Data tends to pile up and can be rendered unusable or obsolete without careful maintenance processes. Reference and Master Data Management (MDM) has been a popular Data Management approach to effectively gain mastery over not just the data but the supporting architecture for processing it. This webinar presents MDM as a strategic approach to improving and formalizing practices around those data items that provide context for many organizational transactions—its master data. Too often, MDM has been implemented technology-first and achieved the same very poor track record (one-third succeeding on-time, within budget, and achieving planned functionality). MDM success depends on a coordinated approach typically involving Data Governance and Data Quality activities.
Learning Objectives:
- Understand foundational reference and MDM concepts based on the Data Management Body of Knowledge (DMBOK)
- Understand why these are an important component of your Data Architecture
- Gain awareness of Reference and MDM Frameworks and building blocks
- Know what MDM guiding principles consist of and best practices
- Know how to utilize reference and MDM in support of business strategy
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DataEd Webinar: Reference & Master Data Management - Unlocking Business Value
1. Peter Aiken, Ph.D.
Reference
&
Master Data
Management
Copyright 2019 by Data Blueprint Slide # !1
Unlocking Business Value
2. • 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.
3. !3Copyright 2019 by Data Blueprint Slide #
• 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
5. Data
Desired To Be State of Data
!5Copyright 2019 by Data Blueprint Slide #
IT Business
6. The Real State of Data
!6Copyright 2019 by Data Blueprint Slide #
Data
IT Business
7.
UsesUsesReuses
What is data management?
!7Copyright 2019 by Data Blueprint Slide #
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
8.
Data Management
!8Copyright 2019 by Data Blueprint Slide #
Sources
Data
Engineering
Data
Delivery
Data
Storage
Specialized Team Skills
Resources
(optimized for reuse)
Data Governance
AnalyticInsight
Specialized Team Skills
9. !9Copyright 2019 by Data Blueprint Slide #
• 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
10. 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
!10Copyright 2019 by Data Blueprint Slide #
Data Platform/Architecture
Data Governance Data Quality
Data Operations
Data Management Strategy
Technologies
Capabilities
11. 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$Areas
DMM℠ Structure of
5 Integrated
DM Practice Areas
Data
Governance
Data
Management
Strategy
Data
Operations
Platform
Architecture
Supporting
Processes
Maintain fit-for-purpose data,
efficiently and effectively
!11Copyright 2019 by Data Blueprint Slide #
Manage data coherently
Manage data assets professionally
Data life cycle
management
Organizational support
Data
QualityData$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$Areas
Data$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$Areas
Data$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$Areas
Data$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$Areas
Data$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
Data architecture
implementation
12. 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
!12Copyright 2019 by Data Blueprint Slide #
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
13. 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
!13Copyright 2019 by Data Blueprint Slide #
Data Management Functions
15. 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
!15Copyright 2019 by Data Blueprint Slide #
16. !16Copyright 2019 by Data Blueprint Slide #
• 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
17. + 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
!17Copyright 2019 by Data Blueprint Slide #
19. Definitions
!19Copyright 2019 by Data Blueprint Slide #
• 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)
20. 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.
!20Copyright 2019 by Data Blueprint Slide #
21. 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.
!21Copyright 2019 by Data Blueprint Slide #
Current Customer
Ex-Custom
er?
Potential Customer
VIP-Custom
er?
Residential
Customer
Commercial
Customer
Customer
23. 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.
!23Copyright 2019 by Data Blueprint Slide #
27. !27Copyright 2019 by Data Blueprint Slide #
• 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
28. 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
!28Copyright 2019 by Data Blueprint Slide #
Source: http://www.igate.com/22926.aspx
29. 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
!29Copyright 2019 by Data Blueprint Slide #
30. interdependencies
!30Copyright 2019 by Data Blueprint Slide #
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
31. Solution Framework
!31Copyright 2019 by Data Blueprint Slide #
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
32. Inextricably intertwined
!32Copyright 2019 by Data Blueprint Slide #
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(
33. Interactions
!33Copyright 2019 by Data Blueprint Slide #
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
34. 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)
!34Copyright 2019 by Data Blueprint Slide #
38. Combined R/M Data Architecture
!38Copyright 2019 by Data Blueprint Slide #
39. "180% Failure Rate" Fred Cohen, Patni
!39Copyright 2019 by Data Blueprint Slide #
http://www.igatepatni.com/bfs/solutions/payments.aspx
40. 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
!40Copyright 2019 by Data Blueprint Slide #
41. 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
!41Copyright 2019 by Data Blueprint Slide #
45. MDM Business Process Overview
!45Copyright 2019 by Data Blueprint Slide #
Attributed to Steven Steinerman
46. !46Copyright 2019 by Data Blueprint Slide #
• 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
53. !53Copyright 2019 by Data Blueprint Slide #
• 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
55. 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
!55Copyright 2019 by Data Blueprint Slide #
Source:http://www.mdmsource.com/master-data-management-tips-best-practices.html
https://www.ase.org.uk/bestpractice
56. !56Copyright 2019 by Data Blueprint Slide #
• 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
57. 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
!57Copyright 2019 by Data Blueprint Slide #
[Source: unknown]
58. Seven Sisters (from British Telecom)
http://www.datablueprint.com/thought-leaders/peter-aiken/book-monetizing-data-management/ [Thanks to Dave Evans]
!58Copyright 2019 by Data Blueprint Slide #
59. Seven Sisters (from British Telecom)
http://www.datablueprint.com/thought-leaders/peter-aiken/book-monetizing-data-management/ [Thanks to Dave Evans]
!59Copyright 2019 by Data Blueprint Slide #
61. 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:
www.datablueprint.com/webinar-schedule
!61Copyright 2019 by Data Blueprint Slide #
Brought to you by:
62. + =
!62Copyright 2019 by Data Blueprint Slide #
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