Data, Information and Knowledge Management Framework and the Data Management Book of Knowledge (DMBOK)

(1)

Structured and

Comprehensive Approach to

Data Management and the

Data Management Book of

Knowledge (DMBOK)

(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

(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

(4)

Preamble

• Every good presentation should start with quotations from

(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.

(6)

Information

• Information in all its forms –

input, processed, outputs – is a core component of any IT

system

• Applications exist to process

data supplied by users and other applications

• Data breathes life into

applications

• Data is stored and managed by

infrastructure – hardware and software

• Data is a key organisation asset

with a substantial value

• Significant responsibilities are

imposed on organisations in managing data Processes People Infrastructure Information Applications IT Systems

(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

(8)

Data, Information, Knowledge and Action

Data Action Knowledge

(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

(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

(11)

Generalised Information Management Lifecycle

• Design, define and implement

framework to manage information through this lifecycle

• Generalised lifecycle that

differs for specific information types

Enter, Create, Acquire, Derive, Update, Capture

Store, Manage, Replicate and Distribute

Protect and Recover

Archive and Recall

Delete/Remove M ana ge, _Co ntr ol a_nd Ad_m inis_ter

(12)

Expanded Generalised Information Management

Lifecycle

Enter, Create, Acquire, Derive, Update, Capture Store, Manage, Replicate and Distribute

Protect and Recover

Archive and Recall

De sig n, Im ple_m ent_{, M} ana ge, _Co ntr ol a_nd Ad_m inis_ter Implement Underlying Infrastructure Plan, Design and

Specify

• Include phases for information

management lifecycle design and implementation of

appropriate hardware and software to actualise lifecycle

(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

(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

(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

(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

(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

(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

(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

(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 Management

Data Warehousing and Business Intelligence Management

(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

(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

(23)

(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

(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

(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

(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

(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

(29)

Your Organisation Recognises and Values Information as a Strategic Asset and Manages it Accordingly

18.5% 39.5% 17.1% 21.5% 3.4% 0% 10% 20% 30% 40% 50% Strongly Agree Agree Neutral Disagree Strongly Disagree

(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

5.4% 0.0% 3.9% 31.9% 50.0% 8.8% 0% 10% 20% 30% 40% 50% 60% 70% Don’t Know Results and Effectiveness Have Significantly

Worsened

Results and Effectiveness Have Worsened Results and Effectiveness Have Remained

Essentially the Same

Results and Effectiveness Have Improved Results and Effectiveness Have Significantly

(31)

Perceived Effectiveness of the Organisation's Current

Formal or Informal Information/Data Governance Processes

2.0% 3.9% 19.1% 51.5% 21.1% 2.5% 0% 10% 20% 30% 40% 50% 60% 70% Don’t Know Very Poor (No Goals are

Met)

Poor (Few Goals are Met) OK (Some Goals are Met) Good (Most Goals are

Met)

Excellent (All Goals are Met)

(32)

Actual Information/Data Governance Effectiveness

vs. Organisation's Perception

11.8% 35.8% 32.4% 20.1% 0% 5% 10% 15% 20% 25% 30% 35% 40% 45% 50% Don’t Know It is Worse Than Most

People Think It is the Same as Most

People Think It is Better Than Most

(33)

Current Status of Organisation's Information/Data

Governance Initiatives

6.4% 8.8% 19.1% 13.2% 23.0% 20.1% 7.4% 0.5% 1.5% 0% 5% 10% 15% 20% 25% 30% Don’t Know

First Interation"in Place for More Than 2 Years First Iteration Implemented the Past 2 Years Now Planning an Implementation Evaluating Alternative Frameworks and Information

Governance Structures

Exploring, Still Seeking to Learn More None Being Considered - Keeping the Status Quo Considered a Focused Information/Data Governance

Effort but Abandoned the Idea

Started an Information/Data Governance Initiative, but Discontinued the Effort

(34)

Expected Changes in Organisation's Information/Data Governance Efforts Over the Next Two Years

2.0% 0.5% 1.0% 10.8% 39.2% 46.6% Don’t Know Will Decrease Significantly Will Decrease Somewhat Will Remain the Same Will Increase Somewhat Will Increase Significantly

(35)

Overall Objectives of Information / Data Governance

Efforts

2.6% 1.0% 5.2% 35.4% 45.3% 49.6% 55.7% 56.8% 59.4% 65.6% 80.2% 0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100 % Don't Know None Applicable Other Involve IT in Data Decisions non-IT Personnel Should

not Make by Themselves

Enable Joint Accountability for Shared Data Promote Interdependencies and Synergies Between

Departments or Business Units

Involve Non-IT Personnel in Data Decisions IT Should not Make by Itself

Provide Mechanism to Resolve Data Issues Increase the Value of Data Assets Establish Clear Decision Rules and Decisionmaking

Processes for Shared Data

(36)

Change In Organisation's Information / Data Quality

Over the Past Two Years

1.8% 0.0% 3.5% 15.8% 68.4% 10.5% 0% 10% 20% 30% 40% 50% 60% 70% 80% Don’t Know Information / Data Quality Has Significantly Worsened Information / Data Quality

Has Worsened Information / Data Quality

Has Remained Essentially the Same

Information / Data Quality Has Improved Information / Data Quality Has Significantly Improved

(37)

Maturity Of Information / Data Governance Goal

Setting And Measurement In Your Organisation

28.9% 28.9% 26.7% 11.8% 3.7% 0% 5% 10% 15% 20% 25% 30% 35% 40% 45% 50% 1 - Ad-hoc 2 - Repeatable 3 - Defined 4 - Managed 5 - Optimised

(38)

Maturity Of Information / Data Governance

Processes And Policies In Your Organisation

22.9% 46.3% 24.5% 4.8% 1.6% 1 - Ad-hoc 2 - Repeatable 3 - Defined 4 - Managed 5 - Optimised

(39)

Maturity Of Responsibility And Accountability For

Information / Data Governance Among Employees In Your Organisation 32.8% 25.4% 31.7% 3.2% 6.9% 0% 5% 10% 15% 20% 25% 30% 35% 40% 45% 50% 1 - Ad-hoc 2 - Repeatable 3 - Defined 4 - Managed 5 - Optimised

(40)

(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

(42)

DMBOK, TOGAF and COBIT

TOGAF Defines the Process for Creating a Data Architecture as Part of an

Overall Enterprise Architecture

COBIT Provides Data Governance as Part of Overall IT Governance

DMBOK Provides Detailed for Definition,

Implementation and Operation of Data

Management and Utilisation Can be a

Precursor to Implementing

Data Management

Can Provide a Maturity Model for Assessing

Data Management

DMBOK Is a Specific and Comprehensive Data Oriented Framework

(43)

DMBOK, TOGAF and COBIT – Scope and Overlap

DMBOK COBIT TOGAF Data Governance Data Architecture Management

Data Management Data Migration

Data Development

Data Operations Management

Reference and Master Data Management

Data Warehousing and Business Intelligence Management Document and Content Management

Metadata Management Data Quality Management

Data Security Management

(44)

TOGAF and Data Management

Phase H: Architecture Change Management Phase G: Implementation Governance Phase F: Migration Planning Phase E: Opportunities and Solutions Phase D: Technology Architecture Phase C: Information Systems Architecture Phase B: Business Architecture Phase A: Architecture Vision Requirements Management Phase C1: Data Architecture Phase C2: Solutions and Application Architecture • Phase C1 (subset of Phase C) relates to defining a data architecture

(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

(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 Architecture

Architecture Repository

Reference Materials External to the Enterprise

Non-Architectural Inputs

Architectural Inputs

Select Reference Models, Viewpoints, and Tools

Develop Baseline Data Architecture Description

Develop Target Data Architecture Description

Perform Gap Analysis

Define Roadmap Components

Resolve Impacts Across the Architecture Landscape

Conduct Formal Stakeholder Review

(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

(48)

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

(49)

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

(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

(51)

COBIT Structure

COBIT

Plan and Organise (PO) Acquire and Implement (AI) Deliver and Support (DS) Monitor and Evaluate (ME)

PO1 Define a strategic IT plan PO2 Define the information

architecture

AI1 Identify automated solutions DS1 Define and manage service levels

ME1 Monitor and evaluate IT performance

PO3 Determine technological direction

PO4 Define the IT processes, organisation and relationships PO5 Manage the IT investment PO6 Communicate management

aims and direction PO7 Manage IT human resources

PO8 Manage quality

PO9 Assess and manage IT risks

PO10 Manage projects

AI2 Acquire and maintain application software AI3 Acquire and maintain technology infrastructure AI4 Enable operation and use

AI5 Procure IT resources

AI6 Manage changes AI7 Install and accredit solutions

and changes

DS2 Manage third-party services DS3 Manage performance and

capacity

DS4 Ensure continuous service

DS5 Ensure systems security

DS6 Identify and allocate costs

DS7 Educate and train users DS8 Manage service desk and

incidents

DS9 Manage the configuration

DS10 Manage problems

DS11 Manage data

DS12 Manage the physical environment DS13 Manage operations

ME2 Monitor and evaluate internal control ME3 Ensure regulatory

compliance ME4 Provide IT governance

(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,

(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

(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

(55)

COBIT Data Management Goals and Metrics

•Backing up data and testing

restoration

•Managing onsite and offsite

storage of data

•Securely disposing of data

and equipment

Activity Goals

•Frequency of testing of

backup media

•Average time for data

restoration

Key Performance Indicators

•Maintain the completeness,

accuracy, validity and accessibility of stored data

•Secure data during disposal

of media

•Effectively manage storage

media

Process Goals

•% of successful data

restorations

•# of incidents where

sensitive data were retrieved after media were disposed of

•# of down time or data

integrity incidents caused by insufficient storage capacity

Process Key Goal Indicators

•Backing up data and testing

restoration

•Managing onsite and offsite

storage of data

•Securely disposing of data

and equipment

Activity Goals

•Occurrences of inability to

recover data critical to business process

•User satisfaction with

availability of data

•Incidents of noncompliance

with laws due to storage management issues

IT Key Goal Indicators

Are Measured By Are Measured By Are Measured By Drive Drive

(56)

(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

(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

(59)

Scope and Structure of Data Management Book of

Knowledge (DMBOK)

Data Management Environmental Elements Data Management Functions

(60)

DMBOK Data Management Functions

Data Management Functions

Data Governance Data Architecture Management

Data Development Data Operations Management

Data Security Management Data Quality Management

Reference and Master Data Management Data Warehousing and Business Intelligence Management

(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

(62)

DMBOK Data Management Environmental Elements

Data Management Environmental Elements

Goals and Principles Activities

Primary Deliverables Roles and Responsibilities

Practices and Techniques Technology

(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

(64)

DMBOK Data Management Functions and

Environmental Elements

Metadata Document and Content Management Data Warehousing and Business Intelligence Management Reference and Master Data Management Data Quality Management Data Security Management

Scope of Each Data Management Function

Data Operations Management Data Development Data Architecture Management Data Governance Organisation and Culture Technology Practices and Techniques Roles and Responsibilities Primary Deliverables Activities Goals and Principles

(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)

(66)

Activity Groups Within Functions

• Activity groups are

classifications of data management

activities

• Use the activity

groupings to define the scope of data management sub-projects and identify the appropriate tasks:

− Analysis and design − Implementation − Operational improvement − Management and administration Development Activities Control

Activities Operational_Activities Planning

(67)

DMBOK Function and Activity Structure

Data Governance Data Architecture

Management Data Development

Data Operations Management Data Security Management Data Quality Management Reference and Master Data Management DW and BI Management Document and Content Management Metadata Management Data Management Planning Data Management Control Understand Enterprise Information Needs Develop and Maintain

the Enterprise Data Model Analyse and Align With Other Business

Models Define and Maintain

the Database Architecture Define and Maintain the Data Integration

Architecture Define and Maintain

the DW / BI Architecture Define and Maintain Enterprise Taxonomies

and Namespaces Define and Maintain

the Metadata Architecture

Data Modeling, Analysis, and Solution

Design Detailed Data Design

Data Model and Design Quality Management Data Implementation Database Support Data Technology Management Understand Data Security Needs and

Regulatory Requirements Define Data Security

Policy Define Data Security

Standards Define Data Security

Controls and Procedures Manage Users, Passwords, and Group

Membership Manage Data Access Views and Permissions

Monitor User Authentication and

Access Behaviour Classify Information

Confidentiality Audit Data Security

Develop and Promote Data Quality

Awareness Define Data Quality

Requirement Profile, Analyse, 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

Understand Reference and Master Data Integration Needs Identify Master and

Reference Data Sources and Contributors Define and Maintain the Data Integration

Architecture Implement Reference

and Master Data Management

Solutions Define and Maintain

Match Rules Establish “Golden”

Records Define and Maintain

Hierarchies and Affiliations

Design and Implement Operational DQM

Procedures Monitor Operational DQM Procedures and

Performance

Plan and Implement Integration of New

Data Sources Replicate and Distribute Reference

and Master Data

Understand Business Intelligence Information Needs Define and Maintain

the DW / BI Architecture Implement Data Warehouses and Data

Marts Implement BI Tools and User Interfaces Process Data for Business Intelligence

Monitor and Tune Data Warehousing

Processes Monitor and Tune BI

Activity and Performance Documents / Records Management Content Management Understand Metadata Requirements Define the Metadata

Architecture Develop and Maintain

Metadata Standards Implement a Managed

Metadata Environment Create and Maintain

Metadata Integrate Metadata

Manage Metadata Repositories Distribute and Deliver

Metadata Query, Report, and

Analyse Metadata Manage Changes to

Reference and Master Data

(68)

DMBOK Function and Activity - Planning Activities

Data Governance Data Architecture _Management Data Development Data Operations _Management _ManagementData Security _ManagementData Quality

Reference and Master Data Management DW and BI Management Document and Content Management Metadata Management Data Management Planning Data Management Control Understand Enterprise Information Needs Develop and Maintain the Enterprise Data Model Analyse and Align With Other Business

the DW / BI Architecture Define and Maintain

Enterprise Taxonomies and

Namespaces Define and Maintain

Data Modeling, Analysis, and Solution Design Detailed Data Design

Controls and Procedures Manage Users, Passwords, and Group Membership Manage Data Access

Views and Permissions Monitor User Authentication and Access Behaviour Classify Information Confidentiality Audit Data Security

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

Understand Reference and

Master Data Integration Needs Identify Master and

Hierarchies and Affiliations Design and Implement Operational DQM Procedures Monitor Operational

and Master Data

the DW / BI Architecture Implement Data Warehouses and Data Marts Implement BI Tools and User Interfaces Process Data for Business Intelligence

Activity and Performance Documents / Records Management Content Management Understand Metadata Requirements Define the Metadata

Architecture Develop and Maintain Metadata Standards Implement a Managed Metadata Environment Create and Maintain

Metadata Integrate Metadata Manage Metadata Repositories Distribute and Deliver Metadata Query, Report, and

Reference and Master Data

(69)

DMBOK Function and Activity - Control Activities

Quality Defects

Performance

and Master Data

(70)

DMBOK Function and Activity - Development

Activities

_Data

Management

Quality Defects

Procedures

and Master Data

(71)

DMBOK Function and Activity - Operational

Activities

_Data

Management

Quality Defects

Performance

and Master Data

(72)

DMBOK Environmental Elements Structure

Data Management Environmental Elements Goals and Principles Activities Primary Deliverables Roles and Responsibilities Technology Practices and Techniques Organisation and Culture

Vision and Mission

Business Benefits Strategic Goals Specific Objectives Guiding Principles Phases. Tasks, Steps Dependencies Sequence and Flow

Use Cases and Scenarios Trigger Events Inputs and Outputs Information Documents Databases Other Resources Individual Roles Organisation Roles Business and IT Roles Qualifications and Skills Tool Categories Standards and Protocols Selection Criteria Learning Curves Recognised Best Practices Common Approaches Alternative Techniques Critical Success Factors Reporting Structures Management Metrics Values, Beliefs, Expectations Attitudes. Styles, Preferences Teamwork, Group Dynamics, Authority, Empowerment. Contracting Strategies Change

(73)

(74)

(75)

Data Governance

• Core function of the Data Management Framework

• Interacts with and influences each of the surrounding ten data

management functions

• Data governance is the exercise of authority and control (planning,

monitoring, and enforcement) over the management of data assets

• Data governance function guides how all other data management

functions are performed

• High-level, executive data stewardship

• Data governance is not the same thing as IT governance

• Data governance is focused exclusively on the management of data

(76)

Data Governance – Definition and Goals

• Definition

− The exercise of authority and control (planning, monitoring, and

enforcement) over the management of data assets

• Goals

− To define, approve, and communicate data strategies, policies,

standards, architecture, procedures, and metrics

− To track and enforce regulatory compliance and conformance to

data policies, standards, architecture, and procedures

− To sponsor, track, and oversee the delivery of data management

projects and services

− To manage and resolve data related issues

(77)

Data Governance - Overview

•Business Goals •Business Strategies •IT Objectives •IT Strategies •Data Needs •Data Issues •Regulatory Requirements Inputs •Business Executives •IT Executives •Data Stewards •Regulatory Bodies Suppliers •Intranet Website •E-Mail •Metadata Tools •Metadata Repository •Issue Management Tools •Data Governance KPI •Dashboard

Tools

•Executive Data Stewards •Coordinating Data Stewards •Business Data Stewards •Data Professionals •DM Executive •CIO Participants •Data Policies •Data Standards •Resolved Issues

•Data Management Projects and

Services

•Quality Data and Information •Recognised Data Value

Primary Deliverables

•Data Producers •Knowledge Workers •Managers and Executives •Data Professionals •Customers

Consumers

•Data Value

•Data Management Cost •Achievement of Objectives •# of Decisions Made

•Steward Representation / Coverage •Data Professional Headcount

•Data Management Process Maturity

Metrics

(78)

Data Governance Function, Activities and

Sub-Activities

Data Governance

Data Management Planning Data Management Control

Understand Strategic Enterprise Data Needs

Develop and Maintain the Data Strategy Establish Data Professional Roles and

Organisations

Identify and Appoint Data Stewards Establish Data Governance and Stewardship

Organisations

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

Supervise Data Professional Organisations 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

(79)

Data Governance

• Data governance is accomplished most effectively as an

on-going program and a continual improvement process

• Every data governance programme is unique, taking into

account distinctive organisational and cultural issues, and the immediate data management challenges and

opportunities

(80)

Data Governance - Possible Organisation Structure

Data Governance Structure

Organisation Data Governance Council

Business Unit Data Governance Councils

Data Stewardship Committees

Data Stewardship Teams

CIO

Data Technologists Data Management Executive

(81)

Data Governance Shared Decision Making

Enterprise Information Model Business Operating Model Information Needs IT Leadership Information Specifications Capital Investments Quality Requirements Research and Development Funding Issue Resolution Data Governance Model

Business Decisions Shared Decision Making IT Decisions

Database Architecture Enterprise Information Management Strategy Data Integration Architecture Enterprise Information Management Policies Data Warehousing and Business Intelligence Architecture Enterprise Information Management Standards Metadata Architecture Enterprise Information Management Metrics Technical Metadata Enterprise Information Management Services