2. Objectives
• To provide an overview of a structured approach to
developing and implementing a detailed data management
policy including frameworks, standards, project, team and
maturity
March 8, 2010 2
3. Agenda
• Introduction to Data Management
• State of Information and Data Governance
• Other Data Management Frameworks
• Data Management and Data Management Book of
Knowledge (DMBOK)
• Conducting a Data Management Project
• Creating a Data Management Team
• Assessing Your Data Management Maturity
March 8, 2010 3
4. Preamble
• Every good presentation should start with quotations from
The Prince and Dilbert
March 8, 2010 4
5. Management Wisdom
• There is nothing more difficult to take in hand, more perilous to conduct or more
uncertain in its success than to take the lead in the introduction of a new order of
things.
− The Prince
• Never be in the same room as a decision. I'll illustrate my point with a puppet
show that I call "Journey to Blameville" starring "Suggestion Sam" and "Manager
Meg.“
• You will often be asked to comment on things you don't understand. These
handouts contain nonsense phrases that can be used in any situation so, let's
dominate our industry with quality implementation of methodologies.
• Our executives have started their annual strategic planning sessions. This involves
sitting in a room with inadequate data until an illusion of knowledge is attained.
Then we'll reorganise, because that's all we know how to do.
− Dilbert
March 8, 2010 5
6. Information
• Information in all its forms –
input, processed, outputs – is a
Applications core component of any IT
system
• Applications exist to process
data supplied by users and
other applications
Processes Information
• Data breathes life into
applications
IT Systems
• Data is stored and managed by
infrastructure – hardware and
software
• Data is a key organisation asset
with a substantial value
People Infrastructure • Significant responsibilities are
imposed on organisations in
managing data
March 8, 2010 6
7. Data, Information and Knowledge
• Data is the representation of facts as text, numbers, graphics,
images, sound or video
• Data is the raw material used to create information
• Facts are captured, stored, and expressed as data
• Information is data in context
• Without context, data is meaningless - we create meaningful
information by interpreting the context around data
• Knowledge is information in perspective, integrated into a viewpoint
based on the recognition and interpretation of patterns, such as
trends, formed with other information and experience
• Knowledge is about understanding the significance of information
• Knowledge enables effective action
March 8, 2010 7
9. Information is an Organisation Asset
• Tangible organisation assets are seen as having a value and
are managed and controlled using inventory and asset
management systems and procedures
• Data, because it is less tangible, is less widely perceived as
a real asset, assigned a real value and managed as if it had
a value
• High quality, accurate and available information is a pre-
requisite to effective operation of any organisation
March 8, 2010 9
10. Data Management and Project Success
• Data is fundamental to the effective and efficient
operation of any solution
− Right data
− Right time
− Right tools and facilities
• Without data the solution has no purpose
• Data is too often overlooked in projects
• Project managers frequently do not appreciate the
complexity of data issues
March 8, 2010 10
11. Generalised Information Management Lifecycle
Enter, Create, Acquire, • Generalised lifecycle that
Derive, Update, Capture
differs for specific
information types
Store, Manage, M
an
Replicate and Distribute ag
e,
Co
nt
ro
la
nd
Ad
Protect and Recover mi
n is
t er
• Design, define and implement
framework to manage Archive and Recall
information through this
lifecycle
Delete/Remove
March 8, 2010 11
12. Expanded Generalised Information Management
Lifecycle
Plan, Design and
Specify
De
Implement sig
Underlying n,
Im
Infrastructure ple
m en
Enter, Create, t, M
Acquire, Derive, an
ag
Update, Capture e,
Co
nt
Store, Manage, ro
la
Replicate and nd
Distribute Ad
mi
ni ste
r
• Include phases for information Protect and Recover
management lifecycle design
and implementation of Archive and Recall
appropriate hardware and
software to actualise lifecycle
Delete/Remove
March 8, 2010 12
13. Data and Information Management
• Data and information management is a business process
consisting of the planning and execution of policies,
practices, and projects that acquire, control, protect,
deliver, and enhance the value of data and information
assets
March 8, 2010 13
14. Data and Information Management
To manage and utilise information as a strategic asset
To implement processes, policies, infrastructure and solutions to
govern, protect, maintain and use information
To make relevant and correct information available in all business
processes and IT systems for the right people in the right context at
the right time with the appropriate security and with the right
quality
To exploit information in business decisions, processes and
relations
March 8, 2010 14
15. Data Management Goals
• Primary goals
− To understand the information needs of the enterprise and all its
stakeholders
− To capture, store, protect, and ensure the integrity of data assets
− To continually improve the quality of data and information,
including accuracy, integrity, integration, relevance and
usefulness of data
− To ensure privacy and confidentiality, and to prevent
unauthorised inappropriate use of data and information
− To maximise the effective use and value of data and information
assets
March 8, 2010 15
16. Data Management Goals
• Secondary goals
− To control the cost of data management
− To promote a wider and deeper understanding of the value of
data assets
− To manage information consistently across the enterprise
− To align data management efforts and technology with business
needs
March 8, 2010 16
17. Triggers for Data Management Initiative
• When an enterprise is about to undertake architectural
transformation, data management issues need to be
understood and addressed
• Structured and comprehensive approach to data
management enables the effective use of data to take
advantage of its competitive advantages
March 8, 2010 17
18. Data Management Principles
• Data and information are valuable enterprise assets
• Manage data and information carefully, like any other
asset, by ensuring adequate quality, security, integrity,
protection, availability, understanding and effective use
• Share responsibility for data management between
business data owners and IT data management
professionals
• Data management is a business function and a set of
related disciplines
March 8, 2010 18
19. Organisation Data Management Function
• Business function of planning for, controlling and
delivering data and information assets
• 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
• Scope of the data management function and the scale of
its implementation vary widely with the size, means, and
experience of organisations
• Role of data management remains the same across
organisations even though implementation differs widely
March 8, 2010 19
20. Scope of Complete Data Management Function
Data Management
Data Governance Data Architecture Management
Data Development Data Operations Management
Data Security Management Data Quality Management
Reference and Master Data Data Warehousing and Business
Management Intelligence Management
Document and Content Management Metadata Management
March 8, 2010 20
21. Shared Role Between Business and IT
• Data management is a shared responsibility between data
management professionals within IT and the business data
owners representing the interests of data producers and
information consumers
• Business data ownership is the concerned with
accountability for business responsibilities in data
management
• Business data owners are data subject matter experts
• Represent the data interests of the business and take
responsibility for the quality and use of data
March 8, 2010 21
22. Why Develop and Implement a Data Management
Framework?
• Improve organisation data management efficiency
• Deliver better service to business
• Improve cost-effectiveness of data management
• Match the requirements of the business to the management of the
data
• Embed handling of compliance and regulatory rules into data
management framework
• Achieve consistency in data management across systems and
applications
• Enable growth and change more easily
• Reduce data management and administration effort and cost
• Assist in the selection and implementation of appropriate data
management solutions
• Implement a technology-independent data architecture
March 8, 2010 22
24. Data Management Issues
• Discovery - cannot find the right information
• Integration - cannot manipulate and combine information
• Insight - cannot extract value and knowledge from
information
• Dissemination - cannot consume information
• Management – cannot manage and control information
volumes and growth
March 8, 2010 24
25. Data Management Problems – User View
• Managing Storage Equipment
• Application Recoveries / Backup Retention
• Vendor Management
• Power Management
• Regulatory Compliance
• Lack of Integrated Tools
• Dealing with Performance Problems
• Data Mobility
• Archiving and Archive Management
• Storage Provisioning
• Managing Complexity
• Managing Costs
• Backup Administration and Management
• Proper Capacity Forecasting and Storage Reporting
• Managing Storage Growth
March 8, 2010 25
26. Information Management Challenges
• Explosive Data Growth
− Value and volume of data is overwhelming
− More data is see as critical
− Annual rate of 50+% percent
• Compliance Requirements
− Compliance with stringent regulatory requirements and audit
procedures
• Fragmented Storage Environment
− Lack of enterprise-wide hardware and software data storage
strategy and discipline
• Budgets
− Frozen or being cut
March 8, 2010 26
27. Data Quality
• Poor data quality costs real money
• Process efficiency is negatively impacted by poor data
quality
• Full potential benefits of new systems not be realised
because of poor data quality
• Decision making is negatively affected by poor data quality
March 8, 2010 27
28. State of Information and Data Governance
• Information and Data Governance Report, April 2008
− International Association for Information and Data Quality (IAIDQ)
− University of Arkansas at Little Rock, Information Quality Program
(UALR-IQ)
March 8, 2010 28
29. Your Organisation Recognises and Values Information as a
Strategic Asset and Manages it Accordingly
Strongly Disagree 3.4%
Disagree 21.5%
Neutral 17.1%
Agree 39.5%
Strongly Agree 18.5%
0% 10% 20% 30% 40% 50%
March 8, 2010 29
30. Direction of Change in the Results and Effectiveness of the
Organisation's Formal or Informal Information/Data
Governance Processes Over the Past Two Years
Results and Effectiveness Have Significantly
8.8%
Improved
Results and Effectiveness Have Improved 50.0%
Results and Effectiveness Have Remained
31.9%
Essentially the Same
Results and Effectiveness Have Worsened 3.9%
Results and Effectiveness Have Significantly
0.0%
Worsened
Don’t Know 5.4%
0% 10% 20% 30% 40% 50% 60% 70%
March 8, 2010 30
31. Perceived Effectiveness of the Organisation's Current
Formal or Informal Information/Data Governance Processes
Excellent (All Goals are
2.5%
Met)
Good (Most Goals are
21.1%
Met)
OK (Some Goals are Met) 51.5%
Poor (Few Goals are Met) 19.1%
Very Poor (No Goals are
3.9%
Met)
Don’t Know 2.0%
0% 10% 20% 30% 40% 50% 60% 70%
March 8, 2010 31
32. Actual Information/Data Governance Effectiveness
vs. Organisation's Perception
It is Better Than Most
20.1%
People Think
It is the Same as Most
32.4%
People Think
It is Worse Than Most
35.8%
People Think
Don’t Know 11.8%
0% 5% 10% 15% 20% 25% 30% 35% 40% 45% 50%
March 8, 2010 32
33. Current Status of Organisation's Information/Data
Governance Initiatives
Started an Information/Data Governance Initiative, but
1.5%
Discontinued the Effort
Considered a Focused Information/Data Governance
0.5%
Effort but Abandoned the Idea
None Being Considered - Keeping the Status Quo 7.4%
Exploring, Still Seeking to Learn More 20.1%
Evaluating Alternative Frameworks and Information
23.0%
Governance Structures
Now Planning an Implementation 13.2%
First Iteration Implemented the Past 2 Years 19.1%
First Interation"in Place for More Than 2 Years 8.8%
Don’t Know 6.4%
0% 5% 10% 15% 20% 25% 30%
March 8, 2010 33
34. Expected Changes in Organisation's Information/Data
Governance Efforts Over the Next Two Years
Will Increase Significantly 46.6%
Will Increase Somewhat 39.2%
Will Remain the Same 10.8%
Will Decrease Somewhat 1.0%
Will Decrease Significantly 0.5%
Don’t Know 2.0%
0% 10% 20% 30% 40% 50% 60%
March 8, 2010 34
35. Overall Objectives of Information / Data Governance
Efforts
Improve Data Quality 80.2%
Establish Clear Decision Rules and Decisionmaking
65.6%
Processes for Shared Data
Increase the Value of Data Assets 59.4%
Provide Mechanism to Resolve Data Issues 56.8%
Involve Non-IT Personnel in Data Decisions IT Should
55.7%
not Make by Itself
Promote Interdependencies and Synergies Between
49.6%
Departments or Business Units
Enable Joint Accountability for Shared Data 45.3%
Involve IT in Data Decisions non-IT Personnel Should
35.4%
not Make by Themselves
Other 5.2%
None Applicable 1.0%
Don't Know 2.6%
0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100
%
March 8, 2010 35
36. Change In Organisation's Information / Data Quality
Over the Past Two Years
Information / Data Quality
10.5%
Has Significantly Improved
Information / Data Quality
68.4%
Has Improved
Information / Data Quality
Has Remained Essentially 15.8%
the Same
Information / Data Quality
3.5%
Has Worsened
Information / Data Quality
0.0%
Has Significantly Worsened
Don’t Know 1.8%
0% 10% 20% 30% 40% 50% 60% 70% 80%
March 8, 2010 36
37. Maturity Of Information / Data Governance Goal
Setting And Measurement In Your Organisation
5 - Optimised 3.7%
4 - Managed 11.8%
3 - Defined 26.7%
2 - Repeatable 28.9%
1 - Ad-hoc 28.9%
0% 5% 10% 15% 20% 25% 30% 35% 40% 45% 50%
March 8, 2010 37
38. Maturity Of Information / Data Governance
Processes And Policies In Your Organisation
5 - Optimised 1.6%
4 - Managed 4.8%
3 - Defined 24.5%
2 - Repeatable 46.3%
1 - Ad-hoc 22.9%
0% 5% 10% 15% 20% 25% 30% 35% 40% 45% 50%
March 8, 2010 38
39. Maturity Of Responsibility And Accountability For
Information / Data Governance Among Employees In Your
Organisation
5 - Optimised 6.9%
4 - Managed 3.2%
3 - Defined 31.7%
2 - Repeatable 25.4%
1 - Ad-hoc 32.8%
0% 5% 10% 15% 20% 25% 30% 35% 40% 45% 50%
March 8, 2010 39
41. Other Data Management-Related Frameworks
• TOGAF (and other enterprise architecture standards) define a
process for arriving an at enterprise architecture definition, including
data
• TOGAF has a phase relating to data architecture
• TOGAF deals with high level
• DMBOK translates high level into specific details
• COBIT is concerned with IT governance and controls:
− IT must implement internal controls around how it operates
− The systems IT delivers to the business and the underlying business processes
these systems actualise must be controlled – these are controls external to IT
− To govern IT effectively, COBIT defines the activities and risks within IT that
need to be managed
• COBIT has a process relating to data management
• Neither TOGAF nor COBIT are concerned with detailed data
management design and implementation
March 8, 2010 41
42. DMBOK, TOGAF and COBIT
Can be a DMBOK Is a Specific and
Precursor to Comprehensive Data
Implementing Oriented Framework
Data
Management DMBOK Provides Detailed
for Definition,
Implementation and
TOGAF Defines the Process Operation of Data
for Creating a Data Management and Utilisation
Architecture as Part of an
Overall Enterprise
Architecture
Can Provide a Maturity
Model for Assessing
Data Management
COBIT Provides Data
Governance as Part of
Overall IT Governance
March 8, 2010 42
43. DMBOK, TOGAF and COBIT – Scope and Overlap
DMBOK
Data Development
Data Operations Management
Reference and Master Data Management
Data Warehousing and Business Intelligence Management
TOGAF Document and Content Management
Metadata Management
Data Quality Management
Data Architecture Management
Data Management
Data Migration
Data
Governance
Data Security COBIT
Management
March 8, 2010 43
44. TOGAF and Data Management
• Phase C1 (subset of
Phase C) relates to
Phase A:
Architecture defining a data
Vision
Phase H:
Phase B:
architecture
Architecture
Business
Change
Architecture
Management
Phase C1:
Data
Architecture
Phase G: Phase C:
Requirements Information
Implementation
Management Systems
Governance Architecture
Phase C2:
Solutions and
Application
Phase F: Phase D: Architecture
Migration Technology
Planning Architecture
Phase E:
Opportunities
and Solutions
March 8, 2010 44
45. TOGAF Phase C1: Information Systems Architectures
- Data Architecture - Objectives
• Purpose is to define the major types and sources of data
necessary to support the business, in a way that is:
− Understandable by stakeholders
− Complete and consistent
− Stable
• Define the data entities relevant to the enterprise
• Not concerned with design of logical or physical storage
systems or databases
March 8, 2010 45
46. TOGAF Phase C1: Information Systems Architectures
- Data Architecture - Overview
Phase C1: Information Systems
Architectures - Data Architecture
Approach Elements Inputs Steps Outputs
Key Considerations for Data Reference Materials External to the Select Reference Models,
Architecture Enterprise Viewpoints, and Tools
Develop Baseline Data Architecture
Architecture Repository Non-Architectural Inputs
Description
Develop Target Data Architecture
Architectural Inputs
Description
Perform Gap Analysis
Define Roadmap Components
Resolve Impacts Across the
Architecture Landscape
Conduct Formal Stakeholder
Review
Finalise the Data Architecture
Create Architecture Definition
Document
March 8, 2010 46
47. TOGAF Phase C1: Information Systems Architectures - Data
Architecture - Approach - Key Considerations for Data
Architecture
• Data Management
− Important to understand and address data management issues
− Structured and comprehensive approach to data management enables the
effective use of data to capitalise on its competitive advantages
− Clear definition of which application components in the landscape will serve as
the system of record or reference for enterprise master data
− Will there be an enterprise-wide standard that all application components,
including software packages, need to adopt
− Understand how data entities are utilised by business functions, processes, and
services
− Understand how and where enterprise data entities are created, stored,
transported, and reported
− Level and complexity of data transformations required to support the
information exchange needs between applications
− Requirement for software in supporting data integration with external
organisations
March 8, 2010 47
48. TOGAF Phase C1: Information Systems Architectures - Data
Architecture - Approach - Key Considerations for Data
Architecture
• Data Migration
− Identify data migration requirements and also provide indicators
as to the level of transformation for new/changed applications
− Ensure target application has quality data when it is populated
− Ensure enterprise-wide common data definition is established to
support the transformation
March 8, 2010 48
49. TOGAF Phase C1: Information Systems Architectures - Data
Architecture - Approach - Key Considerations for Data
Architecture
• Data Governance
− Ensures that the organisation has the necessary dimensions in
place to enable the data transformation
− Structure – ensures the organisation has the necessary structure
and the standards bodies to manage data entity aspects of the
transformation
− Management System - ensures the organisation has the
necessary management system and data-related programs to
manage the governance aspects of data entities throughout its
lifecycle
− People - addresses what data-related skills and roles the
organisation requires for the transformation
March 8, 2010 49
50. TOGAF Phase C1: Information Systems Architectures
- Data Architecture - Outputs
• Refined and updated versions of the Architecture Vision phase deliverables
− Statement of Architecture Work
− Validated data principles, business goals, and business drivers
• Draft Architecture Definition Document
− Baseline Data Architecture
− Target Data Architecture
• Business data model
• Logical data model
• Data management process models
• Data Entity/Business Function matrix
• Views corresponding to the selected viewpoints addressing key stakeholder concerns
− Draft Architecture Requirements Specification
• Gap analysis results
• Data interoperability requirements
• Relevant technical requirements
• Constraints on the Technology Architecture about to be designed
• Updated business requirements
• Updated application requirements
− Data Architecture components of an Architecture Roadmap
March 8, 2010 50
51. COBIT Structure
COBIT
Plan and Organise (PO) Acquire and Implement (AI) Deliver and Support (DS) Monitor and Evaluate (ME)
DS1 Define and manage service ME1 Monitor and evaluate IT
PO1 Define a strategic IT plan AI1 Identify automated solutions
levels performance
PO2 Define the information AI2 Acquire and maintain ME2 Monitor and evaluate
DS2 Manage third-party services
architecture application software internal control
PO3 Determine technological AI3 Acquire and maintain DS3 Manage performance and ME3 Ensure regulatory
direction technology infrastructure capacity compliance
PO4 Define the IT processes,
AI4 Enable operation and use DS4 Ensure continuous service ME4 Provide IT governance
organisation and relationships
PO5 Manage the IT investment AI5 Procure IT resources DS5 Ensure systems security
PO6 Communicate management
AI6 Manage changes DS6 Identify and allocate costs
aims and direction
AI7 Install and accredit solutions
PO7 Manage IT human resources DS7 Educate and train users
and changes
DS8 Manage service desk and
PO8 Manage quality
incidents
PO9 Assess and manage IT risks DS9 Manage the configuration
PO10 Manage projects DS10 Manage problems
DS11 Manage data
DS12 Manage the physical
environment
DS13 Manage operations
March 8, 2010 51
52. COBIT and Data Management
• COBIT objective DS11 Manage Data within the Deliver and
Support (DS) domain
• Effective data management requires identification of data
requirements
• Data management process includes establishing effective
procedures to manage the media library, backup and
recovery of data and proper disposal of media
• Effective data management helps ensure the quality,
timeliness and availability of business data
March 8, 2010 52
53. COBIT and Data Management
• Objective is the control over the IT process of managing data that
meets the business requirement for IT of optimising the use of
information and ensuring information is available as required
• Focuses on maintaining the completeness, accuracy, availability and
protection of data
• Involves taking actions
− Backing up data and testing restoration
− Managing onsite and offsite storage of data
− Securely disposing of data and equipment
• Measured by
− User satisfaction with availability of data
− Percent of successful data restorations
− Number of incidents where sensitive data were retrieved after media were
disposed of
March 8, 2010 53
54. COBIT Process DS11 Manage Data
• DS11.1 Business Requirements for Data Management
− Establish arrangements to ensure that source documents expected from the business are received, all data received from the
business are processed, all output required by the business is prepared and delivered, and restart and reprocessing needs are
supported
• DS11.2 Storage and Retention Arrangements
− Define and implement procedures for data storage and archival, so data remain accessible and usable
− Procedures should consider retrieval requirements, cost-effectiveness, continued integrity and security requirements
− Establish storage and retention arrangements to satisfy legal, regulatory and business requirements for documents, data, archives,
programmes, reports and messages (incoming and outgoing) as well as the data (keys, certificates) used for their encryption and
authentication
• DS11.3 Media Library Management System
− Define and implement procedures to maintain an inventory of onsite media and ensure their usability and integrity
− Procedures should provide for timely review and follow-up on any discrepancies noted
• DS11.4 Disposal
− Define and implement procedures to prevent access to sensitive data and software from equipment or media when they are
disposed of or transferred to another use
− Procedures should ensure that data marked as deleted or to be disposed cannot be retrieved.
• DS11.5 Backup and Restoration
− Define and implement procedures for backup and restoration of systems, data and documentation in line with business
requirements and the continuity plan
− Verify compliance with the backup procedures, and verify the ability to and time required for successful and complete restoration
− Test backup media and the restoration process
• DS11.6 Security Requirements for Data Management
− Establish arrangements to identify and apply security requirements applicable to the receipt, processing, physical storage and
output of data and sensitive messages
− Includes physical records, data transmissions and any data stored offsite
March 8, 2010 54
55. COBIT Data Management Goals and Metrics
Activity Goals Process Goals Activity Goals
•Backing up data and testing •Maintain the completeness, •Backing up data and testing
restoration accuracy, validity and restoration
•Managing onsite and offsite accessibility of stored data •Managing onsite and offsite
storage of data •Secure data during disposal storage of data
•Securely disposing of data of media •Securely disposing of data
and equipment •Effectively manage storage and equipment
media
Are Measured Are Measured Are Measured
By Drive By Drive By
Key Performance Process Key Goal IT Key Goal Indicators
Indicators Indicators
•% of successful data •Occurrences of inability to
restorations recover data critical to
•Frequency of testing of •# of incidents where business process
backup media sensitive data were retrieved •User satisfaction with
•Average time for data after media were disposed of availability of data
restoration •# of down time or data •Incidents of noncompliance
integrity incidents caused by with laws due to storage
insufficient storage capacity management issues
March 8, 2010 55
57. Data Management Book of Knowledge (DMBOK)
• DMBOK is a generalised and comprehensive framework for
managing data across the entire lifecycle
• Developed by DAMA (Data Management Association)
• DMBOK provides a detailed framework to assist
development and implementation of data management
processes and procedures and ensures all requirements
are addressed
• Enables effective and appropriate data management
across the organisation
• Provides awareness and visibility of data management
issues and requirements
March 8, 2010 57
58. Data Management Book of Knowledge (DMBOK)
• Not a solution to your data management needs
• Framework and methodology for developing and
implementing an appropriate solution
• Generalised framework to be customised to meet specific
needs
• Provide a work breakdown structure for a data
management project to allow the effort to be assessed
• No magic bullet
March 8, 2010 58
59. Scope and Structure of Data Management Book of
Knowledge (DMBOK)
Data Management
Environmental Elements
Data
Management
Functions
March 8, 2010 59
60. DMBOK Data Management Functions
Data Management
Functions
Data Governance Data Architecture Management
Data Development Data Operations Management
Data Security Management Data Quality Management
Data Warehousing and Business
Reference and Master Data Management
Intelligence Management
Document and Content Management Metadata Management
March 8, 2010 60
61. DMBOK Data Management Functions
• Data Governance - planning, supervision and control over data management and
use
• Data Architecture Management - defining the blueprint for managing data assets
• Data Development - analysis, design, implementation, testing, deployment,
maintenance
• Data Operations Management - providing support from data acquisition to
purging
• Data Security Management - Ensuring privacy, confidentiality and appropriate
access
• Data Quality Management - defining, monitoring and improving data quality
• Reference and Master Data Management - managing master versions and
replicas
• Data Warehousing and Business Intelligence Management - enabling reporting
and analysis
• Document and Content Management - managing data found outside of databases
• Metadata Management - integrating, controlling and providing metadata
March 8, 2010 61
62. DMBOK Data Management Environmental Elements
Data Management
Environmental Elements
Goals and Principles Activities
Primary Deliverables Roles and Responsibilities
Practices and Techniques Technology
Organisation and Culture
March 8, 2010 62
63. DMBOK Data Management Environmental Elements
• Goals and Principles - 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, sub-activities, tasks and
steps
• Primary Deliverables - information and physical databases and documents created as
interim and final outputs of each function. Some deliverables are essential, some are
generally recommended, and others are optional depending on circumstances
• Roles and Responsibilities - business and IT roles involved in performing and supervising
the function, and the specific responsibilities of each role in that function. Many roles will
participate in multiple functions
• Practices and Techniques - common and popular methods and procedures used to perform
the processes and produce the deliverables and may also include common conventions,
best practice recommendations, and alternative approaches without elaboration
• Technology - categories of supporting technology such as software tools, standards and
protocols, product selection criteria and learning curves
• Organisation and Culture – this can include issues such as management metrics, critical
success factors, reporting structures, budgeting, resource allocation issues, expectations
and attitudes, style, cultural, approach to change management
March 8, 2010 63
64. DMBOK Data Management Functions and
Environmental Elements
Goals and Activities Primary Roles and Practices and Technology Organisation
Principles Deliverables Responsibilities Techniques and Culture
Data
Governance
Data
Architecture
Management
Data
Development
Data
Operations
Management
Scope of Each Data Management Function
Data Security
Management
Data Quality
Management
Reference and
Master Data
Management
Data
Warehousing
and Business
Intelligence
Management
Document and
Content
Management
Metadata
Management
March 8, 2010 64
65. Scope of Data Management Book of Knowledge
(DMBOK) Data Management Framework
• Hierarchy
− Function
• Activity
− Sub-Activity (not in all cases)
• Each activity is classified as one (or more) of:
− Planning Activities (P)
• Activities that set the strategic and tactical course for other data management
activities
• May be performed on a recurring basis
− Development Activities (D)
• Activities undertaken within implementation projects and recognised as part of the
systems development lifecycle (SDLC), creating data deliverables through analysis,
design, building, testing, preparation, and deployment
− Control Activities (C)
• Supervisory activities performed on an on-going basis
− Operational Activities (O)
• Service and support activities performed on an on- going basis
March 8, 2010 65
66. Activity Groups Within Functions
• Activity groups are
classifications of data
management
Planning Development
activities
Activities Activities • Use the activity
groupings to define
the scope of data
management sub-
projects and identify
the appropriate tasks:
Control Operational
Activities − Analysis and design
Activities
− Implementation
− Operational
improvement
− Management and
administration
March 8, 2010 66
67. DMBOK Function and Activity Structure
Data
Management
Reference and Document and
Data Architecture Data Operations Data Security Data Quality DW and BI Metadata
Data Governance Data Development Master Data Content
Management Management Management Management Management Management
Management Management
Understand Data
Data Modeling, Develop and Promote Understand Reference Understand Business
Data Management Understand Enterprise Security Needs and Documents / Records Understand Metadata
Analysis, and Solution Database Support Data Quality and Master Data Intelligence
Planning Information Needs Regulatory Management Requirements
Design Awareness Integration Needs Information Needs
Requirements
Identify Master and
Develop and Maintain Define and Maintain
Data Management Data Technology Define Data Security Define Data Quality Reference Data Define the Metadata
the Enterprise Data Detailed Data Design the DW / BI Content Management
Control Management Policy Requirement Sources and Architecture
Model Architecture
Contributors
Analyse and Align Data Model and Define and Maintain Implement Data
Define Data Security Profile, Analyse, and Develop and Maintain
With Other Business Design Quality the Data Integration Warehouses and Data
Standards Assess Data Quality Metadata Standards
Models Management Architecture Marts
Implement Reference
Define and Maintain Define Data Security Implement a Managed
Define Data Quality and Master Data Implement BI Tools
the Database Data Implementation Controls and Metadata
Metrics Management and User Interfaces
Architecture Procedures Environment
Solutions
Define and Maintain Manage Users,
Define Data Quality Define and Maintain Process Data for Create and Maintain
the Data Integration Passwords, and Group
Business Rules Match Rules Business Intelligence Metadata
Architecture Membership
Define and Maintain Monitor and Tune
Manage Data Access Test and Validate Data Establish “Golden”
the DW / BI Data Warehousing Integrate Metadata
Views and Permissions Quality Requirements Records
Architecture Processes
Define and Maintain Monitor User Define and Maintain Monitor and Tune BI
Set and Evaluate Data Manage Metadata
Enterprise Taxonomies Authentication and Hierarchies and Activity and
Quality Service Levels Repositories
and Namespaces Access Behaviour Affiliations Performance
Define and Maintain Continuously Measure Plan and Implement
Classify Information Distribute and Deliver
the Metadata and Monitor Data Integration of New
Confidentiality Metadata
Architecture Quality Data Sources
Replicate and
Manage Data Quality Query, Report, and
Audit Data Security Distribute Reference
Issues Analyse Metadata
and Master Data
Clean and Correct Data Manage Changes to
Quality Defects Reference and Master
Data
Design and Implement
Operational DQM
Procedures
Monitor Operational
DQM Procedures and
Performance
March 8, 2010 67
68. DMBOK Function and Activity - Planning Activities
Data
Management
Reference and Document and
Data Architecture Data Operations Data Security Data Quality DW and BI Metadata
Data Governance Data Development Master Data Content
Management Management Management Management Management Management
Management Management
Understand Data Understand
Understand Data Modeling, Develop and Promote Understand Business Understand
Data Management Security Needs and Reference and Documents / Records
Enterprise Analysis, and Database Support Data Quality Intelligence Metadata
Planning Regulatory Master Data Management
Information Needs Solution Design Awareness Information Needs Requirements
Requirements Integration Needs
Develop and Identify Master and
Define and Maintain
Data Management Maintain the Data Technology Define Data Security Define Data Quality Reference Data Content Define the Metadata
Detailed Data Design the DW / BI
Control Enterprise Data Management Policy Requirement Sources and Management Architecture
Architecture
Model Contributors
Analyse and Align Data Model and Define and Maintain Implement Data Develop and
Define Data Security Profile, Analyse, and
With Other Business Design Quality the Data Integration Warehouses and Maintain Metadata
Standards Assess Data Quality
Models Management Architecture Data Marts Standards
Implement Reference
Define and Maintain Define Data Security Implement a
Define Data Quality and Master Data Implement BI Tools
the Database Data Implementation Controls and Managed Metadata
Metrics Management and User Interfaces
Architecture Procedures Environment
Solutions
Define and Maintain Manage Users,
Define Data Quality Define and Maintain Process Data for Create and Maintain
the Data Integration Passwords, and
Business Rules Match Rules Business Intelligence Metadata
Architecture Group Membership
Define and Maintain Manage Data Access Test and Validate Monitor and Tune
Establish “Golden”
the DW / BI Views and Data Quality Data Warehousing Integrate Metadata
Records
Architecture Permissions Requirements Processes
Define and Maintain
Monitor User Set and Evaluate Define and Maintain Monitor and Tune BI
Enterprise Manage Metadata
Authentication and Data Quality Service Hierarchies and Activity and
Taxonomies and Repositories
Access Behaviour Levels Affiliations Performance
Namespaces
Define and Maintain Continuously Plan and Implement
Classify Information Distribute and
the Metadata Measure and Monitor Integration of New
Confidentiality Deliver Metadata
Architecture Data Quality Data Sources
Replicate and
Manage Data Quality Query, Report, and
Audit Data Security Distribute Reference
Issues Analyse Metadata
and Master Data
Clean and Correct Manage Changes to
Data Quality Defects Reference and
Master Data
Design and
Implement
Operational DQM
Procedures
Monitor Operational
DQM Procedures and
Performance
March 8, 2010 68
69. DMBOK Function and Activity - Control Activities
Data
Management
Reference and Document and
Data Architecture Data Operations Data Security Data Quality DW and BI Metadata
Data Governance Data Development Master Data Content
Management Management Management Management Management Management
Management Management
Understand Data
Data Modeling, Develop and Promote Understand Reference Understand Business
Data Management Understand Enterprise Security Needs and Documents / Records Understand Metadata
Analysis, and Solution Database Support Data Quality and Master Data Intelligence
Planning Information Needs Regulatory Management Requirements
Design Awareness Integration Needs Information Needs
Requirements
Identify Master and
Develop and Maintain Define and Maintain
Data Management Data Technology Define Data Security Define Data Quality Reference Data Define the Metadata
the Enterprise Data Detailed Data Design the DW / BI Content Management
Control Management Policy Requirement Sources and Architecture
Model Architecture
Contributors
Analyse and Align Data Model and Define and Maintain Implement Data
Define Data Security Profile, Analyse, and Develop and Maintain
With Other Business Design Quality the Data Integration Warehouses and Data
Standards Assess Data Quality Metadata Standards
Models Management Architecture Marts
Implement Reference
Define and Maintain Define Data Security Implement a Managed
Define Data Quality and Master Data Implement BI Tools
the Database Data Implementation Controls and Metadata
Metrics Management and User Interfaces
Architecture Procedures Environment
Solutions
Define and Maintain Manage Users,
Define Data Quality Define and Maintain Process Data for Create and Maintain
the Data Integration Passwords, and Group
Business Rules Match Rules Business Intelligence Metadata
Architecture Membership
Define and Maintain Monitor and Tune
Manage Data Access Test and Validate Data Establish “Golden”
the DW / BI Data Warehousing Integrate Metadata
Views and Permissions Quality Requirements Records
Architecture Processes
Define and Maintain Monitor User Define and Maintain Monitor and Tune BI
Set and Evaluate Data Manage Metadata
Enterprise Taxonomies Authentication and Hierarchies and Activity and
Quality Service Levels Repositories
and Namespaces Access Behaviour Affiliations Performance
Define and Maintain Continuously Measure Plan and Implement
Classify Information Distribute and Deliver
the Metadata and Monitor Data Integration of New
Confidentiality Metadata
Architecture Quality Data Sources
Replicate and
Manage Data Quality Query, Report, and
Audit Data Security Distribute Reference
Issues Analyse Metadata
and Master Data
Clean and Correct Data Manage Changes to
Quality Defects Reference and Master
Data
Design and Implement
Operational DQM
Procedures
Monitor Operational
DQM Procedures and
Performance
March 8, 2010 69