How to get your MDM program up & running” This session will deliver a Master Data Management primer to introduce: Master vs Reference data Multi vs Single domain MDM solutions A MDM reference architecture and MDM implementation architectures This will be illustrated with a real world example from describing how to identify & justify the appropriate data subjects areas that are right for mastering and how to align an MDM initiative with in-flight business initiatives and make the business case.

1. Master Data Management Webinar
H O W T O G E T Y O U R M D M P R O G R A M U P & R U N N I N G

2. Introduction:
My blog: Information Management, Life & Petrol
http://infomanagementlifeandpetrol.blogspot.com
@InfoRacer
uk.linkedin.com/in/christophermichaelbradley/
Christopher Bradley
Chief Data Officer
FROMHEREON

4. Introduction To Chris Bradley
Chris is the Global Chief Data Officer of FromHereOn (FHO).
FromHereOn is an Enterprise Design consultancy. We help leaders
of global organisations discover and develop a vision for
meaningful change. Then we partner on the journey to make it
real.
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).

5. Recent Presentations
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”

6. 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. chris.bradley@fromhereon.com
@inforacer
uk.linkedin.com/in/christophermichaelbradley/
+44 7808 038 173
infomanagementlifeandpetrol.blogspot.com
Chris Bradley
Chief Information Architect

9. E N T E R P R I S E D E S I G N
We call this new capability Enterprise Design, by combining design
thinking with the human value of information and the scale of
enterprise architecture, we take a people centered approach to
transform large organizations and build better consistency,
efficiency, simplicity and experiences.
This approach ensures people and their motivation to realize a vision
worthy of their passion remain at the heart of major change
initiatives, from concept right through to delivery.
Uniquely FHO combines Service Design, Information Management
and Enterprise Architecture capabilities to enable transformation
end-to-end, from strategy development, roadmap design, costing
& estimation, business case development, through to program
management and project level architecture and design services.

10. Enterprise Design
B U S I N E S S T R A N S F O R M A T I O N
Service Design
HUMAN-CENTERED
With a people-oriented design approach we are able to better
connect individuals with what needs to be achieved for customers
and how their role contributes to make it happen.
Enterprise Architecture
ENTERPRISE-SCALE
By taking a whole-of-enterprise view we are able to overcome issues
of complexity and bridge the implementation gap between
strategic planning and what really happens on the ground.
Information Management
INFORMATION-ENABLED
Simplify the language of the business, collaborate more seamlessly
at an enterprise-scale, invest in data as an asset and enable more
personalized customer experiences.
BUSINESS-LED
HUMAN-CENTERED
ENTERPRISE-SCALE
INFORMATION-ENABLED

12. Reference & Master
Data Management

13. 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
› ImprovementReference &
Master Data
Management
C O N T E X T ( D M B O K )

14. What is Event / Transaction Data?
“Bob bought a Mars bar from Morrison's on Monday 3rd Jan at 4pm and paid using cash.”
E V E N T D A T A E X A M P L E :
WHO WHAT WHERE WHEN HOW QUANTITY AMOUNT
Bob Bobson Mars bar Morrison's, Bath 16:00 Monday
3rd January 2011
Cash 1 £0.60
CUSTOMER
CODE
PRODUCT
CODE
VENDOR
CODE
DATE PAYMENT
METHOD
QUANTITY AMOUNT
BB005 CONF101 WMBATH 2011-01-03 16:00 CASH 1 £0.60
Terminology
FIELD (or attribute): column in a database table
RECORD: row in a database table

15. About Event Data
AKA Transaction data
Describes an action (a verb)
E.g. “buy”
May include measurements about
the action:
› Quantity bought
› Amount paid
Does not include information:
describing the nouns:
› Bob Bobson is male, aged 25
and works for British Airways
› Monday 3rd Jan 2011 is a bank
holiday
› The address of Morrisons Bath
is: York Place, London Road,
Bath, BA1 6AE.
Includes information:
identifying the nouns that were
involved in the event
(the Who / What / Where / When
/ How and maybe even the Why):
› Bob Bobson
› Mars bar
› Morrisons, Bath
› 16:00 Monday 3rd Jan 2011
› Cash

16. What is…
MASTER DATA?
› Defines and describes the nouns
(things) of the business.
e.g. Field, Well, Rig, Product, Store,
Theraputic Area, Adverse Event, etc.
› Data about the “things” that will
participate in events.
› Provides contextual information about
events / transactions.
› Stored in many systems
› Packaged Systems
› Line of Business Systems
› Spreadsheets
› SharePoint Lists
MASTER DATA MANAGEMENT
(MDM)?
› The ongoing reconciliation and
maintenance of master data.
› Control over master data values to
enable consistent, shared, contextual
use across systems, of the most
accurate, timely, and relevant version
of truth about essential business
entities.
[DAMA, the Data Management
Association]
MASTER DATA MANAGEMENT
(MDM)?
› Comprises a set of processes and
tools that consistently defines and
manages the non-transactional data
entities of an organisation.
[Wikipedia]

17. Master Data – What’s the problem?
No organisation has just one system
(unless the are tiny)
Details about the same noun are found in
multiple systems, e.g. Customer
Problems
Data may need to be rekeyed in each system
Systems may not be in synch (new records, updated
records)
Duplicate data: are “ABC Ltd” and “ABC Limited” the
same thing?
No single version of the truth
Reporting / Analysis: difficult to combine data from
multiple systems
The same customers may be defined in:
• Finance systems
• Marketing systems
• Line of business systems
SOLUTION:
Master Data Management!

18. MDM Architectures

19. Standard “Hub” architectures
1. REPOSITORY
2. REGISTRY
3. HYBRID
4. VIRTUALISED
*A key difference is the
number of fields that are
stored centrally

20. Example: Customer
Customer
code
First
name
Last
name
Date of birth Preferred delivery
address line 1
Preferred
delivery address
post code
Credit
rating
Occupation Car
BB005 Bob Bobson 1985-12-25 Royal Crescent BA1 7LA A Information
Architect
Audi R8
I D E N T I F I E R S
C O R E F I E L D S
A L L F I E L D S
ALL FIELDS
Repository
CORE
FIELDS
Hybrid
IDENTIFIERS
Registry
NONE
Virtualised

21. Select your MDM Architecture
and Toolset carefully
A physical hub is not the
“only” way...

22. Select your MDM
Architecture and
Toolset carefully
A HUB IS NOT THE ONLY WAY...

25. Typical MDM Capabilities
DATA INTEGRATION
& ACQUISITIONDataDiscovery&Intake
Consolidation
DataQuality
MASTERDATASERVICES
ACCESSCONTROL
DATA DELIVERY
UserInterface
Application
Integration
Business
Services
MASTER DATA “HUB”
MDM Architecture
Master Data Models
Reference Data
Metadata Management
Business Rules
Hierarchy Management
Master Data “Repository”
D A T A S Y N C H R O N I S A T I O N
O P E R A T I O N A L M A N A G E M E N T / D A T A G O V E R N A N C E & S T E W A R D S H I P

26. MDM…
A Hub is not the only way
MASTER DATA CONTRIBUTORS
ERP CRM LEGACY
MIDDLEWARE SYNCHRONISATION LAYER
Synchronized Master
ERP CRM LEGACY
EXTRACT-TRANSFORM-LOAD
SYNCHRONISATION LAYER
Hub Based Master
OPERATIONAL
DATA-STORE
E.G. CUSTOMER
MASTER
Operational Hub structure that overlaps operational and
analytical environments
Supports concept of an Enterprise Data Warehouse
Multiple systems acting as data providers
Appropriate for low data latency and velocity operations
Requires careful data quality management
Multiple operational systems acting as master data
contributors
Real-time information availability
Well suited to enterprises where data is stored across
multiple source systems
Well suited to low data velocity operations

27. MDM…
A Hub is not the only way
SINGLE DATA
SOURCE
ERP CRM LEGACY
SYNCHRONISATION LAYER
Application Specific Master
ERP CRM LEGACY
SYNCHRONISATION LAYER
Master Overlay
INDUSTRY
SPECIFIC
MASTER
Stand-alone, application-neutral master data
Industry-specific data model
Well suited to vertical industries in aligning front-office &
back-office systems in real time
One operational system as master data provider
Well suited to enterprises where data is primarily stored
in a single source system
Support from many enterprise vendors

28. MDM…
A Hub is not the only way
Non
SAP
CRM
LEGACY
Real Time Data Movement
SAP 2
SAP 3
SAP 1
DW
DSL
REPORTS
MESSAGING
Data Virtualization (Federated) Master Data
DV (FEDERATED) MASTER DATA
MASTER
DATA
DATABASES
ORACLE
SAP
SIEBEL
PACKAGES
…..
…..
…..
…..
…..
…..
FILES XML
Virtual MDM hub created via DV layer
DV layer informed from Logical Data Model
Powerful data integration to access multiple disparate systems
Rapid time to solution
Outstanding prototyping, proof of value approach
Compliments other Data Integration approaches
Data is moved between the various systems using a
messaging integration hub or ESB
Data movement is done in real time
Movement of data can be one or two way as required

29. Complete DV Master Data View
MDM applications alone frequently
cannot fully support all requirements as
data frequently exists outside of MDM hub
Complementary data integration solutions
are needed to deal with data maintained
outside of MDM hubs often in complex,
disparate data silos
DV can extend the Master Data and
provide a complete 360o view by using
master data from the hub as the foreign
key to quickly and easily federate master
data with additional transactional and
historical data to get a complete single
view of master data
Complete DV Master Data View
ORACLE
SAP
SIEBEL
…..
…..
…..
…..
…..
…..
ETL SERVER
MASTER
DATAMASTER
DATAMASTER
DATA
Existing
Master
Data Hub
COMPLETE 360° VIEW OF MASTER DATA

30. Single Domain & Multi Domain MDM
Eg Product (PIM), Customer (UCM), Vendor,
Laboratory (LIM)
Incorporate specific “domain” features …
› House holding
› Address chaining
› Company Hierarchies
› Tailored data matching (deterministic or
probabilistic)
Engineered to exploit 3rd party data sources
› GB PAF, USPS Zip+4, Electoral Roll, CCJ …
› D&B, Liquidations, Companies House, …
› CAS, COSHH, …
Interfaces to domain LoB apps
S I N G L E D O M A I N
Highly configurable MDM solutions
Informed by Data Model
Fewer specific data domain features
Interfaces to mainstream apps
Results in fewer MDM solutions throughout
the Enterprise
M U L T I D O M A I N

31. Reference vs Master Data
Reference Master
Number of Values Low, Fixed/Known Medium-High,
Variable/unknown
Volatility Stable, Low rate of change Medium-Frequent rate of
change
Source External (mostly); Standards
bodies
Internal (mostly)
Ease of Governance Easy Harder, Higher number of
stakeholders
Number of Attributes per data
area
Very low; typically code
type/value/description
High
Federation/ownership of
attributes per business area
None (all attributes) Much federation: e.g..
Business Area A masters
Attribute 1/Attribute 2,
Business Area B masters
Attributes 3/4/5
Tool Cost Low (ref data only) High (MDM tools often have
ref-data management also)

32. Aligning MDM with
Business Program
A L I G N M D M I N I T I A T I V E S , P R O C E S S E S A N D R E C O M M E N D A T I O N S F O R
T E C H N O L O G Y W I T H C O R P O R A T E A N D B U S I N E S S U N I T S T R A T E G Y

33. 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

34. MDM Approach:
Align With a "Lead" Business Project
Think Big & Execute Small
P H A S E S
EX P LORE EX ECUTE MA N A GE
EN TERP RISE IN FORMA TION MA N A GEMENT P LA N
MDM & DA TA
A SSET P LA N
P RIN CIP LES MA STER DA TA IMP LEMEN TATION

35. Master &
Reference
Data
Management
Data
Warehousing
Business
Intelligence
Transaction
Data
Management
Semi-
Structured
Data
Management
Content &
Document
Management
Data
Integration &
Interoperability
Data
Quality
Management
Information
Lifecycle
Management
Data
Governance
Data
Asset
Planning
EIM
Goals &
Principles
Big
Data
Analytics
Data Models
& Taxonomy's
Metadata
Management
Geospatial
Data
Management
Consider MDM within the context
of ALL Information Dimensions
(our EIM Framework)
Business-Driven Goals
Drive a Robust
Enterprise
Information
Management Practice
EIM goals and strategies are business-driven for the
entire enterprise, underpinned by guiding
principles supported by senior management
Proactive planning for the information lifecycle
including the acquisition, manipulation, access,
use, archiving & disposal of information
The identification of appropriate data
integration approach for business challenges
e.g. ETL, P2P, EII, Data Virtualisation or EAI.
The full lifecycle control and
management of information from
acquisition to retention & destruction
Organise information to align
with business & technical goals
using Enterprise, Conceptual,
Logical & Physical models
Roles, responsibilities, structures and
procedures to ensure that data
assets are under active stewardship
Processes, procedures and
policies to ensure data is fit
for purpose and monitored
Metadata capture,
management, & manipulation
to place data in business &
technical context
Manage diverse data sources across the
organisation from transaction data
management, to data warehousing and
business intelligence, to Big Data analytics

36. Statoil MDM challenges
Lack of Meta and Master data within and across
environments
Silo oriented/stand alone applications
Master Data Management established in different
processes e.g.:
› Exploration
› Marketing and Supply
› Supply Chain Management
› Finance and Control
› Human Resources
› Service Management
W H A T I S T H E “ M A S T E R D A T A ”
T O B E M A N A G E D ?
85% of SCM Service incidents
due to “master data” errors
FINANCE
Cost Centre
Account
Profit Centre
Wbs
Etc.
HUMAN RESOURCE
Employee
Organisation
Position
SERVICE
MANAGEMENT
Assignment Groups
Services
Etc.
SUPPLY CHAIN
MANAGEMENT
Material
Service
Vendor
MARKETING &
SUPPLY
Customer
Vendor
Product
SUBSURFACE
Licence
Well, field
Reservoir
Country
Etc.

37. Statoil MDM challenges
W H A T I S T H E “ M A S T E R D A T A ”
T O B E M A N A G E D ?
Location
Wells
Field
License
Location
Tag
VendorPipeline
Projects
Processes
Applications
Roles, position
Org. unit
Employee
Service
G E O G R A P H I C A L I N F O R M A T I O N ( G I S )
M E T A & M A S T E R D A T A
Subsurface
Information
BI / DW Data
Admin.
Information
Marketing &
Supply
Information
Operation &
Maintenance
Information
Project
Development
Information
C O L L A B O R A T I O N I N F O R M A T I O N
Structured
Unstructured

38. Statoil MDM challenges
Drilling and Wells (Well Master)
Global licence/lease information project
Supply chain management
Marketing, processing and renewable energy
MapIT project (LCI information)
Management system project
Environmental reporting project
Collaboration project
Enterprise content management project
W H A T B U S I N E S S P R O J E C T S A R E D R I V I N G
T H E N E E D F O R A C O R P O R A T E A P P R O A C H
T O M D M ?

39. How can we build a
business case for
Master Data Management?

40. Master Data Management Maturity
LEVEL 1 - INITIAL LEVEL 2 - REPEATABLE LEVEL 3 - DEFINED LEVEL 4 - MANAGED LEVEL 5 - OPTIMISED
Limited awareness of
MDM.
Master Data domains
have not been defined
across the enterprise.
Silo based approach to
data models (where
models exist) results in
multiple definitions of
potential master data
entities, such as
customer or product.
The impact of master
data issues gain
recognition within the
enterprise.
Limited scope for
effective management
of master data due to
lack of Data
Governance &
Ownership Model.
Project or department
based initiatives
attempt to understand
the enterprise's master
data.
No MDM strategy
defined.
Definition of MDM
strategy in progress.
Master data domains
identified.
Several domains are
targeted for delivering
master data to specific
applications or projects.
Differing software
products may be
adopted in these silos
for MDM.
Senior management
support for MDM grows.
Recognition of need to
develop or align with
Data Governance
model.
A complete MDM
strategy has been
defined and adopted.
MDM joined up with
data governance and
data quality initiatives.
Robust business rules
defined for master data
domains.
Data cleansing and
standardisation
performed in the MDM
environment.
Specific products
adopted for MDM.
Master data models
defined.
A full integrated MDM
environment exists and
has been adopted
across the enterprise for
all key master data
domains.
The MDM environment
controls access to
master data entities.
Many applications
access the MDM
environment through a
service layer.
Business users are fully
responsible for master
data.
AS-IS TO-BETRANSITION PLAN
INTERIM

41. 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

42. Statoil Enterprise Master Data Model

43. Why Produce
a Model?
_ The Business NEEDS a common
vocabulary for communication
_ Clarify terms, collect synonyms
(& homonyms)
_ Understand current situation WRT owners,
stewards (prepare to be horrified!)
_ XREF data concepts to current systems
_ It works best if you don’t talk about
“data modelling”

44. It’s NOT “The Field of
Dreams”
_ Statoil projects cannot afford to wait for
Master Data to be delivered
_ Subsets of the Master Data must be delivered “just in
time” as the Business projects need to use them
_ This is NOT “The Field Of Dreams”
_ This means aligning the MDM initiatives with the Business
Projects
In E&P in general, managing voluminous
complex data types involves compromise
and ‘creaming off.’
E&P data management is about
compromise and the art of the possible.

45. Understand Corporate Plan
Y E A R 3 Y E A R 4 Y E A R 5Y E A R 2Y E A R 1
SUBSEA HARMONISATION
PLANT INTEGRITY
SEISMIC INTERPRETATION
ADVANCED FIELD MANAGEMENT
PROGRAMME X
PROGRAMME Y

46. Catalog Current Initiatives
U S I N G T H E P R O J E C T P O R T F O L I O
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 willing to engage with
MDM initiative is this project?
Importance: How important to the
MDM initiative is this Data area?

47. 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.
Prioritise by multiple criteria
( W I L L I N G N E S S T O E N G A G E , F E A S I B I L I T Y ,
T I M E S C A L E S , I M P O R T A N C E )

48. Harmonise & Xref with Data Model

49. Prioritise by interest

50. 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

51. MDM Dashboards
›BATCH
ROR PENETRATION
ACTIVE BATCHES:
GLOBAL TARGET:
55,000
508,000
NEXT STEPS Poland manufacturing facility
2Q 2013 penetration 20%
XYZ Commercial go-live
3Q 2014 100% Complete
DEFINITION:
Complete
Subject to cross segment
review
ROR PROFILE


MDM Process
Data Modelling
Data
Governance
Data Quality
Management
MDM Service
Initial Conceptual
Data Model in place
Some Data
Stewardship in place
(tbc)
Profiling performed
on XYZ data and
ABC
Batch ID Service in
POC
Data Quality Criteria
Accuracy Coverage Currency
Data Owner
Data
Steward(s)
DG Process
MDM
Platform
Go Live Date
Mary Berry (R&D) John Smith &
ANO (R&D)
TBD
Manufacturing
Defined, still
subject to
agreement in
Manufacturing
SAP MDM April 2013
subject to
successful POC
in XYZ
SCOPE: Tracking of batch identifiers and genealogies for any type
of XYZ development and/or manufacturing batch of
interest to the R&D and Manufacturing organisations.
BUSINESS DRIVER: Projects need to confront the challenge of synching up
R&D and Manufacturing process and analytical
measurement data as movement of materials within the
company often results in batches being registered and
assigned a new Batch ID in multiple IT systems. To resolve
this involves many hours extracting data from R&D and
Manufacturing lab systems, linking common fields across
spreadsheets, and connecting multiple, duplicate Batch
ID#s.
Currently there is no central batch identification for
Biological substance batches, this primarily being recorded
in lab notebooks. This prevents easy traceability of
biological substances through a substance id back to their
original batch.
MDM PROJECT PROCESS

52. Conclusion

53. MATURITY OF MDM ESTABLISHED IN DIFFERENT PROCESSES
DATA GOVERNANCE MATURITY
INFORMATION SYSTEMS
MDM Challenges & Considerations
DETERMINING THE “MASTER DATA” TO BE MANAGED
› Not simply the “usual” suspects: Customer, Product,
Location, etc.
› MD frequently also covers Agreements, Patents,
Trade Marks, Licenses …
› How do you make the case for MDM?
LACK OF CONSISTENCY
› Definition & meaning (can a Supplier also be a
Customer?)
› Presence & level of metadata
› Silo oriented/stand alone applications
› Data Quality
› “The key to successful master data management is
management support for relinquishing local control
of shared data.” [DAMA]
› Service Management
› HR, Finance, etc…
› Master Data for most domains span the business
› Data Ownership & Stewardship
› Who has the wisdom of Solomon?
› Do multiple business systems require update to the master
data?
› What are the conflict resolution rules? (Did I mention Data
Governance)
› Is a decoupled Master Data Hub sufficient?
› Which MDM pattern is appropriate?

54. Conclusions
Don’t believe you can
“build it and they will come”
An MDM initiative needs to be just
ahead of the business projects &
deliver just in time aligned to the
business initiatives.
Data models for MDM
programs are essential
Extended with rich metadata
including Ownership, Source
systems, Transformations etc.
Beware the hype on MDM
technologies
A hub is NOT the only way.
Rarely will one MDM technology
be appropriate for ALL Data
Subject Areas.
Governance is vital
MDM initiatives cannot be successful
without addressing Data Governance
for that data subject area.

55. chris.bradley@fromhereon.com
@inforacer
uk.linkedin.com/in/christophermichaelbradley/
+44 7808 038 173
infomanagementlifeandpetrol.blogspot.com
Chris Bradley
Chief Data Officer

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