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 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.
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 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 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.
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 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.
This document discusses data operations management. It defines data operations management as developing, maintaining, and supporting structured data to maximize value. Key activities include database support and data technology management. Database administrators play an important role in ensuring database availability, performance, integrity, and recoverability through activities like backups, monitoring, tuning, and setting service level agreements.
The document 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.
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 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 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.
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 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.
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.
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 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 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.
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.
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.
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
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
Data Architecture Best Practices for Advanced AnalyticsDATAVERSITY
Many organizations are immature when it comes to data and analytics use. The answer lies in delivering a greater level of insight from data, straight to the point of need.
There are so many Data Architecture best practices today, accumulated from years of practice. In this webinar, William will look at some Data Architecture best practices that he believes have emerged in the past two years and are not worked into many enterprise data programs yet. These are keepers and will be required to move towards, by one means or another, so it’s best to mindfully work them into the environment.
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.
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
Hexaware is a leading global provider of IT and BPO services with leadership positions in banking, financial services, insurance, transportation and logistics. It focuses on delivering business results through technology solutions such as business intelligence and analytics, enterprise applications, independent testing and legacy modernization. Hexaware has over 18 years of experience in providing business technology solutions and offers world class services, technology expertise and skilled human capital.
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.
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.
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 .
The first step towards understanding data assets’ impact on your organization is understanding what those assets mean for each other. Metadata — literally, data about data — is a practice area required by good systems development, and yet is also perhaps the most mislabeled and misunderstood Data Management practice. Understanding metadata and its associated technologies as more than just straightforward technological tools can provide powerful insight into the efficiency of organizational practices, and enable you to combine practices into sophisticated techniques, supporting larger and more complex business initiatives. Program learning objectives include:
* Understanding how to leverage metadata practices in support of business strategy
* Discuss foundational metadata concepts
* Guiding principles for and lessons previously learned from metadata and its practical uses applied strategy
* Understanding how to leverage metadata practices in support of business strategy
* Metadata strategies, including:
* Metadata is a gerund so don’t try to treat it as a noun
* Metadata is the language of Data Governance
* Treat glossaries/repositories as capabilities, not technology
The document discusses modern data architectures. It presents conceptual models for data ingestion, storage, processing, and insights/actions. It compares traditional vs modern architectures. The modern architecture uses a data lake for storage and allows for on-demand analysis. It provides an example of how this could be implemented on Microsoft Azure using services like Azure Data Lake Storage, Azure Data Bricks, and Azure Data Warehouse. It also outlines common data management functions such as data governance, architecture, development, operations, and security.
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-Ed Webinar: Best Practices with the DMMDATAVERSITY
The Data Management Maturity (DMM) model provides a framework for organizations to evaluate their current data management capabilities, identify gaps, and develop a roadmap for process improvement. The webinar will describe the DMM model, which is based on the Capability Maturity Model and allows organizations to assess their maturity level across various data management practices. Attendees will learn about using the DMM to guide strategic improvements to their organizational data management.
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 the activities involved in establishing an effective data governance program, including defining data governance for the organization, performing readiness assessments, developing goals and policies, underwriting data management projects, and engaging change management. The goal of data governance is to manage data as a valuable asset and guide data management activities according to policies and best practices. Setting up an appropriate operating framework, developing a governance strategy, and establishing organizational touchpoints are important for implementing a sustainable data governance program.
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.
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 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 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.
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.
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.
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
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
Data Architecture Best Practices for Advanced AnalyticsDATAVERSITY
Many organizations are immature when it comes to data and analytics use. The answer lies in delivering a greater level of insight from data, straight to the point of need.
There are so many Data Architecture best practices today, accumulated from years of practice. In this webinar, William will look at some Data Architecture best practices that he believes have emerged in the past two years and are not worked into many enterprise data programs yet. These are keepers and will be required to move towards, by one means or another, so it’s best to mindfully work them into the environment.
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.
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
Hexaware is a leading global provider of IT and BPO services with leadership positions in banking, financial services, insurance, transportation and logistics. It focuses on delivering business results through technology solutions such as business intelligence and analytics, enterprise applications, independent testing and legacy modernization. Hexaware has over 18 years of experience in providing business technology solutions and offers world class services, technology expertise and skilled human capital.
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.
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.
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 .
The first step towards understanding data assets’ impact on your organization is understanding what those assets mean for each other. Metadata — literally, data about data — is a practice area required by good systems development, and yet is also perhaps the most mislabeled and misunderstood Data Management practice. Understanding metadata and its associated technologies as more than just straightforward technological tools can provide powerful insight into the efficiency of organizational practices, and enable you to combine practices into sophisticated techniques, supporting larger and more complex business initiatives. Program learning objectives include:
* Understanding how to leverage metadata practices in support of business strategy
* Discuss foundational metadata concepts
* Guiding principles for and lessons previously learned from metadata and its practical uses applied strategy
* Understanding how to leverage metadata practices in support of business strategy
* Metadata strategies, including:
* Metadata is a gerund so don’t try to treat it as a noun
* Metadata is the language of Data Governance
* Treat glossaries/repositories as capabilities, not technology
The document discusses modern data architectures. It presents conceptual models for data ingestion, storage, processing, and insights/actions. It compares traditional vs modern architectures. The modern architecture uses a data lake for storage and allows for on-demand analysis. It provides an example of how this could be implemented on Microsoft Azure using services like Azure Data Lake Storage, Azure Data Bricks, and Azure Data Warehouse. It also outlines common data management functions such as data governance, architecture, development, operations, and security.
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-Ed Webinar: Best Practices with the DMMDATAVERSITY
The Data Management Maturity (DMM) model provides a framework for organizations to evaluate their current data management capabilities, identify gaps, and develop a roadmap for process improvement. The webinar will describe the DMM model, which is based on the Capability Maturity Model and allows organizations to assess their maturity level across various data management practices. Attendees will learn about using the DMM to guide strategic improvements to their organizational data management.
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 the activities involved in establishing an effective data governance program, including defining data governance for the organization, performing readiness assessments, developing goals and policies, underwriting data management projects, and engaging change management. The goal of data governance is to manage data as a valuable asset and guide data management activities according to policies and best practices. Setting up an appropriate operating framework, developing a governance strategy, and establishing organizational touchpoints are important for implementing a sustainable 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
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.
Module 1 Data Governance and Stewardship Core Concepts1.pptxAhmad Rjoub
The document discusses the differences between data management and data governance. It defines data management as planning, organizing, and controlling data assets, while defining data governance as establishing consistent policies and processes to guide data management. The document also discusses how data governance oversees and guides the overall data management function through establishing standards, policies, and decision rights. It emphasizes the importance of separating the duties of data management and data governance to avoid conflicts of interest.
Data-Ed: Data Systems Integration & Business Value PT. 1: MetadataData Blueprint
Certain systems are more data focused than others. Usually their primary focus is on accomplishing integration of disparate data. In these cases, failure is most often attributable to the adoption of a single pillar (silver bullet). The three webinars in the Data Systems Integration and Business Value series are designed to illustrate that good systems development more often depends on at least three DM disciplines (pie wedges) in order to provide a solid foundation.
Much of the discussion of metadata focuses on understanding it and the associated technologies. While these are important, they represent a typical tool/technology focus and this has not achieved significant results to date. A more relevant question when considering pockets of metadata is: Whether to include them in the scope organizational metadata practices. By understanding what it means to include items in the scope of your metadata practices, you can begin to build systems that allow you to practice sophisticated ways to advance their data management and supported business initiatives. After a bit of practice in this manner you can position your organization to better exploit any and all metadata technologies.
You can sign up for future Data-Ed webinars here: http://www.datablueprint.com/resource-center/webinar-schedule/
Data Systems Integration & Business Value Pt. 1: MetadataDATAVERSITY
Certain systems are more data focused than others. Usually their primary focus is on accomplishing integration of disparate data. In these cases, failure is most often attributable to the adoption of a single pillar (silver bullet). The three webinars in the Data Systems Integration and Business Value series are designed to illustrate that good systems development more often depends on at least three DM disciplines (pie wedges) in order to provide a solid foundation.
Much of the discussion of metadata focuses on understanding it and the associated technologies. While these are important, they represent a typical tool/technology focus and this has not achieved significant results to date. A more relevant question when considering pockets of metadata is: Whether to include them in the scope organizational metadata practices. By understanding what it means to include items in the scope of your metadata practices, you can begin to build systems that allow you to practice sophisticated ways to advance their data management and supported business initiatives. After a bit of practice in this manner you can position your organization to better exploit any and all metadata technologies.
Oracle Application User Group sponsored Collaborate 2009 Presentation 'Building a Practical Strategy for Managing Data Quality' by Alex Fiteni CPA, CMA
Good systems development often depends on multiple data management disciplines that provide a solid foundation. One of these is metadata. While much of the discussion around metadata focuses on understanding metadata itself along with its associated technologies, this perspective often represents a typical tool-and-technology focus, which has not achieved significant results to date. A more relevant question when considering pockets of metadata is whether to include them in the scope of organizational metadata practices. By understanding what it means to include items in the scope of your metadata practices, you can begin to build systems that allow you to practice sophisticated ways to advance their data management and supported business initiatives. After a bit of practice in this manner you can position your organization to better exploit any and all metadata technologies in support of business strategy.
Find more data management webinars here: http://www.datablueprint.com/resource-center/webinar-schedule/
Good systems development often depends on multiple data management disciplines that provide a solid foundation. One of these is metadata. While much of the discussion around metadata focuses on understanding metadata itself along with its associated technologies, this perspective often represents a typical tool-and-technology focus, which has not achieved significant results to date. A more relevant question when considering pockets of metadata is whether to include them in the scope of organizational metadata practices. By understanding what it means to include items in the scope of your metadata practices, you can begin to build systems that allow you to practice sophisticated ways to advance their data management and supported business initiatives. After a bit of practice in this manner you can position your organization to better exploit any and all metadata technologies in support of business strategy.
Takeaways:
Metadata value proposition: How to leverage metadata in support of your business strategy
Understanding foundational metadata concepts based on the DAMA DMBOK
Guiding principles & lessons learned
The Importance of MDM - Eternal Management of the Data MindDATAVERSITY
Despite its immaterial nature, data has a tendency to pile up as time goes on, and can quickly be rendered unusable or obsolete without careful maintenance and streamlining of processes for its management. This presentation will provide you with an understanding of reference and master data management (MDM), one such method for keeping mass amounts of business data organized and functional towards achieving business goals.
MDM’s guiding principles include the establishment and implementation of authoritative data sources and effective means of delivering data to various business processes, as well as increases to the quality of information used in organizational analytical functions (such as BI).
To that end, attendees of this webinar will learn how to:
- Structure their data management processes around these principles
- Incorporate data quality engineering into the planning of reference and MDM
- Understand why MDM is so critical to their organization’s overall data strategy
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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.
Data governance course - part 1.
Data Governance is the orchestration of people, process and technology
to enable an organization to leverage data as an enterprise asset.
The core objectives of a governance program are:
Guide information management decision-making
Ensure information is consistently defined and well understood
Increase the use and trust of data as an enterprise asset
Objectives of this presentation :
Introduction to data governance
• Why data governance discussion today : the enterprise challenges
Data-Ed: Unlock Business Value through Document & Content ManagementData Blueprint
Organizations must realize what it means to utilize document and content management in support of business strategy. The volume of unstructured data is growing at an enormous pace. While we are still far away from automated content comprehension, increasingly sophisticated technologies are extending our business and data management capabilities into more critical and regulated areas. This presentation provides you with an understanding of the dimensions of these new developments, including electronic and physical document monitoring, storage systems, content analysis and archive, retrieve and purge cycling.
Learning Objectives:
What is Document & Content Management and why is it important?
Planning and Implementing Document & Content Management
Document/Record Management Lifecycle
Levels of Control
Content management building blocks
Guiding principles & best practices
Understanding foundational document & content management concepts based on the Data Management Body of Knowledge (DMBOK)
http://www.datablueprint.com/webinar-schedule
Data-Ed Online: Unlock Business Value through Document & Content ManagementDATAVERSITY
Organizations must realize what it means to utilize document and content management in support of business strategy. The volume of unstructured data is growing at an enormous pace. While we are still far away from automated content comprehension, increasingly sophisticated technologies are extending our business and data management capabilities into more critical and regulated areas. This presentation provides you with an understanding of the dimensions of these new developments, including electronic and physical document monitoring, storage systems, content analysis and archive, retrieve and purge cycling.
Learning objectives include:
What is Document & Content Management and why is it important?
Planning and Implementing Document & Content Management
Document/Record Management Lifecycle
Levels of Control
Content management building blocks
Guiding principles & best practices
Understanding foundational document & content management concepts based on the Data Management Body of Knowledge (DMBOK)
How to utilize document & content management in support of business strategy
Ashley Ohmann--Data Governance Final 011315Ashley Ohmann
This presentation discusses enterprise data governance with Tableau. It defines data governance as processes that formally manage important data assets. The goals of data governance include establishing standards, processes, compliance, security, and metrics. Good data governance benefits an organization by improving accuracy, enabling better decisions with less waste. The presentation provides examples of how one organization improved data governance through stakeholder involvement, establishing metrics, building a data warehouse, and implementing Tableau for analytics. Key goals discussed are building trust, communicating validity, enabling access, managing metadata, provisioning rights, and maintaining compliance.
Data-Ed: Design and Manage Data Structures Data Blueprint
This document discusses different data structures and their appropriate usage. It begins with an overview of data structures and how they enable efficient data storage and organization. The webinar will cover various available data structures and when each should be used, with the goal of helping attendees apply the correct structures to fit their business needs and maximize business value. Learning objectives include understanding how different structures create different business value and applying the right structures to business requirements. The webinar will be presented on July 8, 2014 by Dave Marsh and Peter Aiken.
Data-Ed Webinar: Design & Manage Data Structures DATAVERSITY
This document discusses different data structures and their appropriate usage. It begins with an overview of data structures and how they enable efficient data storage and organization. The webinar will cover various available data structures and when each should be used, with the goal of helping attendees apply the correct structures to fit their business needs and maximize business value. Learning objectives include understanding how different structures create different business value and applying the right structures to business requirements. The webinar will be presented on July 8, 2014 by Dave Marsh and Peter Aiken.
Similar to Chapter 1: The Importance of Data Assets (20)
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.
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Over the past year, the talk of the town has shifted from $10 billion compute clusters to $100 billion clusters to trillion-dollar clusters. Every six months another zero is added to the boardroom plans. Behind the scenes, there’s a fierce scramble to secure every power contract still available for the rest of the decade, every voltage transformer that can possibly be procured. American big business is gearing up to pour trillions of dollars into a long-unseen mobilization of American industrial might. By the end of the decade, American electricity production will have grown tens of percent; from the shale fields of Pennsylvania to the solar farms of Nevada, hundreds of millions of GPUs will hum.
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Let me tell you what we see.
Predictably Improve Your B2B Tech Company's Performance by Leveraging DataKiwi Creative
Harness the power of AI-backed reports, benchmarking and data analysis to predict trends and detect anomalies in your marketing efforts.
Peter Caputa, CEO at Databox, reveals how you can discover the strategies and tools to increase your growth rate (and margins!).
From metrics to track to data habits to pick up, enhance your reporting for powerful insights to improve your B2B tech company's marketing.
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This is the webinar recording from the June 2024 HubSpot User Group (HUG) for B2B Technology USA.
Watch the video recording at https://youtu.be/5vjwGfPN9lw
Sign up for future HUG events at https://events.hubspot.com/b2b-technology-usa/
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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.
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Research: Technology breakthroughs and their capabilities.
Industry: Areas of commercial application for AI and its business impact.
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It is no wonder time-series databases are now more popular than ever before. Join me in this session to learn about the internal architecture and building blocks of QuestDB, an open source time-series database designed for speed. We will also review a history of some of the changes we have gone over the past two years to deal with late and unordered data, non-blocking writes, read-replicas, or faster batch ingestion.
ViewShift: Hassle-free Dynamic Policy Enforcement for Every Data LakeWalaa Eldin Moustafa
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#SQL #Views #Privacy #Compliance #DataLake
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1. The Importance of Data Assets
Chapter 1 from DAMA DMBOK
Ahmed Alorage
2. Content of table:
1.1 Data: an enterprise Asset 1.9 DAMA- The data management Association
1.2 Data, Information, Knowledge 1.10 Purpose of the DAMA-DMBOK Guide
1.3 The Data Lifecycle 1.11 Goals of the DAMA-DMBOK Guide
1.4 The Data Management Function 1.12 Audiences of the DAMA-DMBOK Guide
1.5 a Shared Responsibility 1.13 Using The DAMA-DMBOK Guide
1.6 a broad scope 1.16 The DAMA-DMBOK Functional Framework
1.7 an Emerging Profession 1.18 Recurring Themes
1.8 A Growing Body of Knowledge
3. 1.1 Data: an enterprise Asset
• Assets are resources with recognized value under the control of individual and organization.
• Enterprise assets help achieve the goals of the enterprise, and need to be controlled
• Usually, money and people considers the enterprise assets
• Data and information are the lifeblood of 21st century economy. Therefore, data consider vital
enterprise assets.
• Data reflect in making decision, operational effectiveness, and profitability
• Therefore, The data management function can effectively provide and control data and information
Assets.
4. 1.2 Data, Information, Knowledge
• Data is the representation of facts as text, numbers, graphics, image..
• Facts are Captured, Stored and expressed as data
• Data is meaningless without context
• Information is data in Context
• The context includes:
• The business meaning of data elements and related terms.
• The format in which the data is presented.
• The timeframe represented by the data.
• The relevance of the data to a given usage.
5. 1.2 Data, Information, Knowledge
• Data is the raw material we interpret as data consumers to continually create information
6. 1.2 Data, Information, Knowledge
• Meta-Data definitions are just some of the many different kinds of “data about data known as meta-
data (Help establish the context of data)
• Managing meta-data contributes directly to improved information quality.
• Managing information assets include the management of data and metadata.
• Knowledge is understanding awareness, cognizance and recognition of situation and familiarity with
its complexity.
• Data is the foundation of information, knowledge, and ultimately, wisdom and informed action.
• (not required to true, may could inaccurate, incomplete, out of data, misunderstood)
7. 1.3 The Data Lifecycle
• Data is created or acquired, stored and maintained, used, and eventually destroyed.
• Work with data: Extracted, exported, imported, migrated, validated, edit, updated, cleansed,
transformed, converted, integrated, segregated, aggregated, referenced, reviewed, reported,
analyzed, minded, backed up, recovered, achieved, retrieved and deleted.
8. 1.3 The Data Lifecycle
• The SDLC describes the stages of a project, while the data lifecycle describes the processes performed to
manage data assets.
9. 1.4 The Data Management Function
• Data management (DM) is the business function of planning for, controlling and delivering data and
information assets.
• This Function includes:
• The disciplines of development, execution, and supervision
• Of plans, policies, programs, projects, processes, practices and procedures.
• That control, protect, deliver, and enhance
• The value of data and information assets.
• DM have other terms and synonymous such as “ information management(IM), Data Resource
management (DRM)… etc. “
10. 1.5 a Shared Responsibility
• The scope of the data management function is scale implementation vary widely with the size, means
and experience of Organizations, therefore,
• It is a shared responsibility between the data management Professionals within information Technology
(IT) organizations and the business data stewards.
11. Data Stewardship & Stewards
• Data Stewardship (Trustees of Data assets) is the assigned accountability for business responsibilities in
data management.
• Data stewards are respected subject matter experts and business leaders appointed to represent the data
interests of their organizations
• Their roles and responsibilities:
• and take responsibility for the quality and use of data.
• carefully guard, invest, and leverage their resources.
• Ensure data resources meet business needs by ensuring the quality of data and its meta-data.
• Collaborate in partnership with data management professionals to execute data stewardship activities and
responsibilities.
12. Data management Professionals
• Operate as the expert technical custodians of data assets
• Perform technical functions to safeguard and enable effective use of enterprise data assets
• Work in data management services organizations within the information technology (IT) department.
13. Data Stewards vs Management Professionals
Data Stewards Data Management Professionals
Subject matter experts and business leaders Expert of Technical (custodians)
Represent the data interests of their organizations Perform technical functions to safeguard and enable
effective use of enterprise data assets
Ensure data resources meet business needs by
ensuring the quality of data and its meta-data
Work in data Management services organization with
IT departments
Execute data stewardship activities and
responsibilities with data management Professionals
collaboration
14. 1.5 a Shared Responsibility
• The importance of information technology infrastructure and application systems
start from Capture, stores, processes and provide data.
• Considers as “pipes” through which data flows. moreover,
• Most IT organizations have been less focused on the structure, meaning and the quality of the
data content flowing through the infrastructure and systems.
• a growing number of IT executives and business leaders today recognize the
importance of data management and the effective data Management Services
organization.
15. 1.6 a broad scope
• Data management function contain 10 major component functions:
1. Data Governance: Planning, Supervision and control data management and use.
2. Data Architecture Management: Defining blueprint (Diagram) for managing data assets
3. Data Development: analysis, design, implementation, testing, deployment, maintenance.
4. Data Operations management: Providing support from data acquisition to purging.
5. Data Security Management: Insuring Privacy, Confidentiality and appropriate access.
6. Data Quality Management: Defining, Monitoring and improving data quality.
7. Reference and Master Data Management: Managing golden versions and replicas (responsible about data related with
others and the hierarchy of data)
8. Data Warehousing and Business Intelligence Management: Enabling reporting and analysis
9. Document and Content Management: Managing data found outside of databases.
10. Meta-data Management: Integrating, Controlling and Providing meta-data.
17. 1.7 an Emerging Profession
• Data Management is a relatively new function and improving rapidly.
• Required specialized knowledge and skills.
• The Challenging Process: is how to build appropriate data management profession, Including all the methods
and techniques (standards terms and definitions, processes and practices, roles and responsibilities,
deliverables and metrics)
• ( the results the need for data management standards are required to communicate with our teammates,
managers and executives. )
18. 1.8 A Growing Body of Knowledge
• “body of knowledge” any commitment simplified and accepted in professional model.
• Provide standard terms and best practices in field of data management
• Hallmarks Publishing : the first journal who put a body of knowledge
19. 1.9 DAMA- The data management Association
• The Data Management Association (DAMA International) is the premiere
Organization for data professionals worldwide.
• Nonprofit (not-for-profit) membership organization
• Its purpose is to promote the understanding, development, and practices of
managing data and information to support business strategies.
• The goal is “ to lead the data management profession toward maturity”
through:
• Conferences Globally and Locally (US, Canada)
• Professional certification programs ( CDMP)
• Data Management Curriculum Framework (Courses in Colleges) in IT and MIS
20. 1.10 Purpose of the DAMA-DMBOK Guide
• No single book can describe the entire body of knowledge.
• DAMA-DMBOK is introduce the concepts and identifies data management:
• Goals
• Functions and activities
• Primary deliverables
• Roles
• Principles
• technology and organizational/ cultural issues
21. 1.11 Goals of the DAMA-DMBOK Guide
1. To build consensus for a generally applicable view of data management functions
2. To provide standard definitions for commonly used data management functions,
deliverables, roles, and other terminology.
3. To identify guiding principles for data management.
4. To overview commonly accepted good practices, widely adopted methods and
techniques, and significant alternative approaches, without reference to specific
technology vendors or their products.
5. To briefly identify common organizational and cultural issues.
6. To clarify the scope and boundaries of data management.
7. To guide readers to additional resources for further understands
22. 1.12 Audiences of the DAMA-DMBOK Guide
• Professionals in Data Management
• IT professionals working with data management professionals.
• Data stewards of all types
• Executives with interest in data and need to manage
• Knowledge workers developing an appreciation of data as an enterprise's
asset such as ( BI manger, Data Architect..etc. )
• Consultants for assessing and improve client data management functions.
• Educators responsible for developing and delivering a data management
curriculum ( Courses)
• Researchers in the field of data management
23. 1.13 Using The DAMA-DMBOK Guide
• The protentional uses of DAMA-DMBOK Guide :
• Informing a diverse audience about the nature and importance of data management
• Helping Standardize terms and their meanings within the data management community.
• Helping data stewards and data management professionals understand their roles and responsibilities.
• Providing the basis for assessments of data management effectiveness and maturity.
• Guiding efforts to implement and improve their data management function.
• Pointing readers to additional sources of knowledge about data Management
• Guiding the development and delivery of data Management curriculum content for higher education.
24. 1.16 The DAMA-DMBOK Functional Framework
• It is process Model (Organizing structure)for data management function, defining a standard view
of activities
• It is Version 3
• Consist of:
• An organizational environment (Environmental Elements) include Goals, principles, activities, roles,
primary deliverable, technology, skills and organizational structures.
• A standard framework for discussing each aspect of data management in organizational culture
25. • This figure identifies 10 data management functions and the scope of
each function:
26. • The basic Environmental Elements are:
• Goals and Principles: The directional business goals of each function and the fundamental principles that guide
performance of each function
• Activities: Each function is composed of lower level activities. Some activities grouped into sub-activities.
Activities decomposed into task and steps.
• Primary Deliverables (Achievements ): The information and physical database and final outputs of each
function
• Roles and responsibilities: The business and IT roles and specific and participate responsibilities in each
functions.
• Practices and Techniques: methods and procedures used commonly to perform processes and produce
deliverables. ( may include recommendations)
• Technology: Software Tools, standards and protocols, Product selection criteria
27. The basic Environmental Elements, cont.
• Organization and Culture: include
• Management metrics-measures of size, effort, time ,cost ,quality, effectiveness, productivity, success, and business value
• Critical success Factors
• Reporting Structures
• Contracting Strategies
• Budgeting and related resource allocation issues
• Teamwork and Group Dynamics
• Authority and empowerment
• Shared Values and Beliefs
• Expectations and Attitudes
• Personal Style and Preference Differences
• Cultural Rites, Rituals and Symbols
• Organizational Heritage
• Change Management Recommendations
28.
29. 1.18 Recurring Themes
• Several Concepts in DAMA-DMBOK Guide will repeated periodically such as :
• Data Stewardship: shared partnership for data management requires the ongoing participation of business data
stewards in every function.
• Data Quality: every data management function contributes in part to improving the quality of data assets.
• Data Integration: The benefits of integration techniques, minimizing redundancy, consolidating data from multiple
sources, and ensure consistency across controlled redundant data with “ golden version”
• Enterprise Perspective: manage data assets consistency across the enterprise
• Cultural change leadership: principles and practices of data management which require leadership form change
agents at all levels.
30. Summary:
• Detailed descriptions and Journey of data developments from starch as facts into knowledge or wisdom
could be gained and be useful in Contexts (1.1 & 1.2)
• Briefly defined Data management Lifecycle Processes in data with parallel and synchronize with SDLC
Stages. (1.3)
• Introduce to the Data Management Functions and identified as disciplines , plan, control, and value for
data assets in certain organizations . (1.4)
• Highlight of Data management diversity in roles and responsibilities which lead to mentioned 10 Data
management Functions (1.5 & 1.6)
31. Summary
• Demonstrate Data management required to be in Book of knowledge to perform its standards and how are
required to communicate with our teammates, managers and executives as emerging Field (1.7 & 1.8)
• Define The data management Association as nonprofit organization and its goals as data management
Leadership to maturity through conferences, Professional certifications and Curriculums. (1.9)
• Define DAMA-DMBOK Guide: purposes, Goals and Audiences, thereafter, (1.10)
• Introduce DAMA-DMBOK Functional Framework Organizing Structure consists of Organizational environment
related to The 10 Data Management Functions (1.16 & 1.18)