DataEd Slides: Unlock Business Value Using Reference and Master Data Management Strategies

Reference &
Master Data Management
Copyright 2020 by Data Blueprint Slide # 1Peter Aiken, Ph.D.
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
• 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)
• CDO Society (iscdo.org)
• 11 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.
2Copyright 2020 by Data Blueprint Slide #
Peter Aiken, Ph.D.

Copyright 2020 by Data Blueprint Slide # X
• 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
IT Business
Data
Perceived State of Data

Data
Desired To Be State of Data
IT Business
The Real State of Data
Data
IT Business

Blind Persons and the Elephant
http://www.dailymirror.lk/print/opinion/editorial-we-need-to-become-channels-of-peace/172-27164
It is like a fan!
It is like a snake!
It is like a wall!
It is like a rope!
It is like a tree!
Unrefined
data management
definition
Sources
Uses
Data Management

More refined
data management
definition
Sources
ReuseData Management➜ ➜
Data Governance
Data Assets/Ethical Framework
Sources
➜ Use
➜Reuse
Better still data management definition
➜

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
Data Management Functions
– Planning, implementation and control activities to ensure
consistency with a "golden version" of contextual data values.
Summary: Reference and MDM
from The DAMA Guide to the Data Management Body of Knowledge © 2009 by DAMA International

Definitions
• Planning, implementation and control activities to ensure
consistency with a "golden version" of contextual data values
• … 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)
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
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"
he Data Management Body of Knowledge © 2009 by DAMA International

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

+ 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
– Inadequate training
• Response
– Get data quality-ing!
• Inexperienced
– Immature data quality practices
– Tool/technological focus
– Purchased a data quality tool

Garbage In ➜ Garbage Out!
My most profound lesson! (so far)
Perfect
Model
Garbage
Data
Garbage
Results
Data
Warehouse
Machine
Learning
Business
Intelligence
Block ChainAIMDM
Data
Governance
AnalyticsTechnology
GI➜GO!

Perfect
Model
Garbage
Data
Garbage
Results
Data
Warehouse
Machine
Learning
Block Chain
AI
MDM
Analytics
Technology
Data
Governance
GI➜GO!
Business
Intelligence
GI➜GO!
Perfect
Model
Quality
Data
Garbage
Results
Data
Warehouse
Machine
Learning
Business
Intelligence
Block Chain
AI
MDM
Analytics
Technology
Data
Governance

Perfect
Model
Quality
Data
Garbage
Results
Data
Warehouse
Machine
Learning
Business
Intelligence
Block Chain
AI
MDM
Analytics
Technology
Data
Governance
GI➜GO!
Perfect
Model
Quality
Data
Garbage
Results
Data
Warehouse
Machine
Learning
Business
Intelligence
Block Chain
AI
MDM
Analytics
Technology
Data
Governance
GI➜GO!

Perfect
Model
Quality
Data
Good
Results
Data
Warehouse
Machine
Learning
Business
Intelligence
Block Chain
AI
MDM
Analytics
Technology
Data
Governance
Quality In ➜ Quality Out!
Version 1
Data
Strategy
Data
Governance
Data
Quality
Improving
operations in
3 data
management
practice areas
BI
Warehouse

Version 2
Data
Strategy
Data
Governance
BI
Warehouse
Metadata
Improving
operations in
3 data
management
practice areas
Version 3
Data
Strategy
Data Governance BI/Warehouse
Reference &
Master Data
Perfecting
operations in 3
data
management
practice areas

A good way to begin practicing data
• 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

Inextricably intertwined implementations and …
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)
Sample 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

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

Task vs. Process Orientation
• What is meant by a task
orientation?
– Industrial work should be broken down
into its simplest and most basic tasks
• What is meant by a process
orientation?
– Reunifying tasks into coherent
business processes
• What else must be part of the
analysis?
– Identify and abandon outdated rules
and assumptions that underlie current
business operations
Task 1
Task 2
Task 3
Task 4
Task 5
Task 6
Task 7
Task 8
Task 9
Task 10
Task 11
Task 12
Task 1
Task 7
Task 9
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

Activities and Flows with amounts and durations
Process Flows and Durations

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
Knowledge © 2009 by DAMA International

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

15 MDM Success Factors
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
[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
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Monday, 3/23/2020 @ 1:30 PM PT
April Webinar:
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Tuesday, April 14, 2020 @ 2:00 PM ET/11:00 AM PT (UTC-5)
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Sign up for webinars at:
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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 2020 by Data Blueprint Slide # 73

DataEd Slides: Unlock Business Value Using Reference and Master Data Management Strategies

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DataEd Slides: Unlock Business Value Using Reference and Master Data Management Strategies