Managing for Effective Data Governance: workshop for DQ Asia Pacific Congress March 2014

Alan Duncan, Director of Data Governance, UNSW
E: Alan.Duncan@unsw.edu.au Tw: @Alan_D_Duncan LinkedIn: http://www.linkedin.com/in/alandduncanUncontrolled when printed
Managing for effective Data
Governance
Delivering value while remaining sane
Alan D. Duncan March 2014

“If- “ Rudyard Kipling
“If you can keep your head when all about you
Are losing theirs and blaming it in you,
If you can trust yourself when all men doubt you,
But make allowance for their doubting too…”

A bit about me....
•  Alan Duncan, Director of Data Governance, UNSW
•  21 years Information Management & Business
Consulting
–  EDS, KPMG, CPW, Acuma, Pelion, SMS
–  Scottish Power, United Distillers, O2, Astra Zeneca,
Carphone Warehouse, Vodafone, Riyad Bank
–  Commonwealth Bank, NSW Roads & Maritime
Services, Centrelink, OATSIH, NSW Family &
Community Services, CASA, AMSA, FaHCSIA, DAFF,
Navy…
•  Information-Management.com “Top 12 on Twitter”
•  Best supporting Actor, 2005 Barnet Drama Festival

…and a bit about UNSW.

Agenda
•  Do we need to rethink our Data Governance
strategies?
•  Is enterprise-wide Data Management really
achievable?
•  What techniques and capabilities do we need to
focus on?
•  What skills and personal attributes are needed for
success?

PART 1. Do we need to rethink our
Data Governance Strategies?
Sponsored by Thomas Edison
“The value of an idea
lies in the using of it.”

“I object!”
•  “I don’t know what you’re going to do with my data once
you have it.”
•  “If I give you my data, you might then ask me to do some
extra work to meet your additional requirements.”
•  “You may not interpret the data in the same way that I do.”
•  “I’m an expert in this area, you’re not. The data is too
complex for you to understand.”
•  “It’s too difficult to get the data out of the system and I’d
need help from I.T.”
•  “I don’t have the budget to pay for your requirements.”
•  “I’d like to help but I’m just far too busy.”
•  “I know there are flaws in the data, but it’s good enough
for my needs. You might criticize me for the errors.”
•  “Management may ask additional questions and hold me
to account for the work I’m doing”.
7

“I’m not interested in
preserving the status quo;
I want to overthrow it.”

Information as a Service: “True Facts”
Identify measurable and targeted Business Outcomes
Why do we need information? For whom? What will we do
differently?
Establish DG Operating Model
Who is accountable? By what
processes?
Execute Activities & Tasks
How do we deliver? Who does the
work?
Confirm the Information Holdings & Gaps
What do we need to provide? (Content + Context)
Implement DG/IMCC Services
Catalogue:
What core capabilities do we need?“When it is obvious that the
goals cannot be reached,
don't adjust the goals,
adjust the action steps.”

Is “Open Data” a good thing?
http://www.ted.com/talks/
tim_berners_lee_the_year_open_data_went_worldwide.html
9

Outcomes of change of mindset
•  Stimulus to improve data quality
•  Consistency of data definitions
•  Openness and trust
•  Transparency & accountability
•  Opportunity value
•  Proactive publication and Open
Data vs. “Need to know”
10

“Publish and be damned!”
http://www.ted.com/talks/tim_berners_lee_the_year_open_data_went_worldwide.html

Summary: Rethinking Data Governance
11

•  Control, structure, discipline &
compliance? OR Advocacy service &
information broker?
•  Intimate understanding of business goals
& processes
•  Engagement, diagnosis & facilitation
•  Understand & articulate the meaning of
data, in context
•  Coach, mentor and advocate
•  Highly visible point-of-access
•  Self-service Information Portal
•  Conduit, communicate & co-ordinate
•  Leadership & direction
•  “Info as a Product”
“The art of government is to
make two-thirds of a nation
pay all it possibly can for the
benefit of the other third.”

Data Collection in a State Transport
Infrastructure Authority,
sponsored by Alfred North Whitehead
PART 1: Case Study
“The art of progress is to
preserve order amid
change and to preserve
change amid order.”

Data collection for road transport
•  Monitoring & management of
the road network
•  Optimise traffic throughput
•  Plan for infrastructure
investment, maintenance
•  Incident management
•  Plus strategic shift from “asset
engineering” to “customer-
centric” culture

Problem: current data can’t meet modern
needs
•  Continuing growth in traffic
•  Some “in-road” sensors over 30 years old
–  Poor data quality
–  Classification by “Big, Medium, Small”
–  No telemetry: up to 3-month lag
•  Sparse distribution of existing sensors
–  OK coverage in major urban areas
–  Few (if any) in rural areas
•  Devices do “Count” only
–  Speed not measured
•  Temporary “spot” surveys leave gaps in the record (or duplicate data!)
•  Over 1000 new sensors would be required
–  New in-road devices approx $50K each to install (as part of road build/upgrade)
–  Piezoelectric “tube” devices easily damaged, poorly installed
–  Radar devices inaccurate in the wet

Solution: augment with GPS vehicle tracking data
•  8000 fleet vehicles with “always on” GPS

Benefits
•  Travel time benchmarking
•  Flow management
•  Congestion “pinch point” analysis
•  Long-term traffic forecasting
•  Road safety speed zoning
•  Incident early-warning predictive
alerts

Customer service: real-time information
updates

PART 2. Is enterprise-wide Data
Management & Governance really
achievable?
Sponsored by Confucius
“When it is obvious that
the goals cannot be
reached, don’t adjust the
goals, adjust the action
steps.”

Information Management Strategy Drivers
Informa-on

Management

Strategy

Informa-on
&

Data
needs

Organisa-onal

Strategic

Direc-on

DG&IM
Best

Prac-ces

Information Use Cases
•  Based on our current understanding of business needs, the following classes of
Information Use Case are identified
•  Detailed Requirements Analysis should be conducted on a project-by-project basis
to explore any detailed Use Cases within each class
•  Not all detailed Use Cases need to be defined ahead of time
•  Solutions should be flexible to accommodate new and changing Use Cases
Structured

data

repor-ng

Strategic

Intelligence

and
Data

Mining

Publish

content
to
a

community

Execu-ve

brieﬁngs

Educa-on,

Training,

Learning

Search
for

content

previously

created

Records

Management,

Compliance
&

Audit

GIPA
&

Privacy

Responses

Ability
to
publish

Filtering/screening/valida7on
of
what
gets
published

Feedback
loop,
measure
of
usefulness
&
con7nuous
improvement

Shared
understanding
(IT
&
Business)

To centralise or not to centralise?

Identifying Owner & Stewards
Typically, there are significantly more
unconscious Owners and Stewards
All key stakeholders in
the Assets driven by an
informal structure
Business pain is felt but has no means of consistent resolution
Conscious Owners and Stewards
Responsibilities blurred and lack of
understanding of the relationship
and how it should work
Owners are accountable for driving up
the level of consciousness

Summary: Information Management for the whole Lifecycle
Plan
Construct,
Create,
Acquire
Commission,
Organise,
Store
Access Use Assess Maintain Retire
• Rigorously evaluate the
decision at the earliest
stages of a proposal
before investing in new or
replacement assets.
• Manage the procurement
whether it be a
construction, purchase,
lease or service
• Minimise the cost and risk of ownership with effective
maintenance strategies and procedures.
• Manage operational costs.
• Evaluate the level of investment in assets to identify
functional or physical obsolescence, financial viability, re-
use opportunities and areas of unacceptable risk.
• Consult with
stakeholders
and plan for
disposal of
assets.
• Examine all
options to
achieve
service
delivery
objectives
and meet
business
requirements.
Information
Owner
Chief
Steward
(CDO)
&
IMCC
(cross-‐func:onal,
cross
domain)

Business
Process
Business
Process
Business
Process
Business
Process
Business
Process
Data
Stewards
An Enterprise approach to Information & Data
Management requires formal organisational
processes and controls that define the rules,
roles and responsibilities for information
ownership, stewardship and associated service
capabilities.
Objective is to achieve explicit assurance for
an agreed level of information quality
(broadest definition) and links to business
value, based on the explicit capture,
formalisation and application of business rules.

Devising the information strategy for a
major retail bank, sponsored by the
Vice-President of Retail Banking
PART 2: Case Study
“We must allow him to
draw his sword….”

Information Strategy for Saudi Retail Bank - Scope
•  “Define Bank Data Management Best Practice - Production
of a definition of Data Management Best Practice appropriate
for the needs of the Bank.”
•  “Education of Bank resources – education in definition of the
Information Environment, Information Architecture and how
Data Management fits within this.”

IM is business critical for Retail Banking
•  Effectiveness
–  e.g. Marketing the right products to the right customers every time,
outperforming the competition
•  Efficiency
–  e.g. lower effort in servicing accounts due to error reduction
•  Cost
–  e.g. IT savings in application development and maintenance as a
direct result of unambiguous information definitions
•  Flexibility
–  e.g. Rapid and controlled ability to adapt without disruption
•  Risk
–  e.g. Better lending decisions, more easily established Compliance,
Trust & Reputation

Why wasn’t the Bank already doing this?
•  Financial benefits are large but...
–  Hard to quantify (Indirect, Distributed)
–  Hard to realise (Contingent)
–  Hard to track (Causality)
•  In contrast, the costs are significant and exactly
quantified
•  => Conventional investment appraisal is hard
•  => Many organisations fail to invest, and lose
competitive advantage

Strategy Development plan – 2 phases
Milestone Activity Date Description
DMD Services Solution Phase 1A started in June 17th, 2006
Assessment & Discovery
Discovery Sessions
From June 18th to
26th, 2006
Over 20 interviews with key stakeholders to evaluate Bank against four major areas
of the i-Environment model.
IVM Theory,Class Training
June 27th, 2006
Given to DM Staff
P Senior Management Feedback Workshop for DM Solution
From July 10th to 12th,
2006
Business, Technical & Architecture
Informatica Extraction Tools Training Course
From July 16th to 19th,
2006 Given to DM Staff
P Detailed Plan for Phase 1B July 13th, 2006 Delivered Project Schedule
P DMD Assessment Report July 30th, 2006 Delivered Document
DMD Services Solution Phase 1B started in July 15th, 2006
Detailed Solution Design
Batch Integration Inventory - Technical Analysis Sessions
From July 15th to Aug.
16th, 2006 Over 25 interviews with Bank systems technical consultants, reviewing the current
batch integration architecture.
P DMD Staff Profiles
August 12th, 2006
Delivered Document
P Batch Integration Inventory - Findings &
Recommendations
August 19th, 2006
Delivered Document
P Riyad Bank Executive Management DMD Best Practice
Design Presentation
September 6th, 2006
This Presentation
P DMD Standard Policies for Data Ownership, Data Quality,
Data Access & Data Definition Processes
September 9th, 2006
Upcoming Milestone
P Implementation Plan for Phase II
September 9th, 2006
Upcoming Milestone

Strategic issues – two problems, not one
•  Data problem
–  Accurate systematic capture, integration, distribution, storage of
granular data items
•  Information problem
–  The common and pervasive definition, understanding, agreement of
business rules enabling consistent interpretation of data
•  These issues are linked
•  Both need to be addressed by the Bank
•  The Information problem in particular is a business issue, not I.T. issue

Key Recommendations
•  RECOMMENDATION 1: Confirm the IM Vision
–  Share, debate & refine the vision at Exec Level
–  Syndicate vision across the bank management & staff
•  RECOMMENDATION 2: Expand “Data Management Dept” & Position
as “Information Management Dept”
–  Agree IMD Charter
–  Confirm IMD organisational structure
–  Locate IMD within IM Governance and Bank Organisation
–  Confirm organisational relationships with customers & other departments
•  RECOMMENDATION 3: IM Transformation Programme
–  Set targets & formulate according to best practice
–  Plan Programme
–  Establish Programme & Project Governance
–  Release resources to the programme

Bank IM Vision
Best
Prac7ce

Informa7on

Management

Info
at
the
heart

of
the

business

Single
trusted

View
of

informa-on

Self-‐service

approach
for

Business
Info

Access

An
IM
Func-on

that
challenges

the
Business

Build
the
future

while
suppor-ng

the
present

Perf.
Mgnt.

suppor-ng

Business

Strategy

Common

Informa-on

Model

Business

data
analysis,

not
data

collec-on

Bank IM Charter
•  VISION: To be the information core competency centre and champion
within the Bank, promoting the effective use of high quality information
and data to maximise business value.
•  DUTIES: Within the context of the Bank’s overall business Vision,
Strategy and Operations, the IMD is responsible and accountable for:
–  Definition and assurance of Information Management Policy
–  Enterprise steward - looking after Enterprise Information Model & Metadata
repository
–  Business Intelligence centre of excellence
–  Information Management Services

Implement
Branch/CSR

Data
Capture
Improvements

Review
Branch/CSR

Data
Capture

Ini-al
cycles
of
Data
Cleansing
&
DQ
Improvement

IMD
Communica-ons

Ongoing
coaching
&
skills
transfer

Appoint/recruit
ini-al
IMD
resources

Speciﬁc
IM
Skills
Training

Formalise

IM
key
docs

Implementation Plan
Month 1 Month 3 Month 4 Month 5 Month 6 Month 7Month 2
Implement
IMD
Capability
Mobilise
IM
Governance

Bank
procedural
changes

WRT
IMD
Impacts

IMD
Quick
Wins
IM
Training

(IT
Bas)

Implement
Data
Masking

Data
Classiﬁca-on
Project

IMD

Setup

&

Mobilise

Design
Data
Masking

“Data quality is like a public
toilet. We all want to use it,
but nobody wants to clean
it.”
Vice President of Retail Banking

PART 3. What techniques and
capabilities do we need to focus on?
Sponsored by Carl Sagan
“I try not to think with my
gut. If I‘m serious about
understanding the world,
thinking with anything
besides my brain, as
tempting as that might be,
is likely to get me into
trouble.”

Information Asset Management
Owners
Asset
Management
Tools
Governance
Admin
Experts
User
Community
Information
Asset
Steward
OwnersOwners
The “Information Asset Community”
Directives
WMSAIRS
Example High Level Data
Systems & Flows
Version 1.0
General
Mandatory
Core
Corporate Support
Automatic One-way Relationship
Automatic Two-way Relationship
Manual One-way Relationship
Manual Two-way Relationship
External
External System
AIRS
Interchange
AUSSAR
AME
Cyber Exams
ATSL
CLIC
SDR
Publishing
System
GMEL
TRIM AD
FRLI
Web Control
Mgmt
System
(WCMS)
FMIS
STI
ESIR
Comweb
ATO
Business
Portal
Inventory
Mgmt
System (IMS)
EPK
ComBIZ
Online
ProMaster
FCAT
Calumo
CBMS
(DoFD)
COMCARE
Thomas
Logistics
SM7
HRMS
HRFlex
DTAR/OTAR
APEX
AOD Audit
AOD Case
Mgmt
System
Timelog
eRooms
Symbion
Health
System
API Upload
System
ChangePoint
Testing
System
MRS
ASSP
AWS
AFD
ASIR
ADMS
Tracker
ATOG Job
Register
AOC
Surveys
Industry Payments
Compensation Payments
Financial Actuals
Financial Actuals
Employee
Expenses /
Adjustment
Journals
Salary Payments
Cash Payments
Payroll (Salary)
Cash Payments / Organisation Info
Human Resources Finance
Surveillance/Audit/
Reporting/Tracking
Workflow and Online Collaboration/
Service Delivery
Service Delivery
Service Delivery
Service Delivery, HR & Finance, Agreements, Permissions, Aerodromes,
Participants, Aircraft
Medical Examinations
Medical Exams
Surveillance /Audits/
Reporting/ Tracking
Alcohol and Other Drugs
Surveys /
Surveillance
Events/Occurrences, Aircraft,
Aerodromes
Surveys/
Certifications
Examinations
Work Orders
Surveillance /Audits
Aerodromes
Aircraft
Events/Occurrences,
Aircraft, Aerodromes
Defects/Events/Occurrences, Aircraft,
Aerodromes
Exemptions
Database
Alternative
Means of
Compliance
(AMOC)
Exemptions
AMOC / Exemptions
Human
Resources – Flex
Time
Human Resources - Travel
Physical Inventory
Audit Data
workflow / service delivery
workflow / service
delivery
Contacts – Ind, Org’s Contacts – Ind, Org’s
Examinations
Medical
Examinations
Search and Rescue
Surveys
Human Resources – Time
Aircraft Equipment Finances
MMEL
Baseline/Minimal Equipment
Medical
PAWS
Retain
Details of
Operators
Incidents
Applications / Permissions
Trending
Workflow
MAAT
Permissions / Change of
Status
Permissions / Change
of Status
Service Log
Alternative Means of Compliance
(AMOC)
Dangerous
Goods
Dangerous Goods
Content
Inventory
FTTO
FTNS
Individual Flight Data
Organisational
Flight Data
Human Resources – Time
Aircraft
Individuals/ARNS
Payments
HR - TimeCash Receipts
Reconcile
Invoice against
Flown Hours
Surveys / Surveillance
Enterprise Data
Warehouse
CASA
Internet
Airports
Landings/ Take Offs
Data
Mandatory
Core
Corporate Support
External
Business Process
Surveillance/
Audit/
Reporting/
Tracking
Bank Data
File
PAYG
payments, Salary
payments, and
Superannuation
payments.
External
Superannuation
Companies
Cash Payments
Superannuation Contributions
Suppliers
Remittance
Advice
300+ Access
Databases
Contacts
Airspace
Organisational
Human Resources
Aircraft
Permissions
Info Asset Register
(inventory)
System Interfaces map
“Science is organized
knowledge. Wisdom is
organized life.”

Analytics & Business Intelligence
37

“The alchemists in their
search for gold discovered
many other things of
greater value.”
•  “Traditional” BI (reporting & ad-hoc analysis)
•  Data Mining
•  Statistical modelling
•  Data visualisation
•  Textual analytics
•  What questions do we want to answer?
•  What questions can we answer with the data
we’ve got?
•  What other data would we need?
•  What does the data tell us we should be
asking?

“Big Data” is a fact of life
•  Three, four, five, six “Vees”?!
•  A lot of data (Tb/day)
•  Streaming data (monitoring, flow-of-control and
alerting analytics)
•  Inference from semi-structured data (Twitter,
Facebook)
•  Synthesise insight from millions of pages of text
•  Programmatic analysis for specific scenarios (hard in
SQL)
•  A disruptive catalyst to put information at the top of
the organisational agenda
•  Not just about the data! Business scenarios are key
•  Beware the Vendors!

All of the data, all of the time
•  Granular, forensic history
•  Modern data management & analytics solutions can make “all
of the data, all of the time” a reality
•  The bigger challenge is that the business community is not
analytically skilled enough to navigate the data and draw
meaning from it…

Get on the Cloud
40

… but security, privacy considerations are heightened.
In principle, it’s just another place to store data….

Compliance & statutory considerations
•  Freedom of Information Act 1982 (Cth)
•  Freedom of Information Amendment (Reform) Act 2010
(Cth)
•  Privacy Act 1988 (Cth)
•  Privacy Amendment (Private Sector) Act 2000
•  Privacy Amendment Act 2012 (Cth)
•  Privacy Amendments (Privacy Alerts) Bill 2013 (Cth)
•  State Records Act 1998 (NSW)
•  Government Information (Public Access) Act 2009 (NSW)
•  Privacy & Personal Information Protection Act 1998
(NSW)
•  Health Records & Information Privacy Act 2002 (NSW)
•  NSW Government Guide To Labelling Sensitive
Information 2011 (NSW Financial & Services)
41

But is “compliance” a motivator?
“All I want is compliance with
my wishes, after reasonable
discussion.”

Collaboration Culture
42

•  A general willingness to share
information
•  Co-operative, communicative &
collegiate OR control, coercion
& criticism?
•  The “whose data is this?” cue
•  Call-to-action?
•  Accountability & measurement?
“Respond intelligently even to
unintelligent treatment.”

Data Models & Metadata Management
Metadata
Repository
Master Data Repositories UNSW Core Systems
Information
Asset
Register
Physical Instantiations
Physical Layer
Logical Layer (Transition)
Analytical
DB Models
Cubes
Conceptual Layer (Business)
Physical
Messages
Formats
DWH
DB
HR
DB
Student
Admin
etc...
Operational
DB Models
Reference models
Data Subject Areas
Data Entities
Data Attributes
Information
Concepts
Business Content Business Rules Data
Business Data
Element
Domain Values
Endorsed Standards
for Content
Business Constraints
Business Measures
Master data models
Classification Entity
Hierarchies
Mappings
Business Rules
Definitions
Business Constraints
Business Measures
Core
SystemsMDM
MetadataManagementProcess
InformationModelManagementProcess
InformationAlliances:DataOwnership&StewardshipProcess
MDM Processes
Related Data
Governance Processes
Application
Logical Data
Models
Logical
Message
Schemas
MDM Data
Model
Systems Data
Models
SOA/EP
MessagesG/L
Application
Logical Data
Models
Logical
Message
Schemas
Analytical
DB Models
Cubes
Physical
Messages
Formats
Operational
DB Models
Business Glossary
Conceptual Model:
Groupings & Relationships
Data Elements, Definitions,
Aliases, and Security
Data Domains
Enterprise Information Model
“Do not quench your
inspiration and your
imagination; do not
become the slave of your
model.”

Classification schemes and taxonomies
•  Taxonomy: method for classification of things
•  Classification Scheme: grouping of kinds of
things, based on their characteristics
•  Information Model: representation of concepts,
relationships and semantics
•  For an Enterprise approach, each should relate to
the other in an ordered manner

Information
Disconnect
Careless data entry
& lack of validation
Teams use different
IT systems
?
Organisations
change rapidly
Teams have different ways
of reporting data
Month
Region
Multiple codes exist
for the same thing
IC_STR
Data is in different
Formats
Overlapping subsets
in different places
Multiple, inconsistent
master data
Data Quality
“Get your facts first,
then you can distort
them as you please.”

Accurate vs Inaccurate Data
Right
Representation
Wrong
Representation
Right Value
Wrong
Value
Valid Values
Invalid
Values
Missing
Values
Accurate data Inaccurate data
“Valid” does not equal “Correct”

Accuracy
Completeness
Consistency
Integrity
Validity
Origins
Compliance
Storage
Retention
Transmission
Distribution
Ownership
Use
Security
Performance
Uniqueness
Accessibility
Flexibility
Timeliness
Inherent
Pragmatic
Data Quality Dimensions
•  DQ Dimensions are the
characteristics against which we
measure quality.
•  May be categorised into two
types:
–  Inherent Quality
–  Pragmatic Quality

Ongoing&BAU&opera.ons&for&Data&Governance&&&
Enterprise&Informa.on&Management
Review&and&refine&Opera.ng&Model,&Processes,&Standards
KPT1305IData&
Governance&
Opera.ng&Model
Accredita)ons-Simplifica)on-for-ASB
QW007
Pilot-Info-Alliance-7-Student-Lifecycle
ORG007
DG&Founda.ons DG&Enablement DG&Embedding Complete&Rollout
KPT1304&I&Consistency&of&
Data&Processing&Methods
Info-Asset-Management-Process
PROC005
Data-Owner-
Role
PEOP001
Data-Interfaces-F/work-
(cf-NextGen)
PROC003
Confirm-DG-
Framework
STRAT007
Phase&1&
Data&
Governance&
Culture
Phase&2& Phase&3& Phase&4&
UNSW&Data&Governance&Strategic&Roadmap&2013/14
Data&
Governance&
Policy&&&
Standards
Data&
Governance&
Processes
Data&
Governance&&&
Informa.on&
Management&
Systems
Data&
Governance&
People&&&Skills
Data&
Governance&
Organisa.on
DG&Strategy
Endorse-DG-Charter-
(Vision-&-Principles)
STRAT002
Other&Related&
Projects
Confirm-the-
DG-Strategy
STRAT005
DG-"Cheat7
Sheet"
CUL002
DG-Org-Model
ORG003
Define-Target-
State-IMCC
ORG005
Pilot-Info-Alliance-7-Staff/HR
ORG006
Iden)fy-Data-Owners-&-
Stewards
PEOP003
Core-DG-Policy
POL001
DG-Standards-
Framework
POL002
ToRs-for-Info-
Alliances
ORG002
Align-EDW-Project-
with-DG-Principles
SYS004
Define-DG-
Strategy-for-2015+
STRAT015
Enterprise-Info-
Environment-Ref.-Model
SYS008
General-Ledger-Simplifica)onQW004
Data-Steward-
Role
PEOP002
DG-Communica)ons-Plan-&-Stakeholder-Mapping
CUL003
KPT1406&I&Improved&
repor.ng&to&Government&
Agencies&
KPT1404&I&Defensible&
Submissions
KPT1402&I&Op.mise&
ASB&Accredita.ons&
Process
Pilot-Info-Alliance-7-Space-Assets
ORG008
KPT1301IConfirm&DG&
Scope&&&Priori.es
KPT1405&I&Improved&
modelling&&&Forecas.ng
KPT1407&I&
Traceable&
integrity&of&Data
KPT1408&I&Streamlined&Cost&Accoun.ng
KPT1409&I&Targeted&
Student&Cohorts
KPT1401&I&Space&Op.misa.on
KPT1403IIMCC&
Opera.onal
Archibus-FM-Solu)on
QW006
Instan)ate-IMCC-approach-7-New/Gap-capabiil)es
ORG010
Metadata-Management-Process
PROC014
DQ-Dashboard-&-Repor)ng
PROC002
Enabling-DG-
Knowledge-Resources
POL003
Implement-Metadata/
Glossary-Tools
SYS010
DQ-Profiling-&-Remedia)on-
Environment
SYS009
Implement-IAM-Tool
SYS002 SYS003
Enterprise-Data-Warehouse-(EDW)-Phase-1
QW005
Organisa)on-Structure-Mapping-Project
QW002
Evaluate-DQ-Logging-
Tool-op)ons
SYS006
Instan)ate-IMCC-approach-7-exis)ng-capabili)es
ORG009
Research-Data-Storage-Ini)a)ve
QW003
Data-Cleanup-for-Staff-data
QW001
Select-Metadata/
Glossary-Tools
SYS007
Evaluate-IAM-Tool-
op)ons
SYS001
Data-Governance-Lifecycle-&-Checkpoints
PROC010
Collate-ini)al-Enterprise-Informa)on-Model
PROC008
DQ-Management-Process
PROC004
DQ-Log
PROC001
EDW-Delivery-Methodology
PROC011
EDW-Design-Pa_erns
POL004
DG-Standards-&-Guidelines
POL005
DG-Induc)on-Training-(Owners-&-Stewards)PEOP004
Ini)al-Info-Asset-Audit
PROC007
Enterprise-Informa)on-Model-as-a-Control
PROC009
Data-Governance-Lifecycle-within-SLDC
PROC013
KPT1303IConsistent&
Data&Defini.ons
KPT1302&I&Op.mised&data&
interfaces&delivery&for&NextGen
Strategic Planning & Benchmarking
“One day Alice came to a fork in the
road and saw a Cheshire cat in a
tree. Which road do I take? she
asked. Where do you want to go?
was his response. I don't know, Alice
answered. Then, said the cat, it
doesn't matter.”

Summary: Data Governance increases
understanding, utility & value of information
Information System
Data Quality Management
(Profiling, root-case analysis,
issues tracking & resolution)
Data Modelling
(Consistent, inter-operable data
structures & semantic meaning)
Information Requirements &
Business Analysis
(Identification & traceability of
business definitions & rules)
Information Asset Register
(Catalogue of data holdings)
Information System
Information System
Information System(s)
Data Set

Applying the Telemanagement
Forum solution models,
Sponsored by Mae West
“I’m no model lady. A
model’s just an imitation
of the real thing.”
PART 3: Case Study

B2B Telco Provider (UK): the problem
•  Poor lead-times for provisioning new orders
•  Orders fulfilled incorrectly
•  High levels of customer credits
Caused by:
•  Multiple business systems
•  Limited levels of integration
•  Silo operations

“Order to Bill” solution approach
•  Unified business processes
•  Enterprise Service Bus for integration
•  Data warehouse & BI (visibility & monitoring)
•  Needed an Enterprise Information Model –
quickly!
– IBM model was expensive!
– Company were already members of TM-Forum…

TMForum: communications industry trade
association
www.tmforum.org

TMForum standard models

TMForum SID (Standard Information
Definition)

Mapping SID to key process groups

Mapping SOA APIs using the cannonical
model
Enterprise
Service
Bus
(ESB)

Order
Management

System

Business
Process

Framework

Supplier
Management

System

Service
Ac-va-on
Fault
Management
Ra-ng/Billing

Network
opera-ons
&
monitoring

EDW

Data schema (example)

Monitoring outputs

Outcomes
•  Average order times down from 3 weeks to 4
days
•  Significant reduction in cancelled orders
•  Approx 75% reduction in customer credits
•  Project was originally to take 6 months; it took
over a year…

PART 4. What skills and personal
attributes does a Data Governance
Manager need? Sponsored by Mark
Twain
“To succeed in life,
you need two things:
ignorance and
confidence.”

Forethought
•  Think about both current and future demand
–  Cf. Google philosophy to “keep everything”
–  Every click, every font change…
•  Imagination, innovation, entrepreneurialism
•  Don’t be inhibited by the current scope of existing data
“Forethought we may have,
undoubtedly, but not
foresight.”
Source
new
data;

Collec-on
&

Integra-on;

Prepara-on
&

Quality.

Demand-‐oriented

Inbound
requests
for

speciﬁc
requirements

Data
Factory
(“push”)
Product-
based
delivery
(“pull”)
Need both “push” and
“pull” modes for evidence-
based decision-making

Expectations Management
•  Finding data that makes an impact
•  Having data for the problem at hand
•  Trusting the data to guide your
decision
•  Justifying pre-determined answers
•  Setting inappropriate goals
•  Not having the right data tools
•  Not thinking about value
“Two things are infinite.
The universe and human
stupidity. …and I’m not so
sure about the universe.”

“That propaganda that is good
which leads to success, and
that is bad which fails to
achieve the desired result. It’s
not propaganda’s task to be
intelligent, it’s task is to lead to
success.”
Communication
•  Listening skills
– e.g. active listening
•  Facilitation
•  Consulting & advisory
•  Coaching & mentoring

“Whosoever desires constant
success must change his
conduct with the times.”
Change management

Summary: Personal attributes
66

http://www.informationaction.blogspot.com.au/2013/10/normal-0-false-false-false-en-au-ja-x_29.html
Data
Owner

Data
Steward

PART 5. Conclusions & Final Thoughts
Sponsored by Terry Pratchett
“It’s still magic even if
you know how it’s
done.”

Conclusions: Information Excellence
EIM Framework:
Enterprise Information Management Framework describes each aspect of an organisations
information management state, provides a baseline of maturity against best practice and a
framework of business transformation to your aspirational information management state.
Provides linkage and balance between business,/IT, and human/technical aspects of EIM.
Information
Governance
Information Security
Information Asset
Mgmt
Metadata
Ownership &
Stewardship
Information and IM
Strategy and
Planning
Information and IM
Quality Mgmt
Information Asset
Classification
Intellectual Property
Reporting design Analytics
Information
Security Policy
and Governance
Asset
Management
Human Resources
Security
Management
Knowledge Transfer
Data Mining
Data WarehousingBusiness Intelligence
Information IM
Workforce
Management
Information and IM
Risk Management
Registration
Data Modelling
Data management
Data Integration
Data Cleansing
Data Capture
Data Migration
Data De-duplication
Record Keeping
Knowledge Management
Information Asset Access and Use
Management
Privacy Publishing
Copyright
Physical and
Environmental
Management
Communications
and Operations
Management
Information
Security Incident
Management
Access
Management
Information
system
acquisition,
development and
maintenance
management
Compliance
Management
Information and IM
Policy, Principles
and Architecture
Information and IM
Governance
Processes
Meta Knowledge
Search and Discovery
ExchangePricing
Licensing and
Rights
Management
Assess and
Accessibility
Redress Mechanisms
Data Quality and
Integrity
Data Conversion
& Transformation
Record Management Archiving Conservation and
Preservation
Record Creation
and Capture
Digital Continuity
Collection Management
Retrieval and Access
Retention and Disposal
Business
Continuity
Enterprise
Informa7on
Model
IM
Solu7ons
and
Technology
IM
Policies

Organisa7on
and
People
Data
Governance
Informa7on
Culture

IM
Processes

Business Processes
DB Models
Definitions, Derivations, Decision Rules, Execution Rules
IM Governance Process
IM Stewardship Process
Technical MetaData Management
Logical Model
ETL Specs
Report
Definitions
Semantic Specs
Data
Marts
ETL Cubes
Semantic
Layer
StandardReportLibrary
ETLOperational
System
Staging Warehouse
Conceptual Model
Logical Model
Physical Model
Capture & Formalise
Requirements
& Rules
Impact
Assessment
& Implementation
Metadata
Lineage
Impact
Etc.
Metadata
Collection
Asset Alignment/Mgt
Architecture Changes
Architecture
Mgt
A holistic, data-centric approach to Information Management & Data Governance,
addressing both human and technical factors in both Business and IT domains

Recap: Seven Transformational Levers
•  Strategy (are the vision, objectives and overall direction for the
organisation clearly articulated and well understood? Do Business
Strategy, IT Strategy and Information Strategy align?)
•  Culture (is the desired behaviour exhibited throughout the organisation?)
•  Organisation (are the organisational structures appropriate to executing
the Strategy?)
•  People (is the workforce properly skilled and motivated?)
•  Process (do all business processes align with and support the Strategy?)
•  Policy (are the organisational controls appropriately defined and applied?)
•  Systems (does the infrastructure of IT Systems provide the right support
for all key business processes?)

Calls to action
•  WHAT: Identify 3 key issues (data related) that need
to be addressed within your business, and the IM
capability areas that support these
•  WHY: Outline the business outcomes/benefits that
you would derive from addressing these issues
•  HOW: Map these changes to the Seven
Transformational Levers of the Enterprise IM
Framework
See example worksheet

Decision Request / Action Plan - 6-step method
1.  REQUIREMENT: State one key issue (data related) from the strategic list. What is
needed?
2.  PROBLEM/INHIBITOR: what is currently preventing your organisation from doing
something about it?
3.  OUTCOME: What specific benefits would your organisation derive if this was
addressed?
4.  SOLUTION: What new product or capability is needed to deliver the requirement
stated in (1)?
5.  PLAN: Outline the step-by-step action plan & timescales that will deliver the
outcome.
6.  DECISION REQUEST: In THREE bullet points, state what specific support you
need from your Sponsor in order to get things started
–  “I need you to agree to… 1, 2, 3.”
–  e.g. budget, resources, new policy, key communication
See example worksheet

FINAL THOUGHTS: perspectives on
Information Management
Applica-ons/Systems
Architecture

(How
do
I
access
it?)

Data
Architecture

(Conceptual,
Logical,
Physical
–
What
does
it
mean?)

Informa-on
Asset
Catalogue

(What
have
we
got?
In
what
context?
For
whom?)

Info
Management
&
Data
Governance
Roadmap

(When
will
it
be
delivered?)

IM
Business

Case

(Why
do
we

want
it?
How

much
will
it

cost?)

Business
Process
Models

(How
do
we
do
it?)

Business
Services
Framework

(What
do
we
do?)

Business
Lifecycle

(Why
do
we
do
it?)

Business
IM

Capability
&

Transforma7on

(Who
is

accountable?)

IM

Capability

Assessment

(What
do
we
want/
need?)

IM
Service

Capability
/

IMCC

(Who
delivers
&

supports
it?)

Blog links…
1.  RETHINKING OUR STRATEGIES: http://
informationaction.blogspot.com.au/2012/07/information-as-service-what-
is-it-and.html
2.  IS EIM ACHIEVABLE?: http://informationaction.blogspot.com.au/2013/11/
to-centralise-or-not-to-centralise-that.html
3.  CAPABILITIES: http://informationaction.blogspot.com.au/p/the-
information-management-tube-map.html
4.  SKILLS & QUALITIES: http://informationaction.blogspot.com.au/2013/10/
normal-0-false-false-false-en-au-ja-x_29.html
5.  MAGIC: http://informationaction.blogspot.com.au/2014/03/now-thats-
magic.htm
73

Intellectual curiosity
Skeptical scrutiny
Critical thinking
http://www.informationaction.blogspot.com.au/
@Alan_D_Duncan
http://www.linkedin.com/in/alandduncan

Managing for Effective Data Governance: workshop for DQ Asia Pacific Congress March 2014

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