DataEd Webinar: Reference & Master Data Management - Unlocking Business Value

Peter Aiken, Ph.D.
Reference  
&  
Master Data
Management
Copyright 2019 by Data Blueprint Slide # !1
Unlocking Business Value

• DAMA International President 2009-2013 / 2018
• DAMA International Achievement Award 2001  
(with Dr. E. F. "Ted" Codd
• DAMA International Community Award 2005
Peter Aiken, Ph.D.
!2Copyright 2019 by Data Blueprint Slide #
• I've been doing this a long time
• My work is recognized as useful
• Associate Professor of IS (vcu.edu)
• Founder, Data Blueprint (datablueprint.com)
• DAMA International (dama.org)
• 10 books and dozens of articles
• Experienced w/ 500+ data
management practices worldwide
• Multi-year immersions
– US DoD (DISA/Army/Marines/DLA)
– Nokia
– Deutsche Bank
– Wells Fargo
– Walmart
– …
PETER AIKEN WITH JUANITA BILLINGS
FOREWORD BY JOHN BOTTEGA
MONETIZING
DATA MANAGEMENT
Unlocking the Value in Your Organization’s
Most Important Asset.

• Data Management Overview
• What is Reference and MDM?
• Why is Reference and MDM important?
• Reference & MDM Building Blocks
• Guiding Principles & Best Practices
• Take Aways, References & Q&A
Reference & Master Data Management - Unlocking Business Value
Tweeting now: t#dataed

IT Business
Data
Perceived State of Data

Data
Desired To Be State of Data
IT Business

The Real State of Data
Data
IT Business

UsesUsesReuses
What is data management?
Sources
 
Data
Engineering
 
Data  
Delivery
 
Data 
Storage
Specialized Team Skills
Data Governance
Understanding the current
and future data needs of an
enterprise and making that
data effective and efficient in
supporting  
business activities  
Aiken, P, Allen, M. D., Parker, B., Mattia, A.,  
"Measuring Data Management's Maturity:  
A Community's Self-Assessment"  
IEEE Computer (research feature April 2007)
Data management practices connect
data sources and uses in an
organized and efficient manner
• Engineering
• Storage
• Delivery
• Governance
When executed,  
engineering, storage, and  
delivery implement governance
Note: does not well-depict data reuse

Data Management
Sources
 
Data
Engineering
 
Data  
Delivery
 
Data 
Storage
 
Resources 
(optimized for reuse) 
Data Governance
AnalyticInsight

Data Management Practices Hierarchy
You can accomplish
Advanced Data Practices
without becoming proficient
in the Foundational Data
Management Practices
however this will:
• Take longer
• Cost more
• Deliver less
• Present  
greater 
risk 
(with thanks to Tom DeMarco)
Advanced  
Data  
Practices
• MDM
• Mining
• Big Data
• Analytics
• Warehousing
• SOA
Foundational Data Management Practices
Data Platform/Architecture
Data Governance Data Quality
Data Operations
Data Management Strategy
Technologies
Capabilities

Data$Management$
Strategy
Data Management Goals
Corporate Culture
Data Management Funding
Data Requirements Lifecycle
Data
Governance
Governance Management
Business Glossary
Metadata Management
Data
Quality
Data Quality Framework
Data Quality Assurance
Data
Operations
Standards and Procedures
Data Sourcing
Platform$&$
Architecture
Architectural Framework
Platforms & Integration
Supporting$
Processes
Measurement & Analysis
Process Management
Process Quality Assurance
Risk Management
Configuration Management
Component Process$Areas
DMM℠ Structure of  
5 Integrated  
DM Practice Areas
Data  
Governance
Data  
Management 
Strategy
Data  
Operations
Platform 
Architecture
Supporting 
Processes
Maintain fit-for-purpose data,
efficiently and effectively
Manage data coherently
Manage data assets professionally
Data life cycle
management
Organizational support
Data  
QualityData$Management$
Strategy
Corporate Culture
Data
Governance
Business Glossary
Metadata Management
Data
Quality
Data
Operations
Data Sourcing
Platform$&$
Architecture
Supporting$
Processes
Process Management
Risk Management
Data$Management$
Strategy
Corporate Culture
Data
Governance
Business Glossary
Metadata Management
Data
Quality
Data
Operations
Data Sourcing
Platform$&$
Architecture
Supporting$
Processes
Process Management
Risk Management
Data$Management$
Strategy
Corporate Culture
Data
Governance
Business Glossary
Metadata Management
Data
Quality
Data
Operations
Data Sourcing
Platform$&$
Architecture
Supporting$
Processes
Process Management
Risk Management
Data$Management$
Strategy
Corporate Culture
Data
Governance
Business Glossary
Metadata Management
Data
Quality
Data
Operations
Data Sourcing
Platform$&$
Architecture
Supporting$
Processes
Process Management
Risk Management
Data$Management$
Strategy
Corporate Culture
Data
Governance
Business Glossary
Metadata Management
Data
Quality
Data
Operations
Data Sourcing
Platform$&$
Architecture
Supporting$
Processes
Process Management
Risk Management
Data architecture
implementation

Your data foundation can
only be as strong as its
weakest link!
Data architecture
implementation
Data  
Governance
Data  
Management 
Strategy
Data  
Operations
Platform 
Architecture
Supporting 
Processes
Maintain fit-for-purpose data,
efficiently and effectively
Manage data coherently
Manage data assets professionally
Data life cycle
management
Organizational support
Data  
Quality
Data  
Governance
Data  
Quality
Platform 
Architecture
Data  
Operations
Data  
Management 
Strategy
3 3
33
1
Supporting 
Processes
Optimized
Measured
Defined
Managed
Initial
Optimized
Measured
Defined
Managed
Initial
Optimized
Measured
Defined
Managed
Initial
Optimized
Measured
Defined
Managed
Initial
Optimized
Measured
Defined
Managed
Initial

The DAMA Guide to the Data Management Body of Knowledge
Published by DAMA
International
• The professional
association for Data
Managers (40
chapters worldwide)
DMBoK organized
around
• Primary data
management
functions focused
around data
delivery to the
organization
• Organized around
several
environmental
elements
Data Management Functions

Summary: Reference and MDM
from The DAMA Guide to the Data Management Body of Knowledge © 2009 by DAMA International

What is the CDMP?
• Certified Data Management
Professional
• DAMA International
• Membership in a distinct group
made up of your fellow
professionals
• Recognition for your specialized
knowledge in a choice of 17
specialty areas
• Series of 3 exams
• For more information, please
visit:
– httphttps://dama.org/content/cdmp-0

+ 1 Year
• Confusion as to the system's value
– Users lack confidence
– Business did not know how to use  
"the MDM"
• General agreement
– Restart the effort
• "Root cause" analysis
– Consensus
– Poor quality data
• Response
– Get data quality-ing!
• Inexperienced
– Immature data quality practices
– Tool/technological focus
– Purchased a data quality tool

Garbage In ➜ Garbage Out

Definitions
• as opposed to mobile device management
• Gartner holds that MDM is a  
discipline or strategy
– "… where the business and the IT organization  
work together to ensure the uniformity, accuracy,  
semantic persistence, stewardship and accountability  
of the enterprise's official, shared master data."
• Sold as technology-based solution
• Official, consistent set of identifiers - examples of these core
entities include:
– Parties (customers, prospects, people, citizens, employees, vendors,
suppliers, trading partners, individuals, organizations, citizens, patients,
vendors, supplies, business partners, competitors, students, products,
financial structures *LEI*)
– Places (locations, offices, regional alignments, geographies)
– Things (accounts, assets, policies, products, services)

Wikipedia: Golden Version
• In software development:
– The Golden Master is usually the RTM (Released to Manufacturing)
version, and therefore the commercial version. It represents the
development stage of "RTM" (Released To Manufacturing), often
referred to as "going gold", or "gone golden".
– Often confused with "gold master" which refers to a physical
recording entity such as that sent to a manufacturing plant.
• In data management:
– It is the data value representing the  
"correct" answer to the business question
• Definition-Reference/Master Data Management
– Planning, implementation and control activities to ensure
consistency with a "golden version" of contextual data values.

Definition: Reference Data Management
• Control over defined domain values (also known as
vocabularies), including:
– Control over standardized terms, code values and other unique
identifiers;
– Business definitions for each value, business relationships within
and across domain value lists, and the;
– Consistent, shared use of  
accurate, timely and  
relevant reference data  
values to classify and  
categorize data.
Current Customer
Ex-Custom
er?
Potential Customer
VIP-Custom
er?
Residential 
Customer
Commercial 
Customer
Customer

Reference Data
• Reference Data:
– Data used to classify or categorize other data, the value domain
– Order status: new, in progress, closed, cancelled
– Two-letter USPS state code abbreviations (VA)
• Reference Data Sets
US United States
GB (not UK) United Kingdom

Definition: Master Data Management
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.

Master Data
• Data about business entities providing context for
transactions but not limited to pre-defined values
• Business rules dictate format and allowable ranges
– Parties (individuals, organizations, customers, citizens, patients,
vendors, supplies, business partners, competitors, employees,
students)
– Locations, products, financial structures
• Provide context for transactions
• From the term "Master File"

Example Transaction Processing System
$5
Balance=$100 Balance=$95

Reference Data versus Master Data
• Reference Data:
– Control over defined
domain values
(vocabularies) for
standardized terms,
code values, and other
unique identifiers
– The fact that we
maintain 9 possible
gender codes
• Master Data:
– Control over master
data values to enable
consistent, shared,
contextual use across
systems
– The "golden" source of
the gender of your
customer "Pat"
Both provide the context
for transaction data

Data Facts
• Poor quality of reference data continues to create major
problems for financial institutions
– To many firms surveyed still manage data locally
• Home-grown reference data solutions predominate
– Putting institutions at risk for meeting regulatory constraints
• Risk management
– More important business driver than cost
• New and changing regulatory  
requirements prompt many to  
re-evaluate their approach
Source: http://www.igate.com/22926.aspx

A good way to begin practicing data strategy
• Select 3 data
management
functions (parts of
the DM BoK)
– Data Governance
– Reference and
Master Data
Management
– Data Quality
Management

interdependencies
Data Governance
Master DataData Quality
makes the
case and is
responsible for
is a necessary but
insufficient prerequisite
to success
MD capabilities
constrain governance
effectiveness

Solution Framework
SORs
SOR 1
SOR 2
SOR 3
SOR 4
SOR 5
SOR 6
SOR 7
SOR 8
Repository
Indicator 
Extraction 
Service 
(could be  
segmented by 
day of week 
month,  
system, etc.)
Update 
Addresses
Latency 
Check 
Service
Ch 1
Ch 2
Ch 3
Ch 4
Ch 5
Ch 6
Channels
Ch 7
Ch 8
External Address  
Validation Processing
Customer 
Contact

Inextricably intertwined
Organized Knowledge 'Data'
Improved Quality Data
Data Organization Practices
Operational Data
Data Quality
Engineering
Master Data
Management
Practices
Suspected/
Identified
Data
Quality
Problems
Routine Data Scans
Master Data Catalogs
Routine Data Scans
Knowledge
Management
Practices
Data that might benefit from
Master Management
Sources( (
Metadata(Governance(
(
Metadata(
Engineering(
(
Metadata(
Delivery(
Uses(
Metadata(Prac8ces((dashed lines not in existence)
Metadata(
Storage(

Interactions
Improved Quality Data
Master
Data
Monitoring
Data
Governance
Practices
Master Data
Management
Practices
Governance
Violations
Monitoring
Data Quality
Engineering
Practices
Data
Quality
Monitoring
Monitoring
Results:
Suspected/
Identified
Data
Quality
Problems Data
Quality
Rules
Monitoring
Results:
Suspected/
Master
Data &
Characteristics
Routine
Data
Scans
Master
Data
Catalogs
Governance
Rules
Routine
Data
Scans
Monitoring
Rules
Focused
Data
Scans
Operational Data
Data
Harvesting
Quality
Rules

Multiple Sources of (for example) Customer Data
Payroll Application 
(3rd GL)Payroll Data
(database)
R& D Applications 
(researcher supported, no documentation)
R & D
Data
(raw) Mfg. Data
(home grown
database)
Mfg. Applications 
(contractor supported)
Marketing Application 
(4rd GL, query facilities,  
no reporting, very large)
 
Marketing Data
(external database)
 
Finance
Data
(indexed)
Finance Application 
(3rd GL, batch  
system, no source)
Personnel App. 
(20 years old, 
un-normalized data)
 
Personnel Data 
(database)

Vocabulary is Important-Tank, Tanks, Tankers

Reference Data Architecture

Master Data Architecture

Combined R/M Data Architecture

"180% Failure Rate" Fred Cohen, Patni
http://www.igatepatni.com/bfs/solutions/payments.aspx

MDM Failure Root-Causes
• 30% of MDM programs are regarded as failures
• 70% of SOA projects in complex, heterogeneous environments
had failed to yield the expected business benefits unless MDM
is included
• Root-causes of failures:
– 80% percent of MDM initiatives fail because of ineffective leadership,
underestimated magnitudes or an inability to deal with the cultural impact
of the change
– MDM was implemented as a technology or as a project
– MDM was an Enterprise Data Warehouse (EDW) or an ERP
– MDM was an IT Effort
– MDM is separate to data governance and data quality
– MDM initiatives are implemented with inappropriate technology
– Internal politics and the silo mentality impede the MDM initiatives

Automating Business Process Discovery (qpr.com)
Benefits
• Obtain holistic perspective on
roles and value creation
• Customers understand and
value outputs
• All develop better shared
understanding
Results
• Speed up process
• Cost savings
• Increased compliance
• Increased output
• IT systems documentation

Traditional Engine

Prius Hybrid Engine

MDM Business Process Overview
Attributed to Steven Steinerman

Goals and Principles
1. Provide authoritative
source of reconciled,
high-quality master and
reference data.
2. Lower cost and
complexity through reuse
and leverage of
standards.
3. Support business
intelligence and
information integration
efforts.

Reference & MDM Activities
• Understand Reference and  
Master Data Integration Needs
• Identify Master and Reference Data  
Sources and Contributors
• Define and Maintain the Data  
Integration Architecture
• Implement Reference and Master  
Data Management Solutions
• Define and Maintain Match Rules
• Establish “Golden” Records
• Define and Maintain Hierarchies and Affiliations
• Plan and Implement Integration of New Data Sources
• Replicate and Distribute Reference and Master Data
• Manage Changes to Reference and Master Data

Specific Reference and MDM Investigations
• Who needs what information?
• What data is available from  
different sources?
• How does data from different  
sources differ?
• How can inconsistencies be reconciled?
• How should valid values be shared?

Primary Deliverables
• Data Cleansing Services
• Master and Reference  
Data Requirements
• Data Models and Documentation
• Reliable Reference and Master Data
• "Golden Record" Data Lineage
• Data Quality Metrics and Reports

Roles and Responsibilities
• Suppliers:
– Steering
Committees
– Business Data
Stewards
– Subject Matter
Experts
– Data Consumers
– Standards
Organizations
– Data Providers
– ... 
 
 
 
• Consumers:
– Application Users
– BI and Reporting
Users
– Application
Developers and
Architects
– Data integration
Developers and
Architects
– BI Vendors and
Architects
– Vendors,
Customers and
Partners
– ...
• Participants:
– Data Stewards
– Subject Matter
Experts
– Data Architects
– Data Analysts
– Application
Architects
– Data
Governance
Council
– Data Providers
– Other IT
Professionals
– ...

Technology
• ETL
• Reference Data  
Management Applications
• Master Data  
Management Applications
• Data Modeling Tools
• Process Modeling Tools
• Meta-data Repositories
• Data Profiling Tools
• Data Cleansing Tools
• Data Integration Tools
• Business Process and Rule Engines
• Change Management Tools

Guiding Principles
1. Shared R/M data belong to the organization
2. R/M data management is an on-going data quality
improvement program – goals cannot  
be achieved by 1 project alone.
3. Business data stewards are the authorities accountable
at determining the golden values.
4. Golden values represent the "best" sources.
5. Replicate master data values only from golden sources.
6. Reference data changes require formal change
management

1. Active, involved executive sponsorship
2. The business should own the data governance  
process and the MDM or CDI project
3. Strong project management and organizational change management
4. Use a holistic approach - people, process, technology and
information
5. Build your processes to be ongoing and repeatable, supporting
continuous improvement
6. Management needs to recognize the importance of a dedicated
team of data stewards
7. Understand your MDM hub's data model and how it integrates with
your internal source systems and external content providers
8. Resist the urge to customize
9. Stay current with vendor-provided patches
10. Test, test, test and then test again.
1. Active, involved executive sponsorship
2. The business should own the data governance  
process and the MDM or CDI project
3. Strong project management and organizational change management
4. Use a holistic approach - people, process, technology and
information
5. Build your processes to be ongoing and repeatable, supporting
continuous improvement
6. Management needs to recognize the importance of a dedicated
team of data stewards
7. Understand your MDM hub's data model and how it integrates with
your internal source systems and external content providers
8. Resist the urge to customize
9. Stay current with vendor-provided patches
10. Test, test, test and then test again.
10 Best Practices for MDM
Source:http://www.mdmsource.com/master-data-management-tips-best-practices.html
https://www.ase.org.uk/bestpractice

1. Success is more likely and more frequently observed once users and prospects understand the
limitations and strengths of MDM.
2. Taking small steps and remaining educated on where the MDM market and technology vendors are
will increase longer-term success with MDM.
3. Set the right expectations for MDM initiative to help assure long-term success.
4. Long-term MDM success requires the involvement of the information architect.
5. Create a governance framework to ensure that individuals manage master data in a desirable
manner.
6. Strong alignment with the organization's business vision, demonstrated by measuring the program's
ongoing value, will underpin MDM success.
7. Use a strategic MDM framework through all stages of the MDM program activity cycle — strategize,
evaluate, execute and review.
8. Gain high-level business sponsorship for the MDM program, and build strong stakeholder support.
9. Start by creating an MDM vision and a strategy that closely aligns to the organization’s business
vision.
10. Use an MDM metrics hierarchy to communicate standards for success, and to objectively measure
progress.
11. Use a business case development process to increase business engagement.
12. Get the business to propose and own the KPIs; articulate the success of this scenario.
13. Measure the situation before and after the MDM implementation to determine the change.
14. Translate the change in metrics into financial results.
15. The business and IT organization should work together to achieve a single view of master data
1. Success is more likely and more frequently observed once users and prospects understand the
limitations and strengths of MDM.
2. Taking small steps and remaining educated on where the MDM market and technology vendors are
will increase longer-term success with MDM.
3. Set the right expectations for MDM initiative to help assure long-term success.
4. Long-term MDM success requires the involvement of the information architect.
5. Create a governance framework to ensure that individuals manage master data in a desirable
manner.
6. Strong alignment with the organization's business vision, demonstrated by measuring the program's
ongoing value, will underpin MDM success.
7. Use a strategic MDM framework through all stages of the MDM program activity cycle — strategize,
evaluate, execute and review.
8. Gain high-level business sponsorship for the MDM program, and build strong stakeholder support.
9. Start by creating an MDM vision and a strategy that closely aligns to the organization’s business
vision.
10. Use an MDM metrics hierarchy to communicate standards for success, and to objectively measure
progress.
11. Use a business case development process to increase business engagement.
12. Get the business to propose and own the KPIs; articulate the success of this scenario.
13. Measure the situation before and after the MDM implementation to determine the change.
14. Translate the change in metrics into financial results.
15. The business and IT organization should work together to achieve a single view of master data
15 MDM Success Factors
[Source: unknown]

Seven Sisters (from British Telecom)
http://www.datablueprint.com/thought-leaders/peter-aiken/book-monetizing-data-management/ [Thanks to Dave Evans]

Summary: Reference and MDM

Upcoming Events
Enterprise Data World 
How I Learned to Stop Worrying & Love My Data Warehouse 
Tuesday, 3/19/2019 @ 1:45 PM ET
Data Management Brain Drain 
Wednesday, 3/20/2019 @ 2:45 PM ET
April Webinar 
Approaching Data Management Technologies 
April 9, 2019 @ 2:00 PM ET
May Webinar 
Data Management Maturity:  
Achieving Best Practices using DMM 
May 14, 2019 @ 2:00 PM ET
 
Sign up for webinars at:  
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Questions?

References

Additional References
• http://www.mdmsource.com/master-data-management-tips-best-practices.html
• http://www.igate.com/22926.aspx
• http://www.itbusinessedge.com/cm/blogs/lawson/just-the-stats-master-data-
management/?cs=50349
• http://searchcio-midmarket.techtarget.com/news/2240150296/Smart-grid-
systems-expert-devises-business-transformation-template
• http://www.itbusinessedge.com/cm/blogs/lawson/free-report-shows-businesses-
fed-up-with-bad-data/?cs=50416
• http://www.itbusinessedge.com/cm/blogs/lawson/whats-ahead-for-master-data-
management/?cs=50082
• http://www.itbusinessedge.com/cm/blogs/vizard/master-data-management-
reaches-for-the-cloud/?cs=49264
• http://www.information-management.com/channels/master-data-
management.html
• http://www.dataversity.net/applying-six-sigma-to-master-data-management-
mdm-framework-for-integrating-mdm-into-ea-part-2/
• http://www.dataqualityfirst.com/getting_master_data_facts_straight_is_hard.htm

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Copyright 2019 by Data Blueprint Slide # 65

DataEd Webinar: Reference & Master Data Management - Unlocking Business Value

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