Chapter 1 DAMA

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.

16. Data Management Functions

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

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)