In order to succeed, organizations must realize what it means to utilize reference and MDM in support of business strategy. This presentation provides you with an Understanding of the goals of reference and MDM, including the establishment and implementation of authoritative data sources, more effective means of delivering data to various business processes, as well as increasing the quality of information used in organizational analytical functions, e.g. BI. We also highlight the equal importance of incorporating data quality engineering into all efforts related to reference and master data management.
Check out more of our webinars here: http://www.datablueprint.com/webinar-schedule
Data-Ed: Unlock Business Value Through Reference & MDM
1. Unlock Business Value
Through Reference & Master Data Management
10124 W. Broad Street, Suite C
Glen Allen, Virginia 23060
804.521.4056
2. Unlocking Business Value Through Reference & Master Data Management
In order to succeed, organizations must realize what it means to
utilize reference and MDM in support of business strategy. This
presentation provides you with an understanding of the goals of
reference and MDM, including the establishment and
implementation of authoritative data sources, more effective means
of delivering data to various business processes, as well as
increasing the quality of information used in organizational analytical
functions, e.g. BI. We also highlight the equal importance of
incorporating data quality engineering into all efforts related to
reference and master data management.
Learning Objectives
• What is Reference & MDM and why is it important?
• Reference & MDM Frameworks and building blocks
• Guiding principles & best practices
• Understanding foundational reference & MDM concepts based
on the Data Management Body of Knowledge (DMBOK)
MONETIZING
DATA MANAGEMENT
• Utilizing reference & MDM in support of business strategy
Date:
Time:
Presenter:
November 12, 2013
2:00 PM ET/11:00 AM PT
Peter Aiken, Ph.D.
Unlocking the Value in Your Organization’s
Most Important Asset.
PETER AIKEN WITH JUANITA BILLINGS
FOREWORD BY JOHN BOTTEGA
2
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4. Peter Aiken, PhD
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•
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•
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25+ years of experience in data
management
Multiple international awards &
recognition
Founder, Data Blueprint (datablueprint.com)
Associate Professor of IS, VCU (vcu.edu)
President, DAMA International (dama.org)
8 books and dozens of articles
Experienced w/ 500+ data
management practices in 20 countries
Multi-year immersions with
organizations as diverse as the
US DoD, Nokia, Deutsche Bank,
Wells Fargo, and the Commonwealth
of Virginia
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5. Unlocking Business Value Through Reference & Master Data Management
• 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
Tweeting now:
#dataed
5
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6. 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
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7. The DAMA Guide to the Data Management Body of Knowledge
Amazon:
http://www.amazon.com/
DAMA-Guide-ManagementKnowledge-DAMA-DMBOK/
dp/0977140083
Or enter the terms "dama
dm bok" at the Amazon
search engine
Environmental Elements
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8. What is the CDMP?
• Certified Data Management Professional
• DAMA International and ICCP
• 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:
– http://www.dama.org/i4a/pages/
index.cfm?pageid=3399
– http://iccp.org/certification/designations/
cdmp
#dataed
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10. Five Interrelated Data Management Practice Areas
Manage data coherently.
Data Program
Coordination
Share data across boundaries.
Organizational
Data Integration
Data Development
Data Stewardship
Assign responsibilities for data.
Engineer data delivery systems.
Data Support
Operations
Maintain data availability.
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11. Five Integrated DM Practice Areas
Data management
processes and
infrastructure
Organizational Strategies
Implementation
Data Program
Coordination
Guidance
Goals
Organizational
Data Integration
Combining multiple
assets to produce
extra value
Integrated
Models
Achieve sharing of data within a
business area
Organizational-entity
subject area data
integration
Data
Stewardship
Standard
Data
Application
Models &
Designs
Provide reliable data
access
Direction
Data Support
Operations
Feedback
Leverage data in organizational activities
Data
Development
Business
Data
Data
Asset Use
Business Value
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12. Unlocking Business Value Through Reference & Master Data Management
• 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
Tweeting now:
#dataed
12
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14. MDM Definition
• 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."
– Master data is the enterprise's official, consistent set
of identifiers, extended attributes and hierarchies.
– Examples of core entities are:
• Parties (e.g., customers, prospects, people, citizens, employees,
vendors, suppliers and trading partners)
• Places (e.g., locations, offices, regional alignments and
geographies) and
• Things (for example, accounts, assets, policies, products and
services).
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15. 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
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17. 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.
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18. 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.
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22. Unlocking Business Value Through Reference & Master Data Management
• 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
Tweeting now:
#dataed
22
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23. Reference Data Facts 2012
• Global industry-wide survey of
reference data professionals
• Results show: Poor quality of
reference data continues to
create major problems for
financial institutions.
• 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
Source: http://www.igate.com/22926.aspx
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24. 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 reevaluate 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.
Source: http://www.igate.com/22926.aspx
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26. Inextricably intertwined
Knowledge
Management
Practices
Data Organization Practices
Organized Knowledge 'Data'
Routine Data Scans
Metadata(Prac8ces((dashed lines not in existence)
(
Sources(
Suspected/
Identified
Data
Quality
Problems
Metadata(
Engineering(
(
Metadata(
Metadata(
Delivery(
Storage(
(
Metadata(Governance(
Uses(
Data that might benefit from
Master Management
Master Data Catalogs
Master Data
Management
Practices
Data Quality
Engineering
Routine Data Scans
Improved Quality Data
Operational Data
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28. Finance
Multiple Sources of (for example) CustomerApplication
Data
(3rd GL, batch
system, no source)
Payroll Data
(database)
Payroll Application
(3rd GL)
Finance
Data
(indexed)
Marketing Data
(external database)
Marketing Application
(4rd GL, query facilities,
no reporting, very large)
Personnel Data
(database)
R&D
Data
(raw)
Personnel App.
(20 years old,
un-normalized data)
R& D Applications
(researcher supported, no documentation)
Mfg. Data
(home grown
Mfg. Applications
database) (contractor supported)
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33. "180% Failure Rate" Fred Cohen, Patni
http://www.igatepatni.com/bfs/solutions/payments.aspx
Copyright 2013 by Data Blueprint
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34. 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
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35. 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
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45. Unlocking Business Value Through Reference & Master Data Management
• 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
Tweeting now:
#dataed
45
Copyright 2013 by Data Blueprint
47. 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
Source:http://www.mdmsource.com/master-data-management-tips-best-practices.html
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48. 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.
Source:http://www.mdmsource.com/master-data-management-tips-best-practices.html
48
Copyright 2013 by Data Blueprint
49. Unlocking Business Value Through Reference & Master Data Management
• 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
Tweeting now:
#dataed
49
Copyright 2013 by Data Blueprint
50. 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.
[Source: unknown]
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51. 15 MDM Success Factors
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]
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52. Seven Sisters (from British Telecom)
http://www.datablueprint.com/thought-leaders/peter-aiken/book-monetizing-data-management/
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Thanks to Dave Evans
54. Questions?
+
=
It’s your turn!
Use the chat feature or Twitter (#dataed) to submit
your questions to Peter now.
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57. Upcoming Events
December Webinar:
Unlock Business Value Through Document & Content
Management
December 10, 2013 @ 2:00 PM ET/11:00 AM PT
Brought to you by:
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