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 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.
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.
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
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.
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.
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.
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 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 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.
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.
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.
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.
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 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.
1. Enterprise Data Management (EDM) is the ability of an organization to precisely define, easily integrate and effectively retrieve data for both internal applications and external communication. It involves managing various types of data across the enterprise.
2. EDM includes areas like master data management, reference data management, metadata management, data governance, data quality, data analytics, data privacy, data integration, and data architecture.
3. The document discusses definitions and concepts for each of these areas, including roles, processes, and technologies involved. It provides examples and diagrams to illustrate key points about enterprise data management.
The document outlines a STEAM-based curriculum to create the next generation of tech leaders. It includes 6 levels from foundation to expert that focus on skills like computer science, coding, game development, web development, and more. Sessions are 2.5 hours per week for 10-12 months per level. Students work in groups of 4 max on projects like mobile apps, websites, and games using technologies like mBlock, Unity, and Flutter. Feedback and assessments are provided to measure learning outcomes and skills. Certificates and membership benefits are available upon completion.
The document discusses data quality management (DQM) concepts and activities. It describes the DQM approach as a continuous cycle of planning, deployment, monitoring, and acting. Key activities include developing data quality awareness, defining requirements, profiling/assessing data, defining metrics/rules, testing requirements, setting service levels, continuously measuring/monitoring quality, and managing issues. DQM aims to ensure data meets fitness-for-use expectations and business needs.
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.
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 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 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.
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.
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.
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.
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 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.
1. Enterprise Data Management (EDM) is the ability of an organization to precisely define, easily integrate and effectively retrieve data for both internal applications and external communication. It involves managing various types of data across the enterprise.
2. EDM includes areas like master data management, reference data management, metadata management, data governance, data quality, data analytics, data privacy, data integration, and data architecture.
3. The document discusses definitions and concepts for each of these areas, including roles, processes, and technologies involved. It provides examples and diagrams to illustrate key points about enterprise data management.
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The document outlines a STEAM-based curriculum to create the next generation of tech leaders. It includes 6 levels from foundation to expert that focus on skills like computer science, coding, game development, web development, and more. Sessions are 2.5 hours per week for 10-12 months per level. Students work in groups of 4 max on projects like mobile apps, websites, and games using technologies like mBlock, Unity, and Flutter. Feedback and assessments are provided to measure learning outcomes and skills. Certificates and membership benefits are available upon completion.
The document discusses data quality management (DQM) concepts and activities. It describes the DQM approach as a continuous cycle of planning, deployment, monitoring, and acting. Key activities include developing data quality awareness, defining requirements, profiling/assessing data, defining metrics/rules, testing requirements, setting service levels, continuously measuring/monitoring quality, and managing issues. DQM aims to ensure data meets fitness-for-use expectations and business needs.
The document discusses concepts and activities 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's Corporate Information Factory model describes the major components as the raw data applications, operational data store, data warehouse, operational data marts, and data marts. Kimball's approach focuses on dimensional modeling and his "data warehouse chess pieces" which include the business process, data, data warehouse, and access layers. The document then covers typical data warehousing and business intelligence activities.
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 concepts and activities related to data security including understanding business and regulatory requirements, defining security policies, standards, controls and procedures, managing users, passwords and access permissions. The goal is to protect information through proper authentication, authorization, access and auditing in alignment with organizational needs and regulations.
The document discusses data architecture management. It provides an overview of data architecture, including its position within enterprise architecture and a diagram showing its key components. It describes the concepts and activities involved in data architecture management, such as developing and maintaining an enterprise data model. The Zachman Framework for enterprise architecture is also introduced as a tool for data architects.
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TIME DIVISION MULTIPLEXING TECHNIQUE FOR COMMUNICATION SYSTEMHODECEDSIET
Time Division Multiplexing (TDM) is a method of transmitting multiple signals over a single communication channel by dividing the signal into many segments, each having a very short duration of time. These time slots are then allocated to different data streams, allowing multiple signals to share the same transmission medium efficiently. TDM is widely used in telecommunications and data communication systems.
### How TDM Works
1. **Time Slots Allocation**: The core principle of TDM is to assign distinct time slots to each signal. During each time slot, the respective signal is transmitted, and then the process repeats cyclically. For example, if there are four signals to be transmitted, the TDM cycle will divide time into four slots, each assigned to one signal.
2. **Synchronization**: Synchronization is crucial in TDM systems to ensure that the signals are correctly aligned with their respective time slots. Both the transmitter and receiver must be synchronized to avoid any overlap or loss of data. This synchronization is typically maintained by a clock signal that ensures time slots are accurately aligned.
3. **Frame Structure**: TDM data is organized into frames, where each frame consists of a set of time slots. Each frame is repeated at regular intervals, ensuring continuous transmission of data streams. The frame structure helps in managing the data streams and maintaining the synchronization between the transmitter and receiver.
4. **Multiplexer and Demultiplexer**: At the transmitting end, a multiplexer combines multiple input signals into a single composite signal by assigning each signal to a specific time slot. At the receiving end, a demultiplexer separates the composite signal back into individual signals based on their respective time slots.
### Types of TDM
1. **Synchronous TDM**: In synchronous TDM, time slots are pre-assigned to each signal, regardless of whether the signal has data to transmit or not. This can lead to inefficiencies if some time slots remain empty due to the absence of data.
2. **Asynchronous TDM (or Statistical TDM)**: Asynchronous TDM addresses the inefficiencies of synchronous TDM by allocating time slots dynamically based on the presence of data. Time slots are assigned only when there is data to transmit, which optimizes the use of the communication channel.
### Applications of TDM
- **Telecommunications**: TDM is extensively used in telecommunication systems, such as in T1 and E1 lines, where multiple telephone calls are transmitted over a single line by assigning each call to a specific time slot.
- **Digital Audio and Video Broadcasting**: TDM is used in broadcasting systems to transmit multiple audio or video streams over a single channel, ensuring efficient use of bandwidth.
- **Computer Networks**: TDM is used in network protocols and systems to manage the transmission of data from multiple sources over a single network medium.
### Advantages of TDM
- **Efficient Use of Bandwidth**: TDM all
Introduction- e - waste – definition - sources of e-waste– hazardous substances in e-waste - effects of e-waste on environment and human health- need for e-waste management– e-waste handling rules - waste minimization techniques for managing e-waste – recycling of e-waste - disposal treatment methods of e- waste – mechanism of extraction of precious metal from leaching solution-global Scenario of E-waste – E-waste in India- case studies.
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