How to identify the correct Master Data subject areas & tooling for your MDM initiative

| MASTER DATA MANAGEMENT – STARTING YOUR MDM STRATEGY | ENTERPRISE ARCHITECTS © 201 41
How to identify the correct
Master Data subject areas &
tooling for your MDM initiative
MASTER DATA
MANAGEMENT
WORKSHOP
Presentedby:
CHRISTOPHER BRADLEY
ENTERPRISE ARCHITECTS

Introduction: who am I?
My blog: Information Management, Life & Petrol
http://infomanagementlifeandpetrol.blogspot.com
@InfoRacer
uk.linkedin.com/in/christophermichaelbradley/
CHRISTOPHER BRADLEY
Chief Information Architect and
Enterprise Services Director
ENTERPRISE ARCHITECTS

Introduction to Chris Bradley
As a columnist, blogger and frequent contributor to
industry publications, Chris is a recognised thought-leader
in Information Management.
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). See Chris’s
blog at “Information Management, Life & Petrol”
Chris is the Global Chief Information Architect and
European Enterprise Services Director of Enterprise
Architects, an International strategy & architecture
professional services firm. EA provides strategic
architecture delivery and support services to Fortune
500's globally. With 33 years of experience in the
Information Management field, Chris works with
leading organisations including Total, Barclays, RBS,
GSK, Disney, 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, a Fellow of the Chartered Institute of
Management Consulting (now IC) member of the MPO,
and SME Director of the DM Board.

chris.bradley@enterprisearchitects.com
@inforacer
+44 7808 038 173
infomanagementlifeandpetrol.blogspot.com


Chris Bradley
Chief Information Architect and
Enterprise Services Director

Recent Presentations
Information Management for E&P Dubai: (DMBoard) March 2014, Dubai, UAE “How
to kick start an MDM initiative”
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 of …” 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”
BeyeNETWORK Webinar: (CA/BeyeNETWORK), March 31st 2010, Webinar.
“Communicating With The Business Through High Level Data Models”

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”,; January 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

About
Enterprise
Architects
› Enterprise Architects (EA) is an
international professional services firm
providing strategy & architecture services.
› Our vision is to be the most respected
specialist architecture organisation
globally, setting new standards for
effectiveness in strategy execution.
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encompassing people, process and
technology dimensions, are designed to
support the strategic objectives of an
organisation.
› Our core value proposition is to help
clients unlock the value of their
investments using the principles of
architecture.
› In summary, we are experts in architecture
with a belief that architecture is important
and, when done well, will profoundly
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OVERVIEW

Contact

Reference &
Master Data
Management

Reference &
Master Data
Management:
Context (DMBoK)
Used with kind permission of DAMA-I
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

What is Event / Transaction Data?
WHO WHAT WHERE WHEN HOW QUANTITY AMOUNT
Bob Bobson Mars bar Morrisons, Bath 16:00 Monday 3rd January 2011 Cash 1 £0.60
Event data example:
› “Bob bought a Mars bar from Morrisons on Monday 3rd Jan at 4pm and paid using cash.”
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

About Event Data
› AKA Transaction data
› Describes an action (a verb):
» E.g. “buy”
› May include measurements
about the action:
» Quantity bought
» Amount paid
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
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.

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]

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

How Do MDM
Solutions
Work?

Master Data Environment
CONSUMING SYSTEMSSYSTEMS OF ENTRY
SYSTEM
SYSTEM
SYSTEM
SYSTEM
SYSTEM
SYSTEM
MASTER DATA ENVIRONMENT
DATA
SOURCING
SYSTEM OF
RECORD
DATA
DISTRIBUTION
META-DATA
CATALOGUE
USER INTERFACE
DATA STEWARDS
DATA OWNERS
DATA QUALITY
Standardise, De-duplicate,
Merge, Enrich etc
“Best version”
To highlight issues and
enable them to be fixed
To store data models and
meta data

Master Data Environment: methods of moving Data
SYSTEM
SYSTEM
SYSTEM
SYSTEM
SYSTEM
SYSTEM
DATA
SOURCING
SYSTEM OF
RECORD
DATA
DISTRIBUTION
META-DATA
CATALOGUE
USER INTERFACE
DATA STEWARDS
DATA OWNERS
DATA QUALITY
ACTIVE PUSH
Webservices
BATCH SCHEDULE
ETL job
EII/DV cache refresh
PULL ON DEMAND
Webservices
SQL queries
ACTIVE PUSH
Webservices
BATCH SCHEDULE
ETL job
EII/DV
PULL ON DEMAND
Webservices
SQL queries

MDM
Architectures

Three standard “Hub” architectures
1. Repository
2. Registry
3. Hybrid
*A key difference is the
number of fields that are
stored centrally

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
IDENTIFIERS
CORE FIELDS
ALL FIELDS
ALL FIELDS
REPOSITORY
CORE
FIELDS
HYBRID
IDENTIFIERS
REGISTRY

Master Data Environment:
Repository Architecture
SYSTEM
SYSTEM
SYSTEM
SYSTEM
SYSTEM
SYSTEM
DATA
SOURCING
SYSTEM OF
RECORD
DATA
DISTRIBUTION
META-DATA
CATALOGUE
USER INTERFACE
DATA STEWARDS
DATA OWNERS
DATA QUALITY
ACTIVE PUSH
Webservices
(CUD)
PULL ON DEMAND
Webservices
(R)
REPOSITORY
REPOSITORY
› Repository serves as the single source of the master data.
› Repository contains the only version of the master data.
› All applications use the data in the repository via services.
› No latency or synchronisation.

Registry Architecture
SYSTEM
SYSTEM
SYSTEM
SYSTEM
SYSTEM
SYSTEM
DATA
SOURCING
SYSTEM OF
RECORD
DATA
DISTRIBUTION
META-DATA
CATALOGUE
USER INTERFACE
DATA STEWARDS
DATA OWNERS
DATA QUALITY
BATCH SCHEDULE
ETL job
BATCH SCHEDULE
ETL job
PULL ON DEMAND
Webservices
SQL queries
REGISTRY
REGISTRY
› Registry only contains identifying attributes.
› Persistent values are still stored in the source systems.
› Registry serves as an index to source data locations.
› Some latency due to caching.
VIRTUAL REPOSITORY
› A virtual database built using Enterprise
Information Integration (EII) technology.
› Survivorship rules are applied to return
the master record.
VIRTUAL
REPOSITORY
?
?
?

SYSTEM
SYSTEM
SYSTEM
SYSTEM
SYSTEM
SYSTEM
DATA
SOURCING
SYSTEM OF
RECORD
DATA
DISTRIBUTION
META-DATA
CATALOGUE
USER INTERFACE
DATA STEWARDS
DATA OWNERS
DATA QUALITY
ACTIVE PUSH
Webservices
BATCH SCHEDULE
ETL job
PULL ON DEMAND
Webservices
SQL queries
ACTIVE PUSH
Webservices
BATCH SCHEDULE
ETL job
PULL ON DEMAND
Webservices
SQL queries
Hybrid Architecture
HYBRID
› Repository contains identifying information and core information.
› Application-specific data is retained only in the application database.
› Applications still manage the full set of data.
› Core information is published back to source systems.
› 2-way synchronisation – normally some latency.
REPOSITORY
?
?
?

Select your MDM
Architecture and
Toolset carefully
A hub is not the only way...

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 &
analytical environments
› Supports concept of an Enterprise Data Warehouse
› Multiple systems acting as data providers
› Appropriate for low data latency & 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

SINGLE DATA
SOURCE
ERP CRM LEGACY
Application Specific Master
ERP CRM LEGACY
Master Overlay
INDUSTRY
SPECIFIC
MASTER
› 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
› 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

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

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
Complete DV Master Data View
ORACLE
SAP
SIEBEL
…..
…..
…..
…..
…..
…..
ETL SERVER
MASTER
DATAMASTER
DATAMASTER
DATA
Existing
Master
Data Hub
COMPLETE 360° VIEW OF MASTER DATA
› 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

Single Domain & Multi Domain MDM
SINGLE DOMAIN
› 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
MULTI DOMAIN
› 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

Align MDM initiatives, processes and
recommendations for technology with
corporate and business unit strategy
Aligning MDM with
Business Program

6 Guiding Principles [DAMA]
Shared Reference and Master
Data belongs to the organisation,
not to a particular application or
department
Reference and Master Data
Management is an on-going
data quality improvement
program; its goals cannot be
achieved by one project alone
Business data stewards are the
authorities accountable for
controlling reference data values
Golden data values represent best
efforts at determining the most
accurate, current and relevant
data values for contextual use
• But they might still be found to be false!
• Apply matching rules with caution and
ensure changes are reversible
Replicate master data values only
from the database of record
Request, communicate and, in
some cases, approve changes to
reference data values before
implementation
1 2 3
4 5 6

MDM Approach:
Align With a "Lead" Business Project
EXPLORE EXECUTE MANAGE
LEAD PROJECT PLAN
DATA ASSET
PLAN
Phases
PRINCIPLES MASTER DATA METHOD

MDM Approach:
DESIGN BUILD DELIVER OPERATE
1. Governance
3. Source Systems Analysis
2. Business Requirements
4. Data Modelling
5. Select MDM Architecture
and Toolset
7. Build Data Sourcing
components
9. Build Data Quality
components
10. Build User Interfaces and
Workflows
11. End-to-End
Testing
12. Acceptance
Testing &
Deployment
6. System Requirements
8. Build Data Distribution
components
13. Project
Review

Statoil MDM challenges
85% of SCM Service incidents
due to “master data” errors
What is the “Master Data” to be
managed?
› 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
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.

What is the “Master Data” to be managed?
Location
Wells
Field
License
Location
Tag
VendorPipeline
Projects
Processes
Applications
Roles, position
Org. unit
Employee
Service
GEOGRAPHICAL INFORMATION (GIS)
META & MASTER DATA
Subsurface
Information
BI / DW Data
Admin.
Information
Marketing &
Supply
Information
Operation &
Maintenance
Information
Project
Development
Information
COLLABORATION INFORMATION
Structured
Unstructured

What business projects are driving the need
for a corporate approach to MDM?
› 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

How can we build a Business Case
for Master Data Management?

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

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
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Version 21-Jan-2011
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Perf. and reward info
A yellow background indicates that the information subject area contains Enterprise Master Data
Maintenance projects

Statoil Enterprise Master Data Model

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”

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.

Understand Corporate Plan
YEAR 3 YEAR 4 YEAR 5YEAR 2YEAR 1
SUBSEA HARMONISATION
PLANT INTEGRITY
SEISMIC INTERPRETATION
ADVANCED FIELD MANAGEMENT
PROGRAMME X
PROGRAMME Y

Catalog Current Initiatives
Using the project portfolio
Decision gate: Where is the initiative
in the life project process right now?
Owner: Which Business area owns this
initiative?
Item Name: What’s the internal name
of the project / program / initiative?
Business Data Objects: What (in
their own terms) are the Business Data
“things” affected by this program?
Interest: How willing to engage with
MDM initiative is this project?
Importance: How important to the
MDM initiative is this Data area?

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

Harmonise & Xref with Data Model

Prioritise by interest

Conceptual Master Data Model

MDM Dashboards
ROR PENETRATION
ACTIVE VENDORS:
GLOBAL TARGET:
ROR DATA QUALITY:
ROR SERVICE LEVEL:
5000
109,647
Zero Duplicate Records
99% Set up < 24hrs
NEXT STEPS 1Q 2008 A&A on PCR
2Q 2008 US refining hub on PR4
2Q 2008 Gulf of Mexico
2Q 2008 Indonesia
3Q 2008 Iberia M&S
DEFINITION: Complete
ROR PROFILE
OWNER:
STEWARD:
PLATFORM:
Group PSCM – Byron Grote
Mary Drabble
SAP MDM
GO LIVE DATE: April, 2007
VENDOR STATUS

MDM Dashboards
ROR PENETRATION INDIRECT PROCURED MATERIALS
ACTIVE MATERIALS:
GLOBAL TARGET:
55,000
508,000
NEXT STEPS Implement Whiting Refinery
2Q 2008 penetration 20%
Implement Iberia
3Q 2008 Complete
R&M rollout plan approval
3Q 2008 Penetration to 100%
DEFINITION: Complete
Subject to cross segment review
ROR PROFILE
OWNER:
STEWARD:
PLATFORM:
R&M – Chris de Luca
E&P – Ed Koval
R&M – Chris Older
E&P – Kim Hart
SAP MDM
GO LIVE DATE: April, 2007
INDIRECT PROCURED MATERIALS (R&M)

Conclusion

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?

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.
Governance is vital
» MDM initiatives cannot be successful
without addressing Data Governance for
that data subject area
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

chris.bradley@enterprisearchitects.com
@inforacer
+44 7808 038 173
infomanagementlifeandpetrol.blogspot.com


Chris Bradley
Chief Information Architect

Appendices
1. Core MDM solution capabilities
2. MDM selection criteria

Core MDM solution
capabilities
MDM Capabilities

Typical MDM Capabilities
DATA INTEGRATION
& ACQUISITION
DATADISCOVERY&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
MASTERDATA “REPOSITORY”
OPERATIONAL MANAGEMENT / DATA GOVERNANCE & STEWARDSHIP
DATA SYNCHRONISATION

1. Master Data “Hub”
› The Master Data Hub* infrastructure supports:
» Master Data Models, for all deployed concepts
» Metadata Management, supports syntax variety across
source data sets
» May also include Reference Data Management e.g.
Currency codes, ISO Country codes, Project Status codes
» Business Rules Management, associated with
consistency and completeness
› The MDM Architecture implements the target core
repository model for each master data concept
that will be deployed
› Some vendor products come packaged with data
models that can be extended
› Some vendors expect implementers to integrate
their own models within the architecture
* Does NOT always have to be a physical / centralised “hub”

2. Data Integration & Acquisition
› Data integration & data acquisition capabilities
including:
» Data Discovery including Connectors to different data
sources together with extract, transform and load and
other DI capabilities
» Consolidation, identity resolution and matching as a built-
in component or as an add-on from another vendor
› Data quality components capable of:
» Data transformation
» Data Quality validation
» Parsing and Standardization
» Data Cleansing and Loading
» Data Discovery to assess quality and metadata discovery
(some MDM tool vendors either bundle it or offer it as a
separate product)
› Some vendors bundle their MDM platforms with
data integration and data quality/cleansing
capabilities, while other vendors have partnerships
to provide these functions

3. Master Data Services
› Data delivery services to obtain master data out
of the “hub”
› Standard data object lifecycle services such as:
» “create a customer record”
» “update product price”
› For each modelled master object, basic master
data services should be configured to:
» create
» read
» update and delete
» either the object itself or its associated attributes
› MDM products may provide either a service
library or the means for creating master data
services as master object models are integrated or
enhanced

4. Data Delivery
› Delivery to consuming applications of the
information built on the Master Data Services
› Added value Data delivery reducing replication of
common functions implemented multiple times
across different applications such as:
» “create a new customer account” or
» “look up product”
› Basic business services can provide more complex
business services, implemented once and used
multiple times
› A standardized interface will help in transitioning
existing applications to use the master data
system by facilitating application interoperability

5. Access Control
› Typical IDAM capabilities frequently leveraging
LDAP (eg Active Directory) or other O/S
capabilities.
› This component is not typically seen as a
“value added” component of an MDM product
› Access control is required as the unified
master data objects are exposed to a wider
variety of applications and corresponding users

6. Synchronization
› Across the range of core MDM repository
implementations (from Registry to Transaction
hub), there are different requirements for
synchronizing data from the numerous source
data systems
› For reporting purposes, a daily synchronization
may be sufficient, while certain operational
activities may demand immediate coherence
across all views of master data
› Vendors should be able to describe how their
product synchronizes data across the enterprise
and show how to adjust the frequency and
monitor the guarantees of consistency

7. Data Quality, Governance & Operations
› Data Quality must be continuously monitored to
verify acceptability. Also there must be
operational governance and stewardship services
to support:
» Inspection
» Root Cause Analysis
» Data Correction
» Propagation of corrections across the master data
environment
» Data Quality Reporting
» Data Governance Reporting
› Inspection of data quality issues is closely tied to
the data quality capabilities of the data
integration layers and to the business rules
managed within the master data hub
› When issues are identified due to a false positive
merge or a false negative non-merge, interfaces
should allow for manual remediation

What’s most appropriate for
YOUR requirements?
MDM Selection
Criteria

Typical Evaluation Criteria
1. Single domain MD support and/or …
2. Support multiple business data entities
within a single MDM system
3. Flexible platform approach to MDM
4. Support complex relationships and
hierarchies
5. For SOA deployment, automatic
generation of services
6. Integrate Data Quality services in the
MDM solution
7. Support multiple MDM architectural
styles
8. Track history and lineage (e.g. to support
regulatory compliance)
9. Support analytical and operational MDM
operation
10.Use multiple deployment modes: own
data centre, cloud, hybrid
11.Provide MDM Data Governance,
workflow and issue tracking at the
project or enterprise level
12.Metadata management capabilities

1. Single Domain MD Support
Support of single / specialised business data entities with domain specific capabilities
› Does the MDM solution provide specialised features to support the entity?
Examples include:
› Customer name & address matching
› Person house holding / address chaining
› Data enrichment
› Data cross matching to industry standard reference data sets e.g.
» ISO Country codes,
» Dunn & Bradstreet,
» Chemical Abstract Service
» etc

2. Multi Domain MD Support
Support of multiple business data entities within a single MDM system
› Can the MDM solution manage multiple business data entities such as customers, products,
and organizations all within the same software platform?
» By doing so, system maintenance is simplified and more cost effective, which results in lower total cost of
ownership.
› Can the MDM system handle multiple datatypes?
» Implementation can begin with a single business data entity such as customer, and can later be extended to
accommodate other master datatypes—resulting in rapid return on investment.

3. Flexible Platform Approach to MDM
› Can the MDM platform be extended to solve multiple business problems using the same
MDM platform?
» ie not targeted at a specific application / ERP / API set
› Are multiple API’s, methods & services provided by which applications connect to the MDM
platform to publish & subscribe to the Master Data?
› Is the multi domain MDM platform domain and data model independent?
› Can you flexibly define your own data model, generate logic and functionality based on this
defined model, and provide support to configure the GUI based on the functionality?

4. Support of complex relationships & hierarchies
› Can the MDM solution allow multiple hierarchies to be defined?
» organizational relationships,
» legal hierarchies,
» geographic hierarchies
» combinations of parent and child organizations
» etc
› Can complex hierarchies and rules among multiple data entities be defined?

5. Automatic generation of services
› Can the MDM solution generate changes to SOA services (eg generate WSDL) whenever its
data model is updated with new attributes, entities, or sources?
» This helps to protect the higher-level SOA applications from any changes made to the underlying MDM system.
› For an MDM solution with SOA services that are built on a fixed data model, how are the
required services changes identified & flagged?
Appropriate for SOA implementations

6. Integrate Data Quality services
› Can Data Quality rules and activities be incorporated with MDM processes?
» profiling,
» cleansing,
» monitoring
› Are these capabilities provided by MDM platform own capabilities or links / interfaces to
DQ tools?
» Which DQ tools are supported?
› Can the MDM system store the complete history of the data before and after it is cleansed?
» History of data changes is required for many compliance initiatives such as the HIPPA, Solvency 2, Sarbanes-Oxley
Act (SOX) etc
Data Quality is an essential part of an MDM implementation

7. Support multiple MDM architectural styles
› Can the MDM solution be deployed in multiple architectural styles ?
» Centralised,
» Overlay,
» Consolidate,
» Registry,
» Virtualised
» etc
› Which DBMS’s can the MDM solution be deployed upon?
» Are there differences in MDM functionality depending upon the DBMS selected?
Technical Architecture styles of implementation

8. Track History & lineage
› Can the history of all changes to master data be captured?
› Can the lineage of how the data has changed be captured as metadata?
› Can this history be reported upon / displayed in dashboards?
» Is the history / lineage / audit data made available for reporting upon by other BI reporting tools?
Data lineage, history and audit

9. Support analytical & operational MDM operation
› Can the MDM system synchronize master data with both operational and analytical
applications?
» Analytical MDM is frequently the initial step taken in an MDM initiative before attempting the more challenging
task of Operational MDM.
Analytical & Operational MDM

10. Multiple technology deployment modes
› Can the MDM solution be implemented on an in-house data centre?
› Which operating systems are supported for client and server components?
› Is there a cloud MDM deployment option?
» Which cloud services are supported (eg Azure, Force.Com, EC2 etc)?
› For cloud services, what is the licensing model?
» Some vendors haven’t developed Cloud licensing models

11. Provide MDM Data Governance
› Can the MDM system support data governance policies and processes including workflow,
definition of roles per data subject area as defined by the organization?
› Does the MDM platform provide controls and auditing capabilities?
› Is the MDM system able to integrate with security and reporting tools to provide fine-
grained access to data and data quality metrics?
› Does the MDM platform provide a “Data Governor” capability for a SME or Super User to
modify the Master Data directly?
› What Master Data & Data Quality dashboards, metrics and reporting capabilities are
provided?

12. Metadata Management Capabilities
› Can the MDM system consume Metadata from source data files, databases, ETL / CDC
flows?
› Does the MDM system provide its own Metadata repository?
» Is it W3C compliant?
› Can the MDM system access Metadata from a 3rd party Metadata repository?
» Which ones?
› Can Data Governor / Super User roles be defined to allow definitions to be edited?
› Is a user portal provided to enable business users to interrogate data definitions, and status
of the Master data?

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How to identify the correct Master Data subject areas & tooling for your MDM initiative

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