Chapter 2: Data Management Overviews

Data Management Overviews
Ahmed Alorage

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.

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.

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.

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.

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.

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.

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.

• 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.

• 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.

• 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

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

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

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

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

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

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

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”

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

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.

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.

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.

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

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.

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

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

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.

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

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

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

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

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

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.

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

Chapter 2: Data Management Overviews

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