake architectures have the opportunity to provide the ability to integrate vast amounts of disparate data across the organization for strategic business analytic value. But without a proper architecture and metadata management strategy in place, a Data Lake can quickly devolve into a swamp of information that is difficult to understand. This webinar will offer practical strategies to architect and manage your Data Lake in a way that optimizes its success.
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.
Data Governance Takes a Village (So Why is Everyone Hiding?)DATAVERSITY
Data governance represents both an obstacle and opportunity for enterprises everywhere. And many individuals may hesitate to embrace the change. Yet if led well, a governance initiative has the potential to launch a data community that drives innovation and data-driven decision-making for the wider business. (And yes, it can even be fun!). So how do you build a roadmap to success?
This session will gather four governance experts, including Mary Williams, Associate Director, Enterprise Data Governance at Exact Sciences, and Bob Seiner, author of Non-Invasive Data Governance, for a roundtable discussion about the challenges and opportunities of leading a governance initiative that people embrace. Join this webinar to learn:
- How to build an internal case for data governance and a data catalog
- Tips for picking a use case that builds confidence in your program
- How to mature your program and build your data community
This document discusses data governance and data architecture. It introduces data governance as the processes for managing data, including deciding data rights, making data decisions, and implementing those decisions. It describes how data architecture relates to data governance by providing patterns and structures for governing data. The document presents some common data architecture patterns, including a publish/subscribe pattern where a publisher pushes data to a hub and subscribers pull data from the hub. It also discusses how data architecture can support data governance goals through approaches like a subject area data model.
DAS Slides: Enterprise Architecture vs. Data ArchitectureDATAVERSITY
Enterprise Architecture (EA) provides a visual blueprint of the organization, and shows key inter-relationships between data, process, applications, and more. By abstracting these assets in a graphical view, it’s possible to see key interrelationships, particularly as they relate to data and its business impact across the organization. Join us for a discussion on how Data Architecture is a key component of an overall enterprise architecture for enhanced business value and success.
This document reviews several existing data management maturity models to identify characteristics of an effective model. It discusses maturity models in general and how they aim to measure the maturity of processes. The document reviews ISO/IEC 15504, the original maturity model standard, outlining its defined structure and relationship between the reference model and assessment model. It discusses how maturity levels and capability levels are used to characterize process maturity. The document also looks at issues with maturity models and how they can be improved.
Activate Data Governance Using the Data CatalogDATAVERSITY
This document discusses activating data governance using a data catalog. It compares active vs passive data governance, with active embedding governance into people's work through a catalog. The catalog plays a key role by allowing stewards to document definition, production, and usage of data in a centralized place. For governance to be effective, metadata from various sources must be consolidated and maintained in the catalog.
The document discusses data governance and why it is an imperative activity. It provides a historical perspective on data governance, noting that as data became more complex and valuable, the need for formal governance increased. The document outlines some key concepts for a successful data governance program, including having clearly defined policies covering data assets and processes, and establishing a strong culture that values data. It argues that proper data governance is now critical to business success in the same way as other core functions like finance.
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 .
Data Architecture Strategies: Data Architecture for Digital TransformationDATAVERSITY
MDM, data quality, data architecture, and more. At the same time, combining these foundational data management approaches with other innovative techniques can help drive organizational change as well as technological transformation. This webinar will provide practical steps for creating a data foundation for effective digital transformation.
Data Architecture - The Foundation for Enterprise Architecture and GovernanceDATAVERSITY
Organizations are faced with an increasingly complex data landscape, finding themselves unable to cope with exponentially increasing data volumes, compounded by additional regulatory requirements with increased fines for non-compliance. Enterprise architecture and data governance are often discussed at length, but often with different stakeholder audiences. This can result in complementary and sometimes conflicting initiatives rather than a focused, integrated approach. Data governance requires a solid data architecture foundation in order to support the pillars of enterprise architecture. In this session, IDERA’s Ron Huizenga will discuss a practical, integrated approach to effectively understand, define and implement an cohesive enterprise architecture and data governance discipline with integrated modeling and metadata management.
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
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
Data development involves analyzing, designing, implementing, deploying, and maintaining data solutions to maximize the value of enterprise data. It includes defining data requirements, designing data components like databases and reports, and implementing these components. Effective data development requires collaboration between business experts, data architects, analysts, developers and other roles. The activities of data development follow the system development lifecycle and include data modeling, analysis, design, implementation, and maintenance.
Data Virtualization for Business Consumption (Australia)Denodo
This document discusses data virtualization and its benefits for business users. It summarizes that data virtualization can create a connected data landscape that is easily shared, empower business users with self-service BI tools, develop trusted high quality data, and support flexibility. It notes data virtualization provides a logical data layer that improves decision making, broadens data usage, and offers performant access to integrated data without moving or replicating source systems.
2. Business Data Analytics and Technology.pptxnirmalanr2
This document discusses business data analytics and technology. It covers topics such as data categorization, data issues including quality and privacy, database management systems, data warehouses, data marts, data mining, text mining, web mining, and business analytics software tools. The goal is to provide an overview of how organizations can effectively collect, store, analyze and utilize data to make informed business decisions.
Data governance maturity levels range from 1 to 5, with level 1 being the initial stage and level 5 being optimized. Level 1 is characterized by undocumented and ad-hoc processes, while level 2 has some repeatable policies but may lack rigor. Level 3 involves defined and documented policies, an enterprise data governance function, and cataloged data assets. Level 4 provides enterprise-wide visibility of data and executive support for data governance. Level 5 focuses on continually improving data through practices from the prior levels.
Data Governance & Data Architecture - Alignment and SynergiesDATAVERSITY
The definition of Data Governance can vary depending on the audience. To many, Data Governance consists of committees and stewardship roles. To others, it focuses on technical Data Management and controls. Holistic Data Governance combines both aspects, and a robust Data Architecture 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.
The art of information architecture in Office 365Simon Rawson
I gave this this presentation at the Collab365 Global Conference in September 2020. It covers the main elements you need to consider in developing an information architecture and management plan for Office 365
About Element22 - Unlocking The Power Of DataElement22
Element22 is a boutique data management advisory, design and technology solutions firm for the financial services industry. On a daily basis, we work with financial institutions to transfer them into data-driven organizations and meet regulatory requirements, such as BCBS 239.
Similar to Chapter 2: Data Management Overviews (20)
Learn SQL from basic queries to Advance queriesmanishkhaire30
Dive into the world of data analysis with our comprehensive guide on mastering SQL! This presentation offers a practical approach to learning SQL, focusing on real-world applications and hands-on practice. Whether you're a beginner or looking to sharpen your skills, this guide provides the tools you need to extract, analyze, and interpret data effectively.
Key Highlights:
Foundations of SQL: Understand the basics of SQL, including data retrieval, filtering, and aggregation.
Advanced Queries: Learn to craft complex queries to uncover deep insights from your data.
Data Trends and Patterns: Discover how to identify and interpret trends and patterns in your datasets.
Practical Examples: Follow step-by-step examples to apply SQL techniques in real-world scenarios.
Actionable Insights: Gain the skills to derive actionable insights that drive informed decision-making.
Join us on this journey to enhance your data analysis capabilities and unlock the full potential of SQL. Perfect for data enthusiasts, analysts, and anyone eager to harness the power of data!
#DataAnalysis #SQL #LearningSQL #DataInsights #DataScience #Analytics
ViewShift: Hassle-free Dynamic Policy Enforcement for Every Data LakeWalaa Eldin Moustafa
Dynamic policy enforcement is becoming an increasingly important topic in today’s world where data privacy and compliance is a top priority for companies, individuals, and regulators alike. In these slides, we discuss how LinkedIn implements a powerful dynamic policy enforcement engine, called ViewShift, and integrates it within its data lake. We show the query engine architecture and how catalog implementations can automatically route table resolutions to compliance-enforcing SQL views. Such views have a set of very interesting properties: (1) They are auto-generated from declarative data annotations. (2) They respect user-level consent and preferences (3) They are context-aware, encoding a different set of transformations for different use cases (4) They are portable; while the SQL logic is only implemented in one SQL dialect, it is accessible in all engines.
#SQL #Views #Privacy #Compliance #DataLake
STATATHON: Unleashing the Power of Statistics in a 48-Hour Knowledge Extravag...sameer shah
"Join us for STATATHON, a dynamic 2-day event dedicated to exploring statistical knowledge and its real-world applications. From theory to practice, participants engage in intensive learning sessions, workshops, and challenges, fostering a deeper understanding of statistical methodologies and their significance in various fields."
End-to-end pipeline agility - Berlin Buzzwords 2024Lars Albertsson
We describe how we achieve high change agility in data engineering by eliminating the fear of breaking downstream data pipelines through end-to-end pipeline testing, and by using schema metaprogramming to safely eliminate boilerplate involved in changes that affect whole pipelines.
A quick poll on agility in changing pipelines from end to end indicated a huge span in capabilities. For the question "How long time does it take for all downstream pipelines to be adapted to an upstream change," the median response was 6 months, but some respondents could do it in less than a day. When quantitative data engineering differences between the best and worst are measured, the span is often 100x-1000x, sometimes even more.
A long time ago, we suffered at Spotify from fear of changing pipelines due to not knowing what the impact might be downstream. We made plans for a technical solution to test pipelines end-to-end to mitigate that fear, but the effort failed for cultural reasons. We eventually solved this challenge, but in a different context. In this presentation we will describe how we test full pipelines effectively by manipulating workflow orchestration, which enables us to make changes in pipelines without fear of breaking downstream.
Making schema changes that affect many jobs also involves a lot of toil and boilerplate. Using schema-on-read mitigates some of it, but has drawbacks since it makes it more difficult to detect errors early. We will describe how we have rejected this tradeoff by applying schema metaprogramming, eliminating boilerplate but keeping the protection of static typing, thereby further improving agility to quickly modify data pipelines without fear.
4th Modern Marketing Reckoner by MMA Global India & Group M: 60+ experts on W...Social Samosa
The Modern Marketing Reckoner (MMR) is a comprehensive resource packed with POVs from 60+ industry leaders on how AI is transforming the 4 key pillars of marketing – product, place, price and promotions.
State of Artificial intelligence Report 2023kuntobimo2016
Artificial intelligence (AI) is a multidisciplinary field of science and engineering whose goal is to create intelligent machines.
We believe that AI will be a force multiplier on technological progress in our increasingly digital, data-driven world. This is because everything around us today, ranging from culture to consumer products, is a product of intelligence.
The State of AI Report is now in its sixth year. Consider this report as a compilation of the most interesting things we’ve seen with a goal of triggering an informed conversation about the state of AI and its implication for the future.
We consider the following key dimensions in our report:
Research: Technology breakthroughs and their capabilities.
Industry: Areas of commercial application for AI and its business impact.
Politics: Regulation of AI, its economic implications and the evolving geopolitics of AI.
Safety: Identifying and mitigating catastrophic risks that highly-capable future AI systems could pose to us.
Predictions: What we believe will happen in the next 12 months and a 2022 performance review to keep us honest.
Beyond the Basics of A/B Tests: Highly Innovative Experimentation Tactics You...Aggregage
This webinar will explore cutting-edge, less familiar but powerful experimentation methodologies which address well-known limitations of standard A/B Testing. Designed for data and product leaders, this session aims to inspire the embrace of innovative approaches and provide insights into the frontiers of experimentation!
06-04-2024 - NYC Tech Week - Discussion on Vector Databases, Unstructured Data and AI
Discussion on Vector Databases, Unstructured Data and AI
https://www.meetup.com/unstructured-data-meetup-new-york/
This meetup is for people working in unstructured data. Speakers will come present about related topics such as vector databases, LLMs, and managing data at scale. The intended audience of this group includes roles like machine learning engineers, data scientists, data engineers, software engineers, and PMs.This meetup was formerly Milvus Meetup, and is sponsored by Zilliz maintainers of Milvus.
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