Implementing Effective Data Governance

I M P L E M E N T I N G E F F E C T I V E D A T A G O V E R N A N C E – C H R I S T O P H E R B R A D L E Y © 2 0 1 3 | PAGE 1
IMPLEMENTING EFFECTIVE DATA
GOVERNANCE
IMPLEMENTING
EFFECTIVE
DATA GOVERNANCE
Seminar
October 2013
Christopher Bradley

INTRODUCTION: WHO AM I?
My blog: Information Management, Life & Petrol
http://infomanagementlifeandpetrol.blogspot.com
@InfoRacer
uk.linkedin.com/in/christophermichaelbradley/
CHRISTOPHER BRADLEY
Information Strategist
chris@chrismb.co.uk

RECENT PRESENTATIONS
DAMA UK Webinar: June 2015; “Data Modelling” Disciplines of the DAMA DMBoK”
PRISME Pharmaceutical Congress: May 2015, Basel, CH; “Building & exploiting a Pharmaceutical
Industry consensus data model”
MDM DG Europe (IRM): May 2015, London; “CDMP Examination Preparation” & “Data Governance
By Stealth?, Can you ‘sell’ Data Governance if the stakeholders don’t get it?”
DAMA UK Webinar: April 2015; “Master & Reference Data Management” Disciplines of the DMBoK”
Enterprise Data World: April 2015, Washington DC USA; “Data Modelling For The Business” and
“Evaluating Information Management Tools”
DAMA UK Webinar: February 2015; “An Introduction to the Information Disciplines of the DMBoK”
Dataversity Webinar: February 2015; “How to successfully introduce Master & Reference data
management”
Petroleum Information Management Summit 2015: February 2015, Berlin DE,
“How to succeed with MDM and Data Governance”
Enterprise Data & Business Intelligence 2014: (IRM), November 2014, London, UK “Data Modelling
101 Workshop”
Enterprise Data World: (DataVersity), May 2014, Austin, Texas, “MDM Architectures & How to identify
the right Subject Area & tooling for your MDM strategy”
E&P Information Management Dubai: (DMBoard),17-19 March 2014, Dubai, UAE “Master Data
Management Fundamentals, Architectures & Identify the starting Data Subject Areas”
DAMA Australia: (DAMA-A),18-21 November 2013, Melbourne, Australia “DAMA DMBoK 2.0”,
“Information Management Fundamentals” 1 day workshop”
Data Management & Information Quality Europe:
(IRM Conferences), 4-6 November 2013, London, UK
“Data Modelling Fundamentals” ½ day workshop:
“Myths, Fairy Tales & The Single View” Seminar
“Imaginative Innovation - A Look to the Future” DAMA Panel Discussion
IPL / Embarcadero series: June 2013, London, UK, “Implementing Effective Data Governance”
Riyadh Information Exchange: May 2013, Riyadh, Saudi Arabia,
“Big Data – What’s the big fuss?”
Enterprise Data World: (Wilshire Conferences), May 2013, San Diego, USA, “Data and Process
Blueprinting – A practical approach for rapidly optimising Information Assets”
Data Governance & MDM Europe: (IRM Conferences), April 2013, London, “Selecting the Optimum
Business approach for MDM success…. Case study with Statoil”
E&P Information Management: (SMI Conference), February 2013, London,
“Case Study, Using Data Virtualisation for Real Time BI & Analytics”
E&P Data Governance: (DMBoard / DG Events), January 2013, Marrakech, Morocco, “Establishing a
successful Data Governance program”
Big Data 2: (Whitehall), December 2012, London, “The Pillars of successful knowledge
management”
Financial Information Management Association (FIMA): (WBR), November 2012, London; “Data
Strategy as a Business Enabler”
Data Modeling Zone: (Technics), November 2012, Baltimore USA
“Data Modelling for the business”
Data Management & Information Quality Europe: (IRM), November 2012, London; “All you need to
know to prepare for DAMA CDMP professional certification”
ECIM Exploration & Production: September 2012, Haugesund, Norway:
“Enhancing communication through the use of industry standard models; case study in E&P
using WITSML”
Preparing the Business for MDM success: Threadneedles Executive breakfast briefing series,
July 2012, London
Big Data – What’s the big fuss?: (Whitehall), Big Data & Analytics, June 2012, London,
Enterprise Data World International: (DAMA / Wilshire), May 2012, Atlanta GA,
“A Model Driven Data Governance Framework For MDM - Statoil Case Study”
“When Two Worlds Collide – Data and Process Architecture Synergies” (rated best workshop in
conference); “Petrochemical Information Management utilising PPDM in an Enterprise
Information Architecture”
Data Governance & MDM Europe: (DAMA / IRM), April 2012, London,
“A Model Driven Data Governance Framework For MDM - Statoil Case Study”
AAPG Exploration & Production Data Management: April 2012, Dead Sea Jordan; “A Process
For Introducing Data Governance into Large Enterprises”
PWC & Iron Mountain Corporate Information Management: March 2012, Madrid; “Information
Management & Regulatory Compliance”
DAMA Scandinavia: March 2012, Stockholm,
“Reducing Complexity in Information Management” (rated best presentation in conference)
Ovum IT Governance & Planning: March 2012, London;
“Data Governance – An Essential Part of IT Governance”
American Express Global Technology Conference: November 2011, UK,
“All An Enterprise Architect Needs To Know About Information Management”
FIMA Europe (Financial Information Management):, November 2011, London; “Confronting
The Complexities Of Financial Regulation With A Customer Centric Approach; Applying a
Master Data Management And Data Governance Process In Clydesdale Bank “
Data Management & Information Quality Europe: (DAMA / IRM), November 2011, London,
“Assessing & Improving Information Management Effectiveness – Cambridge University Press
Case Study”; “Too Good To Be True? – The Truth About Open Source BI”
ECIM Exploration & Production: September 12th 14th 2011, Haugesund, Norway: “The Role Of
Data Virtualisation In Your EIM Strategy”
Enterprise Data World International: (DAMA / Wilshire), April 2011, Chicago IL; “How Do You
Want Yours Served? – The Role Of Data Virtualisation And Open Source BI”
Data Governance & MDM Europe: (DAMA / IRM), March 2011, London,
“Clinical Information Data Governance”
Data Management & Information Management Europe: (DAMA / IRM), November 2010,
London,
“How Do You Get A Business Person To Read A Data Model?
DAMA Scandinavia: October 26th-27th 2010, Stockholm,
“Incorporating ERP Systems Into Your Overall Models & Information Architecture” (rated best
presentation in conference)
BPM Europe: (IRM), September 27th – 29th 2010, London,
“Learning to Love BPMN 2.0”
IPL / Composite Information Management in Pharmaceuticals: September 15th 2010, London,
“Clinical Information Management – Are We The Cobblers Children?”
ECIM Exploration & Production: September 13th 15th 2010, Haugesund, Norway: “Information
Challenges and Solutions” (rated best presentation in conference)
Enterprise Architecture Europe: (IRM), June 16th – 18th 2010, London: ½ day workshop; “The
Evolution of Enterprise Data Modelling”

RECENT PUBLICATIONS
Book: “Data Modelling For The Business – A Handbook for aligning the business with IT using high-level data models”; Technics
Publishing;
ISBN 978-0-9771400-7-7; http://www.amazon.com/Data-Modeling-Business-Handbook-High-Level
White Paper: “Information is at the heart of ALL Architecture disciplines”,; March 2014
Article: The Bookbinder, the Librarian & a Data Governance story ; July 2013
Article: Data Governance is about Hearts and Minds, not Technology January 2013
White Paper: “The fundamentals of Information Management”, January 2013
White Paper: “Knowledge Management – From justification to delivery”, December 2012
Article: “Chief INFORMATION Officer? Not really” Article, November 2012
White Paper: “Running a successful Knowledge Management Practice” November 2012
White Paper: “Big Data Projects are not one man shows” June 2012
Article: “IPL & Statoil’s innovative approach to Master Data Management in Statoil”, Oil IT Journal, May 2012
White Paper: “Data Modelling is NOT just for DBMS’s” April 2012
Article: “Data Governance in the Financial Services Sector” FSTech Magazine, April 2012
Article: “Data Governance, an essential component of IT Governance" March 2012
Article: “Leveraging a Model Driven approach to Master Data Management in Statoil”, Oil IT Journal, February 2012
Article: “How Data Virtualization Helps Data Integration Strategies” BeyeNETWORK (December 2011)
Article: “Approaches & Selection Criteria For organizations approaching data integration programmes” TechTarget
(November 2011)
Article: Big Data – Same Problems? BeyeNETWORK and TechTarget. (July 2011)
Article “10 easy steps to evaluate Data Modelling tools” Information Management, (March 2010)
Article “How Do You Want Your Data Served?” Conspectus Magazine (February 2010)
Article “How do you want yours served (data that is)” (BeyeNETWORK January 2010)
Article “Seven deadly sins of data modelling” (BeyeNETWORK October 2009)
Article “Data Modelling is NOT just for DBMS’s” Part 1 BeyeNETWORK July 2009 and Part 2 BeyeNETWORK August 2009
Web Channel: BeyeNETWORK “Chris Bradley Expert Channel” Information Asset Management
http://www.b-eye-network.co.uk/channels/1554/
Article: “Preventing a Data Disaster” February 2009, Database Marketing Magazine

WHAT IS DATA GOVERNANCE?

CONTENTS
›Introduction to Data Governance
›Drivers for Data Governance & Benefits
›A Data Governance Framework
»Organization & Structures
»Roles & responsibilities
»Policies & Processes
»Programme & Implementation
»Reporting & Assurance
›Summary
›Case Studies

DATA GOVERNANCE ACTIVITIES
›Data Governance (DMBoK)

8
DQ & MDM
Workflow
Modelling
(Data &
Process)

9
“Organisations that do not
understand the overwhelming
importance of managing
information as tangible assets in
the new economy will not survive.”
Tom Peters
Data and information are the
lifeblood of the 21st century
economy. In the Information Age,
data is recognized as a vital
enterprise asset.
The Data Management Association
(DAMA International) is the Premiere
organization for data professionals
worldwide. DAMA International is an
international not-for-profit
membership organization, with over
10,000 members in 40 chapters
around the globe. Its purpose is to
promote the understanding,
development, and practice of
managing data and information to
support business strategies.
Data
Architecture
Management
Database
Operations
Management
Reference &
Master Data
Management
DW & BI
Management
Document
& Content
Management
Meta-data
Management
Data
Quality
Management
Data
Governance
Data
Modelling &
Data
Development
Data Security
& Risk
Management

INTRODUCTION
›Data Governance Terms & Definitions

11
WHAT IS INFORMATION MANAGEMENT?
“The management of information”
• No prizes here
“A set of principles to derive maximum
value from an organisation’s information”
• It’s about deriving real value from information,
not just storing data for data’s sake
“A set of principles to derive maximum value from an organisation’s information,
whilst protecting it as a key corporate asset”
• If the information is valuable it needs to be treated as such
“The execution of a set of principles and processes to derive maximum value from an
organisation’s information, whilst protecting it as a key corporate asset”
• There’s no point in the theory, if it’s not put into practice!!!

12
KEY INFORMATION MANAGEMENT DIMENSIONS
Data Governance
Data Architecture
& Design
Data Integration
Business
Intelligence
Master Data
Management
Data Quality
Management
The key to ensuring
information is
exploited
to its full potential
The key to managing
and maintaining the
“critical entities”
of an organisation
The key to enterprise-
wide
quality assurance of data
The key to
combining
information from
disparate systems
The key to developing
effective information
systems
The key to exercising
positive control over the
management of
information

13
WHAT IS DATA GOVERNANCE?
Where did
this figure
come from?
Data model?
What data
model?
Don't believe
everything
you read
Multiple
personality
disorder
Spreadsheets,
spreadsheets
everywhere
Where's that
darned
report?
Data
Governance
Data
Architecture
and Design
Data Quality
Management
Master Data
Management
Data
Warehousing
and ETL
Business
Intelligence
Includes standards/policies covering …
Design and operation of a management system to assure
that data delivers value and is not a cost
Who can do what to the organisation’s data and how.
Ensuring standards are set and met
A strategic & high level view across the organisation
To ensure …
Key principles/processes of effective Information
Management are put into practice
Continual improvement through the evolution of an
Information Management strategy
Data Governance is NOT …
Tactical management
Technology and IT department alone
The exercise of authority and control (planning, monitoring, and
enforcement) over the management of data assets. (DAMA International)

14
DATA GOVERNANCE
DAMA –DMBOK Functional Framework v3 (Source: DAMA)
Data Quality
Management
DWH and BI
Management
Reference & Master
Data Management
Data Architecture &
Modelling
Management
Data
Governance
Key Data Management Functions for Governance
At the heart of Information Management

15
DATA GOVERNANCE
• DRIVERS FOR &
BENEFITS OF
DATA GOVERNANCE

16
WHY IS EFFECTIVE IM SO CRUCIAL TODAY?
Higher volumes of data generated by organisations
• Information is all pervasive – if you don’t have a strategy to manage
it, you will certainly drown in it
Proliferation of data-centric systems
• ERP, CRM, ECM…
Greater demand for reliable information
• Accurate business intelligence is vital to gain competitive advantage,
support planning/resourcing and monitor key business functions
Tighter regulatory compliance
• Far more responsibility now placed on organisations to ensure they
store, manage, audit and protect their data
Business change is no longer optional – it’s inevitable
• Mergers/acquisitions, market forces, technological advances…
• Data Governance is essential for managing Information in “The
Cloud”

17
3 DRIVERS FOR DATA GOVERNANCE
1. Reactive Governance
2. Pre-emptive Governance
3. Proactive Governance

18
REACTIVE GOVERNANCE
• Tactical exercise
• Efforts designed to respond to current pains
• Organization has suffered a regulatory breach
or a data disaster

19
PRE-EMPTIVE GOVERNANCE
• Organization is facing a major change or threats.
• Designed to ward off significant issues that
could affect success of the company
• Probably driven by impending regulatory &
compliance needs

20
BUT BEWARE ….
If your main motivation for
Data Governance is
Regulation & Compliance, the
best you can ever hope to
achieve is just to be
compliant
Chris Bradley

21
PROACTIVE DATA GOVERNANCE
• Efforts designed to improve capabilities to
resolve risk and data issues.
• Build on reactive governance to create an ever-
increasing body of validated rules, standards,
and tested processes.
• Part of a wider Information Management
strategy

22
BENEFITS OF DATA GOVERNANCE
Assurance and evidence that data is managed effectively reduces
regulatory compliance risk and improves confidence in operational and
management decisions
Known individuals, their responsibilities and escalation route reduces the
time and effort to resolve data issues
Increased capability to respond to change and events faster through joint
understanding across users and IT
Reduced system design and integration effort
Reduced risk of departmental silos and duplication leading to
reconciliation effort and argument

23
Now – That should clear up a few things around here!
“Ultimately, poor data quality is like dirt on
the windshield. You may be able to drive
for a long time with slowly degrading
vision, but at some point you either have
to stop and clear the windshield or
risk everything.”
Ken Orr, The Cutter Consortium
Businesses NEED a common vocabulary
for communication

24
dave.huxford@ipl.com
Data Governance Framework
• A Data Governance
Framework

25
DG CONTEXT IN INFORMATION ARCHITECTURE
FRAMEWORK
Master Data MI/BI Data
Transaction
Data
Structured
Technical
Data
Unstructured
Data
Models / Taxonomy Catalog / Meta data
Distribution &
Infrastructure
Services
Quality
Lifecycle
Management
Governance
Information
Planning
Goals
Principles
1
2 3
4 5 6
7 8
9 10 11 12 13
0
1
2
3
4
5
IM Principles
Data
Governance
IM Planning
Data Quality
IM Lifecycle
Management
Integration &
Access
Models &
Taxonomy
Catalog &
Metadata
Master Data
Management
Business
Intelligence
To-Be
As-Is
13 components containing ...
• Principles & rationale
• Maturity model
• Detailed methodology
• Tools & templates
• Example business cases

26
A DATA
GOVERNANCE
FRAMEWORK
IPL DG
Framework
Council &
Organisation
Council Terms
of Reference
Working Groups
Alignment
Liaison
Roles &
Responsibilities
Owners
Stewards
Custodians
Data
Governance
Office
Data
Management
Policies &
Processes
Principles
Policies
Standards
Processes
Programme
Maturity Matrix
Strategy
Scope
Business Case
Implementation
Reporting &
Assurance
Perform
Measur
Contin
Improve
Evide
Repos
Commun

27
Data Governance
Framework
• Council & Organisation

28
A DATA
GOVERNANCE
FRAMEWORK
IPL DG
Framework
Council &
Organisation
Council Terms
of Reference
Working Groups
Alignment
Liaison
Roles &
Responsibilities
Owners
Stewards
Custodians
Data
Governance
Office
Data
Management
Policies &
Processes
Principles
Policies
Standards
Processes
Programme
Maturity Matrix
Strategy
Scope
Business Case
Implementation
Reporting &
Assurance
Perform
Measur
Contin
Improve
Evide
Repos
Commun

29
DG ORGANISATION
Roles
Teams
Management
Governance
Direction Board
DG Council
(Owners)
Data Quality
Working
Groups
Stewards
Quality
Analysts
Master &
Reference Data
Domain
Working Group
Stewards
Custodians
Data
Warehousing &
BI
BICC
Business
Analysts
Providers
Change
Programme
Enterprise
Architecture
Data
Architecture
Repository /
ETL
Architects
Models &
Metadata
Enterprise /
Application
Modellers
Analysts
Other functions
such as security,
lifecycle,
compliance & risk
management also
need to be covered
as applied to same
enterprise data

30
TYPICAL GOVERNANCE STRUCTURE
Data Working
Group
Lead Data
Steward
Data Working
Group
Lead Data
Steward
Data Working
Group
Lead Data
Steward
Data Working
Group
Lead Data
Steward
Data Governance Council
Lead Data Stewards Key Business Unit Heads
Chief Information Officer (CIO)
Initiatives
Guidance
Issues
Measures
Data Mgt Exec
Data
Steward
Data
Custodian
Data
Steward
Data
Custodian
Data
Steward
Data
Custodian
Data
Steward
Data
Custodian
Working Groups
aligned to Subject
Area

31
Board
Security Management
Committee
Compliance
Committee
Data Governance Council
Data Quality
Management
Master & Reference
Data Management
Data Warehouse &
BI Management
Data Security &
Privacy
Data Architecture
Management
Value or Risk
Initiatives & Projects
Change Programme
Committee
Chief Information Officer
Head of Data
Management
Head of Marketing Head of Compliance
Head of Finance
Head of Operations
Enterprise Data Architect
Data Quality Manager
IT Security Manager
Lead Data Steward (s)

32
INFORMATION GOVERNANCE
Ongoing data maintenance
and quality
Compliance with policy
and procedures
Three tiered governance with individual
accountability: By SUBJECT AREA
Information
Owners:
Information
Stewards:
Information Director:
Maintain high-level corporate data model
Define the overall process and framework
Allocate accountability for individual data entities
Determine business process to manage data
Mandate stewardship and quality activity
Primacy over entire data entity, including data
quality metrics

33
DATA GOVERNANCE
FRAMEWORK
• ROLES &
RESPONSIBILITIES

34
A DATA
GOVERNANCE
FRAMEWORK
IPL DG
Framework
Council &
Organisation
Council Terms
of Reference
Working Groups
Alignment
Liaison
Roles &
Responsibilities
Owners
Stewards
Custodians
Data
Governance
Office
Data
Management
Policies &
Processes
Principles
Policies
Standards
Processes
Programme
Maturity Matrix
Strategy
Scope
Business Case
Implementation
Reporting &
Assurance
Perform
Measur
Contin
Improve
Evide
Repos
Commun

35
ROLES
CIO
Lead Data Steward
Data Steward
Data Management Exec
Data Custodian
STEWARDSHIP (LEGISLATIVE & JUDICIAL) DATA MANAGEMENT SERVICES (EXECUTIVE)

36
INFORMATION
Quality
Reporting
Location
Modelling
Analysis
TECHNOLOGY
Architecture
Processing
Integration
Access
Development
Operations
BUSINESS
Risk
Finance
Actuarial
Underwriting
Marcoms
HR
Data Owners &
Data Stewards
Data
Management
Data Custodians
GOVERNANCE

37
DATA GOVERNANCE
FRAMEWORK
• POLICIES,
PRINCIPLES,
PROCESSES

38
A DATA
GOVERNANCE
FRAMEWORK
IPL DG
Framework
Council &
Organisation
Council Terms
of Reference
Working Groups
Alignment
Liaison
Roles &
Responsibilities
Owners
Stewards
Custodians
Data
Governance
Office
Data
Management
Policies &
Processes
Principles
Policies
Standards
Processes
Programme
Maturity Matrix
Strategy
Scope
Business Case
Implementation
Reporting &
Assurance
Perform
Measur
Contin
Improve
Evide
Repos
Commun

39
POLICIES
A set of measurable rules for a set of data elements, in the context of an
organizational scope, for the benefit of a business process, irrespective of
where the data is stored and the party that provides the data
1. Data Model
2. Data Definitions
3. Data Quality
4. Data Security
5. Data Lifecycle Management
6. Reference Data
7. Master Data

40
TAXONOMY OF PRINCIPLES
A principle is a rule or belief that governs behaviour and consists of:
– Statement
• A description of the principle to be adopted
– Rationale
• The reason(s) for adopting the principle
– Implications:
• The conclusions drawn from the principle
– Key actions
• The key actions required by BICC and other functions to ensure the principles are
adopted within Riyad Bank
– References
• Supporting artefacts/tools that support or relate to the principle (initially many of
these will not exist and will form a key part of the next steps)

41
The Enterprise, rather than any individual or business unit, owns all data.
Every data source must have a defined custodian (a business role) responsible for the accuracy,
integrity, and security of those data.
Wherever possible, data must be simple to enter and must accurately reflect the situation; they must
also be in a useful, usable form for both input and output.
Data should be collected only if they have known and documented uses and value.
Data must be readily available to those with a legitimate business need.
Processes for data capture, validation, and processing should be automated wherever possible.
Data must be entered only once.
Processes that update a given data element must be standard across the information system.
Data must be recorded as accurately and completely as possible, by the most informed source, as close
as possible to their point of creation, and in an electronic form at the earliest opportunity.
Where practical, data should be recorded in an auditable and traceable manner.
The cost of data collection and sharing must be minimised.
Data must be protected from unauthorised access and modification.
Data must not be duplicated unless duplication is absolutely essential and has the approval of the
relevant data steward. In such cases, one source must be clearly identified as the master, there must be
a robust process to keep the copies in step, and copies must not be modified (i.e., ensuring that the
data in the source system is the same as that in other databases).
Data structures must be under strict change control, so that the various business and system
implications of any change can be properly managed.
Whenever possible, international, national, or industry standards for common data models must be
adopted. When this is not possible, organisational standards must be developed instead.
Data should be defined consistently across the Enterprise.
Users must accurately present the data in any use that is made of them.

42
DATA GOVERNANCE
FRAMEWORK
• PROGRAMME &
MATURITY

43
A DATA
GOVERNANCE
FRAMEWORK
IPL DG
Framework
Council &
Organisation
Council Terms
of Reference
Working Groups
Alignment
Liaison
Roles &
Responsibilities
Owners
Stewards
Custodians
Data
Governance
Office
Data
Management
Policies &
Processes
Principles
Policies
Standards
Processes
Programme
Maturity Matrix
Strategy
Scope
Business Case
Implementation
Reporting &
Assurance
Perform
Measur
Contin
Improve
Evide
Repos
Commun

45
Overall Data Governance Maturity
Level 1 - Initial
Level 2 -
Repeatable
Level 3 -
Defined
Level 4 -
Managed
Level 5 -
Optimised
There is no clear
data ownership
assigned. Data
Owners, (if any),
evolve on their
own approach
during project
rollouts (i.e. self
appointed data
owners). No
standard tools
nor
documentation
is available for
use across the
whole
enterprise.
A Data
Ownership
Stewardship &
Governance
Model does not
exist. Owners
are
commissioned
in the short-
term for specific
projects &
initiatives. This
is often
department or
silo focused
leading to
ownership by
A defined
Enterprise wide
Data Ownership,
Stewardship &
Governance
Model exists.
Conceptual
Enterprise wide
Data model in
place &
ownership
model is loosely
applied to major
data entities.
Limited
collaboration.
Organisation not
Enterprise Data
Ownership,
Stewardship &
Governance
Model is
implemented
for the major
data entities.
Collaboration
between
stakeholders is
in place.
Governance
process
regularly
reviews this
model and its
Enterprise wide
Data Ownership,
Stewardship &
Governance
Model has been
extended such
that the
majority of data
assets are now
under active
stewardship.
Effective data
governance
processes are
employed by
stakeholders &
stewards. Well

46
DATA GOVERNANCE MATURITY BY COMPONENT
Level 1 Initial Level 2
Repeatable
Level 3 Defined Level 4 Managed Level 5
Optimised
Data
Governance
Council &
Organisation
Individual project boards
and functional areas
reacting to data issues
when raised.
Informal group of data
champions / subject matter
experts without budget
advising functional areas
and projects
Vision for Data Governance
defined but not fully
bought into .
Data issues addressed by
programme management
or Enterprise Architecture
Executive level sponsorship
and council full terms of
reference and sub groups in
place.
Accountabilities for all
aspects of data defined and
regularly reviewed
Recognised by C level
executives with regular
meetings and decisions
communicated
DG Council part of business
internal controls
Ownership /
Stewardship
Roles &
Responsibilit
ies
No clear ownership
assigned. Individual
system and analysts
assumed responsible for
data or self appointed
Data champions or super
users in business functions
but limited collaboration
for shared data.
Ownership and stewardship
defined and loosely
applied to a Master Data
subject.
Responsibilities part of role
descriptions
Key data subjects have
owners / stewards
appointed with
responsibilities measured
and rewarded
Majority of data subjects
are actively stewarded in
accordance with polices and
standards and are accepted
across organisation
Principles,
Policies &
Standards
No policies or standards
specifically covering
relevant component
subjects.
Limited number of formal
policies but ways of
working in hand or projects
initiated.
Principles and Policies for all
subjects agreed and
published
Standards adopted or being
rolled out
Processes in place to assure
policies and standards are
being adopted and
achieved.
Dispensations and issues
resolved
Policies and standards
regularly reviewed and
approved by DG Council.
Changes readily adopted in
operations and projects
Data
Governance
Programme
Data issues raised and
considered as part of
requirements for projects.
No cross business area
mandate
Individual data projects
cover local initiatives with
some interaction
Data Governance and
Management Strategy
across organisation
developed and
communicated.
Programme kicked off to
establish DG processes
Major components of DG
covered.
2nd iteration to refine
processes and management
taking place.
Constant communication
and DG part of induction
training
Programme completed and
continuous improvement of
Governance components
through review and refine
cycle
Communication and
updating training ongoing
Reporting &
Limited, ad-hoc and
varied levels of reporting
Standards for projects and
Shared repository for data
related documents and
Documents and measures
regularly reviewed and
DG Council working on
exception reporting basis.
As-Is To-BeTransition Plan

47
Maturity: Data Governance Council & Organisation
Repeatable
Level 3
Defined
Level 4
Managed
Level 5
Optimised
Individual
project boards
(where they
exist) and
Business
functional
areas reacting
to data issues
when they are
raised . No
proactive data
planning.
An informal
group of data
champions or
data subject
matter experts
without budget
or a central
function
advising
functional areas
and projects.
Need for Data
Governance
recognised &
pushed by 1 or
2 visionaries but
A vision for
Enterprise Data
Governance is
defined but not
fully bought
into across the
business.
Data issues are
addressed by
Programme
Management or
Enterprise
Architecture.
Executive level
sponsorship
established and
full terms of
reference for a
DG council is
established.
Sub groups start
to be put in
place. RACI /
accountabilities
for all aspects
of data are
defined,
workflows
established and
DG fully
recognised by C
level executives
with regular
meetings and
decisions
communicated
DG Council part
of business
internal controls

48
Maturity: Data Ownership & Stewardship Roles +
Responsibilities
Repeatable
Level 3
Defined
Level 4
Managed
Level 5
Optimised
No clear Data
ownership
has been
assigned.
Individual
system
owners
and/or
technicians or
analysts
assumed to
be
responsible
Data
champions or
super users
with passion
for data
emerge in
business
functions.
Limited
collaboration
for shared
data, common
data policies &
Data
ownership
and
stewardship is
defined and
loosely
applied to a
Master Data
subject area.
Responsibilitie
s for Data now
become part
of role
Corporate
Data model
developed,
Data Subject
areas defined.
Major data
subjects have
data owners /
stewards
appointed
with their
responsibilitie
s measured
All data
subject areas
have Data
owners. The
majority of
data subjects
areas are
actively
stewarded in
accordance
with polices
and standards
and are

49
Maturity: Principles, Policies & Standards
Repeatable
Level 3
Defined
Level 4
Managed
Level 5
Optimised
No published
principles,
policies or
standards
specifically
covering
relevant
component
data subjects.
A limited
number of
formal policies
emerge.
Limited
traction in
turning
policies /
principles into
actions.
Principles,
Policies and
Standards for
most Data
subjects
agreed and
published.
Standards
adopted and
being rolled
out
Processes put
in place to
assure the
principles,
policies and
standards are
being adopted
and achieved.
Dispensations
and issues
resolved via
agreed
workflow
involving Data
owners.
Data
Principles,
Policies and
standards are
regularly
reviewed and
approved by
the Data
Governance
Council.
Changes
readily
adopted in
operations
and projects

50
Maturity: Data Governance Programme
Repeatable
Level 3
Defined
Level 4
Managed
Level 5
Optimised
Data issues (if
identified) are
raised and
considered as
part of
requirements
for projects.
Shared data
subject areas
not
considered.
No cross
business area
mandate for
data.
Individual data
projects within
one business
area cover local
initiatives.
Interaction
regarding
shared data &
ownership is
primarily
within one
business unit.
Limited
interaction
outside of
business unit.
Data
Governance
and
Information
Management
Strategy across
the
organisation
developed and
communicated.
Formal
programme is
kicked off to
establish DG
processes.
Major
components of
DG now
covered.
Communities
of interest
established.
2nd iteration to
refine
processes and
management
taking place.
Constant
communication
regarding DG
forms part of
DG Programme
completed
with
continuous
improvement
of Governance
components
through review
and refine
cycle.
Regular
communication
and updated
training is on-
going.

51
Maturity: Data Governance Reporting & Assurance
Repeatable
Level 3
Defined
Level 4
Managed
Level 5
Optimised
Limited, ad-
hoc and
varied levels
of Data
Governance &
Quality
reporting.
Where it exists
is aligned to
local
initiatives of
functional
areas,
business
processes or
Standards
being defined
and enacted
for projects
relating to Data
Governance,
Quality and
operational
reporting of
data issues and
architecture.
A shared
widely
accessible
repository
exists for data
related
documents and
data models.
Detailed
requirements
for data quality
measures and
metrics are
developed.
Models, data
related
documents and
Data Quality
measures are
regularly
reviewed and
approved.
Processes put
in place to
deliver
assurance and
to audit
documentation
.
Data
Governance
Council now
working on an
exception
reporting basis.
Few assurance
and audit
issues are
apparent but
where they
exist are
resolved
quickly.

52
DG MATURITY
BY COMPONENT
0
1
2
3
4
5
Data Governance
Council &
Organisation
Data Ownership &
Stewardship Roles
+ Responsibilities
Information
Principles, Policies
& Standards
Data Governance
Programme
Data Governance
Reporting &
Assurance
Vision DG Maturity
Target DG Maturity
Baseline DG Maturity

53
DATA GOVERNANCE IMPLEMENTATION

54
A DATA GOVERNANCE METHODOLOGY
Conceptual Models

55
ENABLERS FOR DATA GOVERNANCE
• High Level Sponsorship
• Data Management Strategy
• Data Management Plan
• Data Architecture & Models … rich metadata
• Data Principles, Policies and Standards
• Organisation Structures, Roles & Responsibilities, Terms of Reference
• Governance Processes
• Performance Measurement and Reporting
• Tools / Supporting IT

56
MATURITY – MODELS & TAXONOMY

57
EXAMPLE GOVERNANCE WORKFLOW
Responsible (R)
Accountable
(A)
Consulted (C) Informed (I)
Gordon Banks
Chief Steward (Finance)
Bobby Moore
Chief Steward (Sales)
Geoff Hurst
Data Steward (Finance)
Nobby Stiles
Business Steward (Finance)
1 2
3 4
Review
Approve
Notify
Example: New (or revised) data definition, quality criteria, security (eg access control) are required for data items in a data
subject area. In this example we’ll use some financial data such as Credit Limit, Debt amount, Current Credit Amount
The request is received and the business data steward in Finance Nobby (2) is consulted and reminds Geoff (1) that it’s not
just finance who use this data, although its only finance who should be permitted to update Credit Limit.
Gordon (3) makes a great save and approves the changes which are then made.
The changes (or additions) are notified to the chief data steward in Sales Bobby (4) because Sales are also stakeholders for
this data.

58
DATA GOVERNANCE
FRAMEWORK
• REPORTING &
ASSURANCE

59
A DATA
GOVERNANCE
FRAMEWORK
IPL DG
Framework
Council &
Organisation
Council Terms
of Reference
Working Groups
Alignment
Liaison
Roles &
Responsibilities
Owners
Stewards
Custodians
Data
Governance
Office
Data
Management
Policies &
Processes
Principles
Policies
Standards
Processes
Programme
Maturity Matrix
Strategy
Scope
Business Case
Implementation
Reporting &
Assurance
Perform
Measur
Contin
Improve
Evide
Repos
Commun

60
Dimensions Measures
Data Governance
Organisation &
Structures
Roles &
Responsibilities
Assigned
Standards &
Guidelines
Training &
Mentoring
Data Definitions
Accuracy
Integrity
Consistency
Completeness
Validity
Workflow &
Decisions
Decision workflow
queues
Decisions resolved &
outstanding
EXAMPLE DATA
GOVERNANCE
METRICS

61
Dimensions Measures Indicators
Data Quality
Accuracy
Validity
Percentage of Fields
Deemed to be Valid
Integrity
Credibility
Percentage of
Numerical
Aggregations within
Tolerance
Currency
Timeliness
Punctuality
Percentage of Records
Received On Time
Coverage
Completeness
Percentage of
Mandatory Fields
Supplied
Uniqueness Percentage of Records
Deemed to be Unique
Percentage of
Records Deemed to
be Valid
Percentage of
Optional Fields
Supplied
Percentage of
Expected Records
Received
EXAMPLE
DATA QUALITY
METRICS

62
SUMMARY
• DATA GOVERNANCE

63
LESSONS FROM THE FIELD ….
One size does NOT fit all
Need to have a flexible approach to Data Governance that delivers
maximum business value from its data asset
Data Governance can drive massive benefit
Needs reuse of data, common models, consistent understanding,
data quality, and shared master and reference data
A matrix approach is needed …
Different parts of the organisation and data types will need to be
driven from different directions
… And central organization is required
To drive Data Governance adoption, implement corporate
repositories and establish corporate standards

64
THE BOTTOM LINE
This is only important if
Information is REALLY treated as
a valuable corporate asset in
YOUR Business

66
PRODUCTS CONCEPTUAL DATA MODEL

STATOIL ENTERPRISE MODELS
Business partner
Statoil Enterprise Data Model
Exploration ( DG1) & Petroleum technology (DG1-DG4)
Seismic Wellbore data
Geological & reservoir models
Production
volumes
ReservesTechnical info (G&G reports)
License
Contractors
Supply chain
Inventory
Requisitions
Agreements
IT
Administrative info
Operation and Maintenance
Petroleum
technical data
Corporate Executive Committee
Operations
Government
Marketing & Supply
Contract
Price
Email
Operation
assurance
Delivery
Finance & Control
Perform reporting
Production, License split (SDFI), Invoice
Management
system
Governing doc.
SDFI
Customer
Drilling & well technology ( DG4)
Drilling data
Monitoring data
IT inventory
Geography
IT project portfolio
LogisticsProject portfolio
(Business case)
Global ranking Redeterminations
Reservoir mgmt plans
Maintenance program
Material master
Technical information (LCI)
Risk information
Archived info
Mgmt info (MI)
Vendor Vendor
Authorities
Partners
Directional data
Process area
Equipment monitoring
Contract
Deal
Market info
Profit structure
Invoice
Volume
Commodity
Invoice
Position and risk result
Delivery
Monitoring plan
Operating model
Human
Resources
Health, Safety &
Environment
Health info Safety info
HSE Risk Incidents
Attraction information Security info Env. info
Emergency info
Plant
Project portfolio
Drilling candidates Master drilling plan
Drilling
plans Well construction
Project development Technical concepts Facility def. package Technology qualifications
Quality planProject framing Project work planWBS Manpower projection planProject portfolio
CD&E:
Management system Values
Variation orders
Project documentation
GSS O&P
Financial transactions
Financial reports Fin planning
Calendar
Investment analysis
Fin authorities
Operation profit
IM/IT strategies
Estimates Risk register Document plan
Credit info
Supply plan
Refining plan
Lab analysis
Contact portfolio
Financial results
Legal
Company register
Service Management
Service catalogue
Ethics &
anti-corruption
Corp. social resp.
Social risks and impacts
Governing body doc
Integrity Due
Diligence reportsSustain. rep CSR plans Enquiries Agreements
Technology
dev.
R&D portfolio
IPR register
Communication
Brand
Authority information
Facilities
Real Estate
Access info
Country analysis
Risk
Corp risk
Business continuity plans
Insurance
Organisational info
Capital Value Process
Business planning DG0 Feasibility DG1 Concept DG2 Definition DG3 Execution DG4 Operation
Post Investment ReviewBenchmarkingDecision Gate Support Package Decision memo Project infoBusiness Case Leadership Team infoBusiness case
Functional location (tag) Volume monitoring
Version 21-Jan-2011
Investment project structure: PETEC, D&W, FM, OM
Perf. and reward info
A yellow background indicates that the information subject area contains Enterprise Master Data
Maintenance projects

STATOIL ENTERPRISE MASTER DATA MODEL

CATALOG CURRENT INITIATIVES
USING THE PROJECT PORTFOLIO
Decision gate: Where is the
initiative in the life project process
right now?
Owner: Which Business area owns
this initiative?
Item Name: What’s the internal
name of the project / program /
initiative?
Business Data Objects: What (in
their own terms) are the Business
Data “things” affected by this
program?
Interest: How interested / willing
is this project to engage with the
MDM initiative?
Importance: How important to the
Data Area is the MDM initiative?

Prioritise by multiple criteria (willingness to engage, feasibility, timescales, importance)
Forget: Timescales, level of engagement,
strategic importance wrong. “Train has left
the station”
Improbable: Timescales for Business
initiative too tight to successfully introduce
MDM without adversely affecting Business
programme.
Stretch: Good engagement, good strategic
fit, tight timescales. Spiking in resources
immediately can make these data areas fly.
Prime Candidates: Great engagement,
good strategic fit, ok timescales & widely
usable Data subject areas.

HARMONISE & XREF WITH DATA MODEL

74
COLLECTIONS EXAMPLE ILLUSTRATIVE PURPOSES
ONLY

75
AS-IS: UNMANAGED SUBJECT & COLLECTIONS
Business Party
Customer
Supplier
Counter Party
- DUNS #
- Counterparty Name
R&M IST
Subject
Hierarchy
Subject
Attribute
Self Appointed Data
Collection
Multiple Processes need the same data!
Delegation of Data Subject Authority not resolved.
Results: duplication, inconsistency and re-work
Subject
Self Appointed Data
Collection

76
TO-BE: MANAGED SUBJECT & COLLECTIONS
Business Party
Customer
Supplier
Counterparty
- DUNS #
- Counterparty Name
R&M
IST
Subject
Hierarchy
Subject
Subject
Attribute
Governed Data
Collection
Governed Data
Collection

77
HOW DOES THIS HELP THE BUSINESS COMMUNICATE
WITH IT&S?
Governed by the Business;
modeled by IT&S
Governed by IT&S
Communication Bridge
Collaboration between the
business & IT&S, and modeled
by IT&S
High level Subjects and
Subject hierarchies, grouped
into collections
Collections, Subjects, Subject
Hierarchies & Attributes =
IT&S “Logical Data Model”
Physical Model

78
BUSINESS DATA GOVERNANCE ROLES
1. Organizational Delegation of Authority (DOA); Examples:
• Backbone Governance Board
• Function Leader, Segment Leader
• SPU leader
• BU Leader
• Etc.
2. Implementation & Improvements
• Information Director
3. Specification Owners (Makes the rules)
• Subject Owner – hierarchy and other specifications
• Attribute Owner – detailed specifications
• Collections Owner – sets subject hierarchy boundaries
4. Content
• Data Steward (Follows the rules)
• Quality Control Data Steward (enforces the rules)

79
BUSINESS
SPECIFICA
TION AND
CONTENT
GOVERNA
NCE
Local Information
Director
Local Specification
Owners
[local data]
Data Steward(s)
Data Quality Steward(s)
Collaborating
Specification Owners
[Data common across
many localities]
+
Collaborating
Information Director(s)+
IT&S & Business Implementation
re-using common data

80
INFORMATION GOVERNANCE
Ongoing data maintenance
and quality
Compliance with policy
and procedures
Three tiered governance with individual
accountability: By SUBJECT AREA
Information
Owners:
Information
Stewards:
Information Director:
Maintain high-level corporate data model
Define the overall process and framework
Allocate accountability for individual data entities
Determine business process to manage data
Mandate stewardship and quality activity
Primacy over entire data entity, including data
quality metrics

Implementing Effective Data Governance

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