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Unlock Business Value
Through Reference & Master Data Management

10124 W. Broad Street, Suite C
Glen Allen, Virginia 23060
804.521.4056
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
Copyright 2013 by Data Blueprint
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Copyright 2013 by Data Blueprint
Peter Aiken, PhD
•
•
•
•
•
•
•
•

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
4
Copyright 2013 by Data Blueprint

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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
Copyright 2013 by Data Blueprint
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
6
Copyright 2013 by Data Blueprint
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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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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Data Management

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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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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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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
Copyright 2013 by Data Blueprint
Summary:
Reference
and MDM

from The DAMA Guide to the Data Management Body of Knowledge © 2009 by DAMA International
13
Copyright 2013 by Data Blueprint
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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Copyright 2013 by Data Blueprint
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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Reference/Master Data Management
• Definition

– Planning, implementation and control activities to ensure
consistency with a "golden version" of contextual data values.

from The DAMA Guide to the Data Management Body of Knowledge © 2009 by DAMA International

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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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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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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
from The DAMA Guide to the Data Management Body of Knowledge © 2009 by DAMA International

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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

• From the term Master File
from The DAMA Guide to the Data Management Body of Knowledge © 2009 by DAMA International

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Copyright 2013 by Data Blueprint
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
from The DAMA Guide to the Data Management Body of Knowledge © 2009 by DAMA International
21
Copyright 2013 by Data Blueprint
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
Copyright 2013 by Data Blueprint
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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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 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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Copyright 2013 by Data Blueprint
Interdependencies
Data Governance

Data Quality

Master Data

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Copyright 2013 by Data Blueprint
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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Copyright 2013 by Data Blueprint
Interactions
Governance
Violations
Monitoring
Routine
Data
Scans

Master
Data
Monitoring

Data
Quality
Monitoring

Governance
Rules

Monitoring
Rules

Data
Governance
Practices
Quality
Rules
Routine
Data
Scans

Data
Harvesting

Monitoring
Results:
Suspected/
Identified
Data
Quality
Problems

Monitoring
Results:
Suspected/
Master
Data &
Characteristics

Master
Data
Catalogs

Data
Quality
Rules

Data Quality
Engineering
Practices

Focused
Data
Scans

Master Data
Management
Practices

Improved Quality Data

Operational Data
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Copyright 2013 by Data Blueprint
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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Vocabulary is Important-Tank, Tanks, Tankers, Tanked

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Reference Data Architecture

from The DAMA Guide to the Data Management Body of Knowledge © 2009 by DAMA International
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Master Data Architecture

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Combined R/M Data Architecture

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"180% Failure Rate" Fred Cohen, Patni

http://www.igatepatni.com/bfs/solutions/payments.aspx
Copyright 2013 by Data Blueprint

33
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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Copyright 2013 by Data Blueprint
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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Traditional Engine

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Prius Hybrid Engine

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Goals and Principles
1. Provide authoritative
source of reconciled, highquality master and
reference data.
2. Lower cost and complexity
through reuse and leverage
of standards.
3. Support business
intelligence and information
integration efforts.
from The DAMA Guide to the Data Management Body of Knowledge © 2009 by DAMA International

39
Copyright 2013 by Data Blueprint
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
from The DAMA Guide to the Data Management Body of Knowledge © 2009 by DAMA International
40
Copyright 2013 by Data Blueprint
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?
from The DAMA Guide to the Data Management Body of Knowledge © 2009 by DAMA International
41
Copyright 2013 by Data Blueprint
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
from The DAMA Guide to the Data Management Body of Knowledge © 2009 by DAMA International
42
Copyright 2013 by Data Blueprint
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
from The DAMA Guide to the Data Management Body of Knowledge © 2009 by DAMA International
43
Copyright 2013 by Data Blueprint
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
from The DAMA Guide to the Data Management Body of Knowledge © 2009 by DAMA International

44
Copyright 2013 by Data Blueprint
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
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
from The DAMA Guide to the Data Management Body of Knowledge © 2009 by DAMA International

46
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
Source:http://www.mdmsource.com/master-data-management-tips-best-practices.html

47
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.
Source:http://www.mdmsource.com/master-data-management-tips-best-practices.html

48
Copyright 2013 by Data Blueprint
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
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]

50
Copyright 2013 by Data Blueprint
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]

51
Copyright 2013 by Data Blueprint
Seven Sisters (from British Telecom)

http://www.datablueprint.com/thought-leaders/peter-aiken/book-monetizing-data-management/

52
Copyright 2013 by Data Blueprint

Thanks to Dave Evans
Summary:
Reference
and MDM

from The DAMA Guide to the Data Management Body of Knowledge © 2009 by DAMA International
53
Copyright 2013 by Data Blueprint
Questions?

+

=

It’s your turn!
Use the chat feature or Twitter (#dataed) to submit
your questions to Peter now.

54
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References

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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-expertdevises-business-transformation-template

•

http://www.itbusinessedge.com/cm/blogs/lawson/free-report-shows-businesses-fed-upwith-bad-data/?cs=50416

•

http://www.itbusinessedge.com/cm/blogs/lawson/whats-ahead-for-master-datamanagement/?cs=50082

•

http://www.itbusinessedge.com/cm/blogs/vizard/master-data-management-reaches-for-thecloud/?cs=49264

•

http://www.information-management.com/channels/master-data-management.html

•

http://www.dataversity.net/applying-six-sigma-to-master-data-management-mdmframework-for-integrating-mdm-into-ea-part-2/

•

http://www.dataqualityfirst.com/getting_master_data_facts_straight_is_hard.htm

56
Copyright 2013 by Data Blueprint
Upcoming Events
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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 Copyright 2013 by Data Blueprint
  • 3. Get Social With Us! Live Twitter Feed Like Us on Facebook Join the Group Join the conversation! www.facebook.com/ datablueprint Data Management & Business Intelligence Follow us: @datablueprint @paiken Ask questions and submit your comments: #dataed Post questions and comments Ask questions, gain insights Find industry news, insightful and collaborate with fellow data management content professionals and event updates. 3 Copyright 2013 by Data Blueprint
  • 4. Peter Aiken, PhD • • • • • • • • 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 4 Copyright 2013 by Data Blueprint 2
  • 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 Copyright 2013 by Data Blueprint
  • 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 6 Copyright 2013 by Data Blueprint
  • 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 7 Copyright 2013 by Data Blueprint
  • 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 8 Copyright 2013 by Data Blueprint
  • 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. 10 Copyright 2013 by Data Blueprint
  • 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 11 Copyright 2013 by Data Blueprint
  • 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 Copyright 2013 by Data Blueprint
  • 13. Summary: Reference and MDM from The DAMA Guide to the Data Management Body of Knowledge © 2009 by DAMA International 13 Copyright 2013 by Data Blueprint
  • 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). 14 Copyright 2013 by Data Blueprint
  • 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 15 Copyright 2013 by Data Blueprint
  • 16. Reference/Master Data Management • Definition – Planning, implementation and control activities to ensure consistency with a "golden version" of contextual data values. from The DAMA Guide to the Data Management Body of Knowledge © 2009 by DAMA International 16 Copyright 2013 by Data Blueprint
  • 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. 17 Copyright 2013 by Data Blueprint
  • 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. 18 Copyright 2013 by Data Blueprint
  • 19. 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 from The DAMA Guide to the Data Management Body of Knowledge © 2009 by DAMA International 19 Copyright 2013 by Data Blueprint
  • 20. 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 • From the term Master File from The DAMA Guide to the Data Management Body of Knowledge © 2009 by DAMA International 20 Copyright 2013 by Data Blueprint
  • 21. 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 from The DAMA Guide to the Data Management Body of Knowledge © 2009 by DAMA International 21 Copyright 2013 by Data Blueprint
  • 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 Copyright 2013 by Data Blueprint
  • 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 23 Copyright 2013 by Data Blueprint
  • 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 24 Copyright 2013 by Data Blueprint
  • 25. Interdependencies Data Governance Data Quality Master Data 25 Copyright 2013 by Data Blueprint
  • 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 26 Copyright 2013 by Data Blueprint
  • 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) 28 Copyright 2013 by Data Blueprint
  • 29. Vocabulary is Important-Tank, Tanks, Tankers, Tanked 29 Copyright 2013 by Data Blueprint
  • 30. Reference Data Architecture from The DAMA Guide to the Data Management Body of Knowledge © 2009 by DAMA International 30 Copyright 2013 by Data Blueprint
  • 31. Master Data Architecture 31 Copyright 2013 by Data Blueprint
  • 32. Combined R/M Data Architecture 32 Copyright 2013 by Data Blueprint
  • 33. "180% Failure Rate" Fred Cohen, Patni http://www.igatepatni.com/bfs/solutions/payments.aspx Copyright 2013 by Data Blueprint 33
  • 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 34 Copyright 2013 by Data Blueprint
  • 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 35 Copyright 2013 by Data Blueprint
  • 37. Prius Hybrid Engine 37 Copyright 2013 by Data Blueprint
  • 38. 38 Copyright 2013 by Data Blueprint
  • 39. Goals and Principles 1. Provide authoritative source of reconciled, highquality master and reference data. 2. Lower cost and complexity through reuse and leverage of standards. 3. Support business intelligence and information integration efforts. from The DAMA Guide to the Data Management Body of Knowledge © 2009 by DAMA International 39 Copyright 2013 by Data Blueprint
  • 40. 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 from The DAMA Guide to the Data Management Body of Knowledge © 2009 by DAMA International 40 Copyright 2013 by Data Blueprint
  • 41. 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? from The DAMA Guide to the Data Management Body of Knowledge © 2009 by DAMA International 41 Copyright 2013 by Data Blueprint
  • 42. 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 from The DAMA Guide to the Data Management Body of Knowledge © 2009 by DAMA International 42 Copyright 2013 by Data Blueprint
  • 43. 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 from The DAMA Guide to the Data Management Body of Knowledge © 2009 by DAMA International 43 Copyright 2013 by Data Blueprint
  • 44. 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 from The DAMA Guide to the Data Management Body of Knowledge © 2009 by DAMA International 44 Copyright 2013 by Data Blueprint
  • 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
  • 46. 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 from The DAMA Guide to the Data Management Body of Knowledge © 2009 by DAMA International 46 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 47 Copyright 2013 by Data Blueprint
  • 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] 50 Copyright 2013 by Data Blueprint
  • 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] 51 Copyright 2013 by Data Blueprint
  • 52. Seven Sisters (from British Telecom) http://www.datablueprint.com/thought-leaders/peter-aiken/book-monetizing-data-management/ 52 Copyright 2013 by Data Blueprint Thanks to Dave Evans
  • 53. Summary: Reference and MDM from The DAMA Guide to the Data Management Body of Knowledge © 2009 by DAMA International 53 Copyright 2013 by Data Blueprint
  • 54. Questions? + = It’s your turn! Use the chat feature or Twitter (#dataed) to submit your questions to Peter now. 54 Copyright 2013 by Data Blueprint
  • 56. 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-expertdevises-business-transformation-template • http://www.itbusinessedge.com/cm/blogs/lawson/free-report-shows-businesses-fed-upwith-bad-data/?cs=50416 • http://www.itbusinessedge.com/cm/blogs/lawson/whats-ahead-for-master-datamanagement/?cs=50082 • http://www.itbusinessedge.com/cm/blogs/vizard/master-data-management-reaches-for-thecloud/?cs=49264 • http://www.information-management.com/channels/master-data-management.html • http://www.dataversity.net/applying-six-sigma-to-master-data-management-mdmframework-for-integrating-mdm-into-ea-part-2/ • http://www.dataqualityfirst.com/getting_master_data_facts_straight_is_hard.htm 56 Copyright 2013 by Data Blueprint
  • 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: 57 Copyright 2013 by Data Blueprint