modul pembelajaran robotic Workshop _ by Slidesgo.pptx
Data Governance by stealth v0.0.2
1. Selling Data Governance or
DG by Stealth?
C H R I S T O P H E R B R A D L E Y
C H I E F D A T A O F F I C E R
2. P / 2
Introduction: Who Am I?
My blog: Information Management, Life & Petrol
http://infomanagementlifeandpetrol.blogspot.com
@InfoRacer
uk.linkedin.com/in/christophermichaelbradley/
Christopher Bradley
Chris@chrismb.co.uk
3. P / 3
Introduction To Chris Bradley
Chris is a leading Information Management strategist with 34 years
experience in the Information Management field, Chris works with
leading organisations including Total, Barclays, ANZ, GSK, Shell, BP,
Statoil, Riyad Bank & Aramco in Data Governance, Information
Management Strategy, Data Quality & Master Data Management,
Metadata Management and Business Intelligence.
He is a Director of DAMA- I, holds the CDMP Master certification,
examiner for CDMP, a Fellow of the Chartered Institute of
Management Consulting (now IC) member of the MPO, and SME
Director of the DM Board.
A recognised thought-leader in Information Management Chris is
creator of sections of DMBoK 2.0, a columnist, a frequent
contributor to industry publications and member of standards
authorities.
He leads an experts channel on the influential BeyeNETWORK, is a
regular speaker at major international conferences, and is the co-
author of “Data Modelling For The Business – A Handbook for
aligning the business with IT using high-level data models”. He also
blogs frequently on Information Management (and motorsport).
6. P / 6
Recent Presentations
DAMA UK Webinar: February 2015; “An Introduction to the Information Disciplines of the
DAMA DMBoK”
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 IPL’s 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”
7. P / 7
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
8. P / 8
Data Governance
Foundations
W H A T I S D A T A G O V E R N A N C E
W H Y D A T A G O V E R N A N C E
B U S I N E S S C A S E
10. P / 10
DAMA
Framework
IM Disciplines (DMBoK 1)
DATA
ARCHITECTURE
MANAGEMENT
DATA
DEVELOPMENT
DATABASE
OPERATIONS
MANAGEMENT
DATA
SECURITY
MANAGEMENT
REFERENCE &
MASTER DATA
MANAGEMENT
DATA
QUALITY
MANAGEMENT
META DATA
MANAGEMENT
DOCUMENT &
CONTENT
MANAGEMENT
DATA
WAREHOUSE
& BUSINESS
INTELLIGENCE
MANAGEMENT
DATA
GOVERNANCE
11. P / 11
What Is Data Governance?
The Design & Execution Of 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 whole 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 …
› A “one off” Tactical management exercise
› The responsibility of the Technology and IT department alone
T H E E X E R C I S E O F A U T H O R I T Y A N D C O N T R O L , P L A N N I N G , M O N I T O R I N G , A N D
E N F O R C E M E N T O V E R T H E M A N A G E M E N T O F D ATA A S S E T S . ( D A M A I N T E R N A T I O N A L )
12. P / 12
Data governance – alternate
definitions
“Data Governance is the exercise of
authority and control (planning,
monitoring, and enforcement) over
the management of data assets.”
(DAMA International)
“Data Governance is a quality control
discipline for adding new rigor and
discipline to the process of managing,
using, improving and protecting
organizational information.”
(IBM Data Governance Council)
“Data Governance is a system of
decision rights and accountabilities for
information-related processes,
executed according to agreed-upon
models which describe who can take
what actions with what information,
and when, under what circumstances,
using what methods.”
(Data Governance Institute)
“Data Governance is the formal
orchestration of people, processes,
and technology to enable an
organization to leverage data as an
enterprise asset.”
(MDM Institute)
13. P / 13
Data Governance – a simple
definition
“The process of
managing and
improving data for
the benefit of all
stakeholders”
15. P / 15
Why Is Data Governance Critical?
_Higher volumes of data generated by
organisations
_Proliferation of data-centric systems
_Greater demand for reliable information
_Tighter regulatory compliance
_Competitive advantage
_Business change is no longer optional;
it’s inevitable
_Big Data explosion (and hype)
16. P / 16
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
_Improved opportunity to rapidly and
effectively exploit information for
customer insights and competitive
advantage
_ Increased agility and 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
I N F O R M A T I O N T H A T I S T R U S T E D A N D F I T F O R P U R P O S E
17. P / 17
A typical average company
loses 30% of revenue and
turnover through poor
data quality
Millions of UK National
Health Service patient
records sold to insurance
firms
On average, organizations
waste 15-18% of budgets
dealing with data
inaccuracies
The US economy loses
$3.1 trillion a year
because of poor data quality
Why Data Governance?
18. P / 18
3 motivations
for Data
Governance
2. Pre-emptive Governance
1. Reactive Governance
3. Proactive Governance
19. P / 19
Motivations For Data Governance
_Tactical exercise
_Efforts designed to respond to current pains
_Organisation has suffered a regulatory breach or a data disaster
R E AC T I V E GOV E R N A NC E
_Organisation 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
PRE-EMPTIVE 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
PROACTIVE GOVERNANCE
“If your main motivation for Data Governance is
Regulation & Compliance, the best you can ever
hope to achieve is just to be compliant”
22. P / 22
What is the Business Motivation Model?
The language of strategic planning is often inconsistent. The
BMM provides us with a Consistent Language to articulate
business strategy.
“The BMM is a technique in which one determines an ultimate
goal and determines the best strategy for attaining the goal in
the current situation”
Mission
Strategies
Tactics
Vision
Goals
Objectives
A statement describing the aims,
values and overall plan of an
organisation.
e.g. “To be the leading creator and
protector of wealth.”
The strategic plan.
e.g. “Defend our current
customer base to reduce churn
and increase repeat business”
A concise statement of a desired
change.
e.g. “To be the leading provider of
wealth management services in our
major target markets within the next 5
years.”
A high level statement of what the
plan will achieve.
e.g. “Improve customer satisfaction
(over the next five years)”
A Course of Action that channels
efforts towards objectives
e.g. “Call first-time customers
personally”
The outcome of projects improving
capabilities, process, assets, etc.
e.g. “Develop an operational
customer call centre by June 30, 2015”
23. P / 23
The Business Motivation Model Example
The Motivation Model resonates well with business sponsors
› Business Stakeholders can often
find business architecture models
difficult to understand
› The Business Motivation model
resonates well with business
stakeholders allowing us to talk in
Business terms
› Helps move away from point
solutions to focus on business
outcomes
25. P / 25
Validate and Refine
Business Goals
_ A Goal is a statement about a state or condition of the
enterprise to be brought about or sustained through
appropriate Means.
_ A Goal amplifies a Vision — that is, it indicates what must
be satisfied on a continuing basis to effectively attain the
Vision.
_ A Goal should be narrow — focused enough that it can
be quantified by Objectives.
_ A Vision, in contrast, is too broad or grand for it to be
specifically measured directly by Objectives.
However, determining whether a statement is a Vision or a
Goal is often impossible without in depth knowledge of the
context and intent of the business planners.
In light of the mission and vision and the
influencer pressures, validate and refine the
goals of the organisation
26. P / 26
Look for Levers
Derive a set of measurable levers of business value
and growth by cascading down the drivers of income
in your business.
_ The levers are intended to be durable even as business
strategy shifts.
Value levers indicate which business dimensions need
to be analysed for change projects.
_ Business consultants use the matrix to understand
which business architecture dimensions have the
greatest impact on each lever, focusing attention on
those dimensions most relevant to the levers in focus.
Look for levers that can help you
address the goals
27. P / 27
Improvement
Levers Example Increase price
Increase volume
Improve mix
Improve process
Reduce cost of inputs
Improve warehouse
utilisation
Increase productivity
Decrease staffing
Optimize scheduling
Optimize physical network
Decrease staffing
Use alternative distribution
Lower Customer Service &
Order Management Costs
Lower I/S costs
Lower Finance /
Accounting costs
Lower HR costs
Improve capital planning/
investment process
Reduce inventories
Reduce A/R increase A/P
o Profit-driven marketing
efforts:
• Target “best” customers
• Offer “best” product mix
• Improve pricing
management
• Proactive production
planning for inventory
management
• Most profitable capacity
allocation/utilization
o Reduced sales management
layers
o Focus on high-profit
accounts
o Improved inventory flow
visibility
• Lower transportation costs
• Higher facilities utilization
• Less “fire fighting”
o Better carrier
evaluation/mgmt.
o Higher quality Customer
Service
o Improved Supply Chain
visibility
• Improved order fill rates
• Significantly lower cost
• More consistent service
• Faster problem resolution
o Improved capital
stewardship
• Increased capital
productivity
• Reduced inventory
investment
• Reduced receivables
investment
o Automated PO
requisitions
o Improved information for
evaluating vendors
o Automation of some
scheduling functions
o Single point of entry
eliminates data re-entry
and improves accuracy
o Faster data reconciliation
o Automated billing
processes
o Automated payroll
processes
o Moderately lower safety
stock inventory
o Moderately improved
A/R and A/P
management
Increase
revenues
Decrease
costs
Reduce
selling costs
Reduce
distribution
costs
Reduce
administrative
costs
Increase
gross profit
Decrease
operating
expenses
Capital
deployment
Cost
of capital
Increase net
operating
profit after
tax (NOPAT)
(I/S)
Improve
capital
allocation
(B/S)
Enterprise
Value
Map
VALUE LEVERS
TRANSFORMATION
BENEFIT (Outcome)
AUTOMATION
BENEFIT
Align benefits
with Information
28. P / 28
Example
Decomposition
What are our corporate goals?
What are the priorities and battlegrounds given our corporate goals?
What IT assets and data do we need to support these capabilities?
How will our business model change over the next three to five years?
What are the key capabilities that will maximize value creation in the business?
How do we optimize our IT operating model to deliver the required business capabilities?
Earn all
Our customers’
business
Drive a strong
Customer culture
Enhanced branch
Capability and
Power in frontline
Transform
Service delivery
And processes
Customer
centricity
Front office
empowerment
Channel and
Product operational
excellence
Customer
Profile
management
Relationship
management
Customer
analytics
Offer
design
Product
management
Integrated
Data store
Enterprise
Service bus
Channel
platforms
Product
platforms
Security
platforms
End-user
computing
Customer
analytics engine
Universal
customer master
Integrated sales &
Service front end
Internet platform
transformation
Core banking
transformation
Vision
Strategic agenda
Business objectives
Business capabilities
IT capabilities
IT investments
30. P / 30
DATA
ARCHITECTURE
MANAGEMENT
DATA
DEVELOPMENT
DATABASE
OPERATIONS
MANAGEMENT
DATA SECURITY
MANAGEMENT
REFERENCE &
MASTER DATA
MANAGEMENT
DATA QUALITY
MANAGEMENT
META DATA
MANAGEMENT
DOCUMENT & CONTENT
MANAGEMENT
DATA
WAREHOUSE
& BUSINESS
INTELLIGENCE
MANAGEMENT
DATA
GOVERNANCE
› Enterprise Data Modelling
› Value Chain Analysis
› Related Data Architecture
› External Codes
› Internal Codes
› Customer Data
› Product Data
› Dimension Management
› Acquisition
› Recovery
› Tuning
› Retention
› Purging
› Standards
› Classifications
› Administration
› Authentication
› Auditing
› Analysis
› Data modelling
› Database Design
› Implementation
› Strategy
› Organisation & Roles
› Policies & Standards
› Issues
› Valuation
› Architecture
› Implementation
› Training & Support
› Monitoring & Tuning
› Acquisition & Storage
› Backup & Recovery
› Content Management
› Retrieval
› Retention
› Architecture
› Integration
› Control
› Delivery
› Specification
› Analysis
› Measurement
› Improvement
Data Governance
Is At The Heart Of
ALL Information
Management
Disciplines
Information Management Disciplines
DAMA-International
31. P / 31
Data Governance
Part Of An Overall EIM Framework
I N F O R M A T I O N I S A T T H E H E A R T O F T H E
B U S I N E S S & M U S T B E M A N A G E D
E F F E C T I V E L Y T O D R I V E V A L U E
34. P / 34
4 – MAYBE MORE
Organisational Models For DG
PROCESS CENTRIC
Process owner(s) become(s) the data
owner for all data created, amended
& deleted by the business process for
which he / she is responsible.
DATA CENTRIC
Business appointed FT or PT roles
accountable for improvement of key
data domains wherever created or
used across an organisation, e.g. Data
Stewardship.
SYSTEMS CENTRIC
System owner(s) become(s) the data
owner for all data created, amended
& deleted by the system for which he /
she is responsible.
CONTINGENT
There is no single best model for data
governance, either when initiating
data improvement activities, or as
Business As Usual. The best model is
dependent on the type of data and
the circumstances of each initiative, at
each stage of maturity.
35. P / 35
Data Governance
Organisations
DATA GOVERNANCE COUNCIL
The primary and highest authority organisation for data
governance. Includes senior managers serving as
executive data stewards, DM Leader and the CIO.
DATA STEWARDSHIP STEERING COMMITTEE
One or more cross-functional groups of coordinating data
stewards responsible for support and oversight of a
particular data management initiative.
DATA STEWARDSHIP TEAM
One or more business data stewards collaborating on an
area of data management, typically within an assigned
subject area, led by a Coordinating Data Steward.
DATA GOVERNANCE OFFICE
Exists in larger organisations to support the above teams.
36. P / 36
Data Stewards
EXECUTIVE DATA STEWARD
Senior Managers who serve on a Data Governance
Council.
COORDINATING DATA STEWARD
Leads and represents teams of business data stewards in
discussions across teams and with executive data stewards.
Coordinating data stewards are particularly important in
large organizations.
BUSINESS DATA STEWARD
A knowledge worker and business leader recognized as a
subject matter expert who is assigned accountability for the
data specifications and data quality of specifically
assigned business entities, subject areas or databases.
38. P / 38
What’s the evidence?
_Starting “bottom up”
_Gather the facts – horror stories work well
_Undertake Data Quality profiling
_Publish DQ metrics
› Unconscious competition
› Teases out who is responsible for the data
› Improvement Projects begin to self form
› Ultimately becomes self policing
› Data Governance (lite) starts to emerge as the
way to address the issues
› Momentum & an appetite for DG created
E X P O S E T H E P R O B L E M
39. P / 39
Perception is
important
_Don’t call it Data Governance (at least at
the start)
_Start Small
_Promote Data Improvement Projects (vs a
Data Governance strategy)
_Who is responsible for the data?
W H A T ’ S I N A N A M E ?
40. P / 40
Identify Best Practices
_Identify in-house good guys
_Does anyone actually do it well?
_What are current best practices
_Where is there some passion & emotion
about data, it’s quality and meaning?
_Often found in downstream areas who are
impacted day to day; e.g.
› ETL developers
› BI users
› Customer Service operators
› DBA’s
I S A N Y O N E D O I N G I T W E L L ?
41. P / 41
Join it up
_Identify the Islands of excellence / atoll's of
mediocrity
_Join them up
_Community of interest
_Promote as best practice
_Evolve Organisation structures
› Do not set up the target DG organisation too
early
› Have the target in mind
› Develop transition steps
I S L A N D S O F E X C E L L E N C E ?
42. P / 42
Land & expand
_Community of interest evolves best practices
that work in your environment
› A gentle steer & guidance is always useful
› Operating models & processes emerge
_Communicate successes & widen COI
_Establish common glossaries
› Always useful across the organization
› What do you mean by XYZ?
_Infiltrate Data Governance into existing
processes
› Jump on transformation programs
› “Customer First”
› Process Improvement
_Grow incrementally & eventually “top down”
support emerges
U N D E R T H E R A D A R ?
43. P / 43
I N D E P E N D E N T O R C O E X I S T E N T ?
MDM & DG
44. P / 44
Benefits$
# projects / reused objects
Portfolio planning / design for reuse
Project by project / without reuse
Design for reuse: First projects hit a
“cost” as there is nothing in place
that can be re-used / leveraged for
benefit.
Project based accounting
discourages infrastructure
investment.
Seed Money: To not penalise initial projects,
but rather encourage them to do the “right
thing” for the corporation, seed money helps
with provision of resources, budget offset
etc.
Design for reuse: Once a few reusable
artefacts, models, Master Data
objects, reusable methods, skills etc.
are established, projects start to reap
big benefits
Design in isolation: Initially no
interaction outside the confines of “the
project” and just a few interfaces will
appear attractive as no wider
considerations need to be made
Design in isolation: Costs increase
dramatically with Increasing number
of point to point interfaces, undo-
redo work as clashes about data
concepts explode.
Portfolio vs Per Project
45. P / 45
Plan Big – implement small
Business Initiative / Project 1
Some of MD
area A
needed
here
Some of MD
area B
needed
here
Business Initiative / Project 2
More of MD
area A
needed
here
Some of MD
area C
needed
here
More of MD
area B
needed
here
Business Initiative / Project 3
More of MD
area C
needed
here
More of MD
area B
needed
here
More of MD
area A
needed
here
Business Initiative / Project 4
Lots of MD
area D
needed
here
More of MD
area B
needed
here
More of MD
area A
needed
here
Project4 later
includes MD for
area D
MDM program cannot deliver
Data Subject Area D at this time
for Project 4.
Project 4 gains exemption to add
this MD later
IN THE CONTEXT OF THE BIG PICTURE
IMPLEMENT MDM IN ALIGNMENT WITH BUSINESS INTITIATIVES
47. P / 47
Why might DG fail?
_Lack of business leadership and commitment
_Failure to link Data Governance to
organisational goals and benefits
_Giving people data responsibility but not
equipping them to succeed
_Failure to focus on the data that really matters
_Placing too much emphasis on data monitoring
and not data improvement
_Thinking new technology will alone solve the
problems
_Forgetting Data Governance must embrace all
who use data across an organisation
_Not delivering benefits early and regularly
48. P / 48
Data Governance Readiness Assessment
Source: R.Brennan
49. P / 49
Typical Data Governance
Operating Models
Source: Mitre
Source: Informeta
Source: Informatica
Source: Collibra
50. P / 50
Common Themes In Operating Models
& Frameworks
Understand Business Drivers &
build a foundation
Set the
Scope
Assess
Current
position
Determine
readiness
for DG
Build
Business
Case
Understand
Business
Motivation
Define the Organisation & approach
to introduce DG
DG
implementation
Program
Communicatio
n plan
Organisation
structure &
bodies
Education
plan
Roles &
Responsibilities
Apply
Create and
apply
policies
Execute
communicatio
n plan
Organisation
structure &
bodies
Execute
education &
mentoring
Develop & roll
out standards
& procedures
Introduce DG
Processes
Introduce
DGO
Establish
Principles
Monitor, Report &
Measure
Adherence
to Principles
DG Metrics
Feedback &
continuous
improvement
DG
Knowledge
Base
51. ESTABLISH FOUNDATION
ESTABLISH
STRATEGY
BUILD
BUSINESS
CASE
AGREE DG
SCOPE
ESTABLISH
AS-IS
SITUATION
ESTABLISH DG PROGRAM
CREATE COMMUNICATION
STRATEGY
ORGANISATION
OWNERS STEWARDS
CUSTODIANS STAKEHOLDERS
COUNCIL
DG
GROUPS
WORKING
GROUPS
DGO
DIRECTION
PRINCIPLES &
STANDARDS
POLICY
PROCESS PROCEDURES
Control & Report Control
& Report
REPORTING & ASSURANCE
PERFORMANCE
MANAGEMENT
CONTINUOUS
IMPROVEMENT
MATURITY MODEL
ContinuousActivity
InitialActivity
PLAN FOR IMPLEMENTING
DATA GOVERNANCE
Typical
Data Governance
Operating Model
52. P / 52
What is
the
Business
Motivation
What
Information
do we need
to run our
business
What
business
processes &
capabilities
must we
have
What roles
are
necessary to
operate our
business
What systems
do we
depend
upon to run
our business
Key is to understand the Business
motivation & its operation
53. P / 53
Data Governance Office
D A T A G O V E R N A N C E H A S T O U C H P O I N T S T H R O U G H O U T T H E P R O J E C T L I F E C Y C L E
V I A L I A I S O N W I T H T H E D A T A G O V E R N A N C E O F F I C E ( D G O , S I M I L A R T O P M O )
54. P / 54
Early Step: EIM Maturity Assessment
IM Disciplines IM Enablers
2
1.5
2
1.5
1.5
2
1.5
1.5
1.5
2
4
4
4
3
4
4
3.5
4
3.5
4
0
1
2
3
4
5
IM Principles
Data Governance
IM Planning
Data Quality
IM Lifecycle Management
Data Integration & Access
Data Models & Taxonomy
Metadata Management
Master Data Management
DW & BI
Information Management Maturity Assessment
Current Target
1.5
1.5
1.5
2
1.5
1.5
3.5
3.5
4
3.5
3
3
0
1
2
3
4
5
People
Processes
Executive Sponsorship/Leadership
Technology
Compliance
Measurement
Information Management Enablers Maturity Assessment
Current Target
55. P / 55
Applying The Framework
Maturity Assessment
Current Status
Vision &
Strategy
Org. &
People
DM &
Measures
Processes
& W/flows
Comms &
Training
Tools &
Technology
.
Roadmap
Implementation Plan
Business Justification
DGVision
Business
Drivers
Desired
State
56. P / 56
By aligning the various activities and providing an overarching management
framework can:
_ Identify the dependencies and boundaries of the activities,
_ Reduce the likelihood of duplication, and
_ Ensure tighter integration across the frameworks.
Architecture Framework
(TOGAF)
IT Governance
(COBIT)
Business
Analysis
(BABOK)
Data
Managemen
t
(DMBOK)
Project
Managemen
t
(PMBOK)
IT Service
Managemen
t
(ITIL)
Informs
Governs
System
Developmen
t
(SDLC)
Aligning multiple frameworks?
57. P / 57
Data governance must be
embedded within broader
governance frameworks.
Data governance is designed
to govern the data
management practices.
Data governance is informed
by the enterprise information
architecture.
A closer look at Data Governance
The exercise of authority and
control (planning, monitoring,
and enforcement) over the
management of data assets.
(DAMA International)
Data governance is NOT a
tactical one off exercise nor
the responsibility of the IT
Function alone
58. P / 58
Summary
_ Business ownership is key
_ Communication is vital
_ Must connect and align Data Governance with business
motivations, strategies and goals – current & future
_ This is not simply an IT problem. Requires holistic solutions –
people, process, and technology
_ It’s essential to outline & communicate what success can
deliver and is delivering
_ Establish the current baseline and maturity
_ Deliver early & incrementally
_ Demonstrate success & real business benefits to sustain
business support
_ Ensure accountable people are equipped to succeed –
knowledge, methods & tools; training & mentoring
_ Stealth DG is possible – up to a point
Data Governance
