This presentation provides you with an understanding of reference and master data management (MDM) goals, including establishing and implementing authoritative data sources, establishing and implementing more effective means of delivering data to various business processes, and increasing the quality of information used in organizational analytical functions (such as BI). Attendees will learn how to incorporate data quality engineering into the planning of reference and MDM. Finally, we will discuss why MDM is so critical to the organization’s overall data strategy.
Takeaways:
•What is reference and MDM?
•Why are reference and MDM important?
•How to use Reference and MDM Frameworks
•Guiding principles & best practices for MDM
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Data-Ed Webinar: The Importance of MDM
1. Unlock Business Value
Through Reference & Master Data Management
10124 W. Broad Street, Suite C
Glen Allen, Virginia 23060
804.521.4056
Peter Aiken, Ph.D.
• 30+ years in data management
• Repeated international recognition
• Founder, Data Blueprint (datablueprint.com)
• Associate Professor of IS (vcu.edu)
• DAMA International (dama.org)
• 9 books and dozens of articles
• Experienced w/ 500+ data
management practices
• Multi-year immersions:
– US DoD (DISA/Army/Marines/DLA)
– Nokia
– Deutsche Bank
– Wells Fargo
– Walmart
– …
• DAMA International President 2009-2013
• DAMA International Achievement Award 2001 (with
Dr. E. F. "Ted" Codd
• DAMA International Community Award 2005
PETER AIKEN WITH JUANITA BILLINGS
FOREWORD BY JOHN BOTTEGA
MONETIZING
DATA MANAGEMENT
Unlocking the Value in Your Organization’s
Most Important Asset.
The Case for the
Chief Data Officer
Recasting the C-Suite to Leverage
Your MostValuable Asset
Peter Aiken and
Michael Gorman
2Copyright 2016 by Data Blueprint Slide #
2. We believe ...
Data
Assets
Financial
Assets
Real
Estate Assets
Inventory
Assets
Non-
depletable
Available for
subsequent
use
Can be
used up
Can be
used up
Non-
degrading √ √ Can degrade
over time
Can degrade
over time
Durable Non-taxed √ √
Strategic
Asset √ √ √ √
3
Copyright 2015 by Data Blueprint
• Today, data is the most powerful, yet underutilized and poorly
managed organizational asset
• Data is your
– Sole
– Non-depleteable
– Non-degrading
– Durable
– Strategic
• Asset
– Data is the new oil!
– Data is the new (s)oil!
– Data is the new bacon!
• Our mission is to unlock business value by
– Strengthening your data management capabilities
– Providing tailored solutions, and
– Building lasting partnerships
Asset: A resource controlled by the organization as a result of past events or
transactions and from which future economic benefits are expected to flow [Wikipedia]
Copyright 2013 by Data Blueprint
• 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
Unlocking Business Value Through Reference & Master Data Management
Tweeting now:
#dataed
4
3.
UsesUsesReuses
What is data management?
5Copyright 2016 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
Data Management
6Copyright 2016 by Data Blueprint Slide #
Sources
Data
Engineering
Data
Delivery
Data
Storage
Specialized Team Skills
Resources
(optimized for reuse)
Data Governance
AnalyticInsight
Specialized Team Skills
4. Maslow's Hierarchiy of Needs
7
Copyright 2015 by Data Blueprint
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)
Data Management Practices Hierarchy
Advanced
Data
Practices
• MDM
• Mining
• Big Data
• Analytics
• Warehousing
• SOA
Foundational Data Management Practices
8
Copyright 2015 by Data Blueprint
Data Platform/Architecture
Data Governance Data Quality
Data Operations
Data Management Strategy
Technologies
Capabilities
5. 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 architecture
implementation
Data
Governance
Data
Management
Strategy
Data
Operations
Platform
Architecture
Supporting
Processes
Maintain fit-for-purpose data,
efficiently and effectively
9Copyright 2016 by Data Blueprint Slide #
Manage data coherently
Manage data assets professionally
Data life cycle
management
Organizational support
Data
Quality
Copyright 2013 by Data Blueprint
The DAMA Guide to the Data Management Body of Knowledge
10
Data Management Functions
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
6. Copyright 2013 by Data Blueprint
• 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
Unlocking Business Value Through Reference & Master Data Management
Tweeting now:
#dataed
11
+ 1 Year
12
Copyright 2015 by Data Blueprint
• 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
8. 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.
15Copyright 2016 by Data Blueprint Slide #
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.
16Copyright 2016 by Data Blueprint Slide #
11. Copyright 2013 by Data Blueprint
• 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
Unlocking Business Value Through Reference & Master Data Management
Tweeting now:
#dataed
21
Copyright 2013 by Data Blueprint
Reference Data Facts 2012
• Home-grown reference data solutions predominate,
putting institutions at risk for meeting regulatory
constraints
• Risk management is seen as a more important
business driver for improving data quality than cost
22
Source: http://www.igate.com/22926.aspx
• Global industry-wide survey of
reference data professionals
• Results show: Poor quality of
reference data continues to
create major problems for
financial institutions.
12. Copyright 2013 by Data Blueprint
Reference Data Facts 2012, cont’d
• Despite recommended practices of centralizing
reference data operations, 31% of the firms surveyed
still manage data locally
• New and changing regulatory requirements have
prompted many financial service companies to re-
evaluate their reference data strategies. To prepare
for new regulations,
nearly 62% of survey
respondents are planning
to extend or customize
their reference data
systems during 2012 and 2013.
23
Source: http://www.igate.com/22926.aspx
Copyright 2013 by Data Blueprint
Interdependencies
24
Data Governance
Master DataData Quality
13. interdependencies
25Copyright 2016 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
Solution Framework
26Copyright 2016 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
14. Copyright 2013 by Data Blueprint
Inextricably intertwined
27
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(
Copyright 2013 by Data Blueprint
Interactions
28
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
15. Copyright 2013 by Data Blueprint
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)
Finance
Data
(indexed)
Finance Application
(3rd GL, batch
system, no source)
Marketing Application
(4rd GL, query facilities,
no reporting, very large)
Marketing Data
(external database)
Personnel App.
(20 years old,
un-normalized data)
Personnel Data
(database)
29
Multiple Sources of (for example) Customer Data
Copyright 2013 by Data Blueprint
Vocabulary is Important-Tank, Tanks, Tankers, Tanked
30
17. Copyright 2013 by Data Blueprint
Combined R/M Data Architecture
33
Copyright 2013 by Data Blueprint
"180% Failure Rate" Fred Cohen, Patni
34
http://www.igatepatni.com/bfs/solutions/payments.aspx
18. Copyright 2013 by Data Blueprint
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
35
Copyright 2013 by Data Blueprint
Automating Business Process Discovery (qpr.com)
36
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
19. Copyright 2013 by Data Blueprint
Traditional Engine
37
Copyright 2013 by Data Blueprint
Prius Hybrid Engine
38
20. Copyright 2013 by Data Blueprint
39
Copyright 2013 by Data Blueprint
• 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
Unlocking Business Value Through Reference & Master Data Management
Tweeting now:
#dataed
40
25. Copyright 2013 by Data Blueprint
10 Best Practices for MDM
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
49
Source:http://www.mdmsource.com/master-data-management-tips-best-practices.html
Copyright 2013 by Data Blueprint
10 Best Practices for MDM, cont’d
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.
50
Source:http://www.mdmsource.com/master-data-management-tips-best-practices.html
26. Copyright 2013 by Data Blueprint
• 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
Unlocking Business Value Through Reference & Master Data Management
Tweeting now:
#dataed
51
Copyright 2013 by Data Blueprint
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.
52
[Source: unknown]
27. Copyright 2013 by Data Blueprint
15 MDM Success Factors
53
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]
Copyright 2013 by Data Blueprint
54
30. Copyright 2013 by Data Blueprint
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