Data-Ed Online: MDM: Quality is not an Option but a Requirement

Welcome!
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MDM: Quality is Not an Option
but a Requirement

Date: June 12, 2012
Time: 2:00 PM ET
Presenter: Dr. Peter Aiken
Twitter: #dataed

PRODUCED BY CLASSIFICATION DATE SLIDE
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06/06/12 © Copyright this and previous years by Data Blueprint - all rights reserved!

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Meet Your Presenter: Dr. Peter Aiken
• Internationally recognized thought-leader in
the data management field with more than 30
years of experience
• Recipient of the 2010 International Stevens
Award
• Founding Director of Data Blueprint
(http://datablueprint.com)
• Associate Professor of Information Systems
at Virginia Commonwealth University
(http://vcu.edu)

• President of DAMA International (http://dama.org)
• DoD Computer Scientist, Reverse Engineering Program Manager/
Office of the Chief Information Officer
• Visiting Scientist, Software Engineering Institute/Carnegie Mellon
University
• 7 books and dozens of articles
• Experienced w/ 500+ data management practices in 20 countries
#dataed

MDM:
Text

Integration with Quality and
Governance is not an
Option but a Requirement

Dr. Peter Aiken: Reference & Master Data Management Webinar
DATA BLUEPRINT 10124-C W. BROAD ST, GLEN ALLEN, VA 23060 EDUCATION 06/12/12

Outline
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1. Data Management Overview
2. What is Reference and MDM?
3. Why is Reference and MDM
important?
4. Reference & MDM Building
Blocks
5. Guiding Principles & Best
Practices
6. Take Aways, References & Tweeting now:
#dataed
Q&A

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The DAMA Guide to the Data Management Body of Knowledge

Data Management Functions

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Published by DAMA
International


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Published by DAMA
International
• The professional
association for Data
Managers (40
chapters worldwide)


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Published by DAMA
International
Managers (40
chapters worldwide)
DMBoK organized
around


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Published by DAMA
International
Managers (40
chapters worldwide)
DMBoK organized
around
• Primary data
management
functions focused
around data delivery
to the organization


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Published by DAMA
International
Managers (40
chapters worldwide)
DMBoK organized
around
• Primary data
management
functions focused
around data delivery
to the organization
• Organized around
several
environmental
elements


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Environmental Elements

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Amazon:
http://www.amazon.com/
DAMA-Guide-
Management-Knowledge-
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?

#dataed

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What is the CDMP?
• Certified Data Management
Professional

#dataed

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What is the CDMP?
Professional
• DAMA International and ICCP

#dataed

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What is the CDMP?
Professional
• Membership in a distinct group made
up of your fellow professionals

#dataed

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What is the CDMP?
Professional
• Recognition for your specialized
knowledge in a choice of 17 specialty
areas

#dataed

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What is the CDMP?
Professional
areas
• Series of 3 exams

#dataed

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What is the CDMP?
Professional
areas
• For more information, please visit:

#dataed

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What is the CDMP?
Professional
areas
– http://www.dama.org/i4a/pages/
index.cfm?pageid=3399

#dataed

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What is the CDMP?
Professional
areas
– http://www.dama.org/i4a/pages/
index.cfm?pageid=3399
– http://iccp.org/certification/
designations/cdmp
#dataed

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Data Management Basics


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Data Program
Coordination

Organizational
Data Integration

Data Stewardship Data Development

Data Support
Operations


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Manage data coherently.

Data Program
Coordination

Organizational
Data Integration


Data Support
Operations


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Data Program
Coordination
Share data across boundaries.
Organizational
Data Integration


Data Support
Operations


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Data Program
Coordination
Organizational
Data Integration


Assign responsibilities for data.

Data Support
Operations


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Data Program
Coordination
Organizational
Data Integration


Engineer data delivery systems.

Data Support
Operations


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Data Program
Coordination
Organizational
Data Integration


Engineer data delivery systems.

Data Support
Operations

Maintain data availability.


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Outline
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important?
Blocks
Practices
#dataed
Q&A

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Summary: Reference and MDM

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

MDM Definition
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MDM Definition
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• Gartner holds that MDM is a
discipline or strategy


MDM Definition
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– "… 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."


MDM Definition
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– Master data is the enterprise's official, consistent set
of identifiers, extended attributes and hierarchies.


MDM Definition
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– Examples of core entities are:


MDM Definition
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• Parties (e.g., customers, prospects, people, citizens,
employees, vendors, suppliers and trading partners)


MDM Definition
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• Places (e.g., locations, offices, regional alignments and
geographies) and


MDM Definition
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• Places (e.g., locations, offices, regional alignments and
geographies) and
• Things (for example, accounts, assets, policies, products and
services).


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Wikipedia: Golden Version


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• 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".


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– Often confused with "gold master" which refers
to a physical recording entity such as that sent
to a manufacturing plant.


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– 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.


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Definition: Reference Data Management


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Control over defined domain values (also known as
vocabularies), including:


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• Control over standardized terms, code values and other
unique identifiers;


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unique identifiers;
• Business definitions for each value, business
relationships within and across domain value lists, and
the;


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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.


Definition: Master Data Management
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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


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Master Data


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Master Data
• Data about business entities providing context
for transactions but not limited to pre-defined
values


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Master Data
values

• Business rules dictate format and allowable
ranges


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Master Data
values

ranges
– Parties (individuals, organizations, customers,
citizens, patients, vendors, supplies, business
partners, competitors, employees, students)


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Master Data
values

ranges
– Locations, products, financial structures


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Master Data
values

ranges
– Locations, products, financial structures

• From the term Master File

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Reference Data versus Master Data


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• 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


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• Reference Data:
domain values
(vocabularies) for
standardized terms,
code values, and
other unique
identifiers
maintain 9 possible
gender codes
• Master Data:
– Control over master
data values to enable
consistent, shared,
systems
– The "golden" source
of the gender of your
customer "Pat" from The DAMA Guide to the Data Management Body of Knowledge © 2009 by DAMA International

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• Reference Data:
domain values
(vocabularies) for
standardized terms,
code values, and
other unique
identifiers
maintain 9 possible
gender codes
• Master Data:
– Control over master
data values to enable
consistent, shared,
systems
Both provide the context
– The "golden" source
of the gender of your for transaction data
customer "Pat" from The DAMA Guide to the Data Management Body of Knowledge © 2009 by DAMA International

Outline
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important?
Blocks
Practices
#dataed
Q&A

Reference Data Facts 2012
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Source: http://www.igate.com/22926.aspx

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• Global industry-wide survey
of reference data
professionals
• Results show: Poor quality of
reference data continues to
create major problems for
financial institutions.


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• 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

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Reference Data Facts 2012, cont’d


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• Despite recommended practices of centralizing
reference data operations, 31% of the firms
surveyed still manage data locally


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• 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.

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Polling Question #1
What percentage of firms still manage data locally?

1. 75%
2. 31%
3. 55%
4. 24%


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Interdependencies

Data Governance

Data Quality Master Data


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Inextricably intertwined

Organized Knowledge 'Data' Knowledge
Management
Practices
Routine Data Scans Data Organization Practices
Metadata(Prac8ces((dashed lines not in existence)
( (

Metadata( Metadata( Metadata(
Engineering( Storage( Delivery(
Sources( ( Uses(
Metadata(Governance(

Data that might benefit from
Suspected/ Master Management
Identified
Master Data Catalogs
Data
Quality Master Data
Management
Problems Data Quality Practices
Engineering

Routine Data Scans
Improved Quality Data
Operational Data


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Interactions
Data
Harvesting

Governance Master
Violations Data
Monitoring Data Monitoring
Quality
Routine Monitoring
Data
Scans Governance
Rules
Monitoring
Monitoring Results:
Master
Monitoring Results: Suspected/ Data
Rules Suspected/ Master
Catalogs
Identified Data &
Data Characteristics
Data Quality
Governance Problems Data
Practices Quality
Rules
Quality Focused
Rules Data Quality Master Data Data
Engineering Management Scans
Routine Practices Practices
Data
Scans

Improved Quality Data

Operational Data

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Multiple Sources of (for example) Customer Data
Finance Application
(3rd GL, batch
Payroll Data system, no source)
(database)
Payroll Application Finance
(3rd GL) Data
(indexed)

Marketing Data Marketing Application
(external database) (4rd GL, query facilities,
no reporting, very large)

Personnel Data
(database)

Personnel App.
(20 years old,
un-normalized data) Mfg. Data
R&D
Data (home grown
(raw) database) Mfg. Applications
(contractor supported)
R& D Applications
(researcher supported, no documentation)

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Reference/Master Data: Tank, Tanks, Tankers, Tanked


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Reference Data Architecture



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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

PRODUCED BY http://www.igatepatni.com/bfs/solutions/payments.aspx
CLASSIFICATION DATE SLIDE

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MDM Failure Root-Causes


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• 30% of MDM programs are regarded as failures


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• 70% of SOA projects in complex, heterogeneous environments had
failed to yield the expected business benefits unless MDM is
included


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included
• Root-causes of failures:


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included
– 80% percent of MDM initiatives fail because of ineffective
leadership, underestimated magnitudes or an inability to deal
with the cultural impact of the change


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included
– MDM was implemented as a technology or as a project


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included
– MDM was an Enterprise Data Warehouse (EDW) or an ERP


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included
– MDM was an IT Effort


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included
– MDM is separate to data governance and data quality


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included
– MDM initiatives are implemented with inappropriate technology


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included
– MDM initiatives are implemented with inappropriate technology
– Internal politics and the silo mentality impede the MDM
initiatives

Outline
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important?
Blocks
Practices
#dataed
Q&A

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Automating Business Process


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Benefits


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Benefits
• Obtain holistic perspective on
roles and value creation


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Benefits
• Customers understand and
value outputs


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Benefits
value outputs
• All develop better shared
understanding

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Benefits Results
value outputs
understanding

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Benefits Results
• Obtain holistic perspective on • Speed up process
value outputs
understanding

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Benefits Results
roles and value creation • Cost savings
value outputs
understanding

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Benefits Results
• Customers understand and • Increased compliance
value outputs
understanding

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Benefits Results
value outputs
• Increased output
understanding

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Benefits Results
value outputs
• Increased output
understanding • IT systems documentation


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Traditional Engine


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Prius Hybrid Engine


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Data Security Management Overview

      
      
      
      
      

Illustration from The DAMA Guide to the Data Management Body of Knowledge p. 37 © 2009 by DAMA International

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Goals and Principles


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1. Provide authoritative
source of reconciled,
high-quality master and
reference data.


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reference data.
2. Lower cost and
complexity through reuse
and leverage of
standards.


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reference data.
2. Lower cost and
complexity through reuse
and leverage of
standards.
3. Support business
intelligence and
information integration
efforts.

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Reference & MDM Activities


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• Understand Reference and
Master Data Integration Needs


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• Identify Master and Reference Data
Sources and Contributors


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• Define and Maintain the Data
Integration Architecture


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• Implement Reference and Master
Data Management Solutions


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• Define and Maintain Match Rules


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• Establish “Golden” Records


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• Define and Maintain Hierarchies and Affiliations


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• Plan and Implement Integration of New Data Sources


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• Replicate and Distribute Reference and Master Data


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• Replicate and Distribute Reference and Master Data
• Manage Changes to Reference and Master Data

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Specific Reference and MDM Investigations


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• Who needs what information?


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• What data is available from
different sources?


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different sources?

• How does data from different
sources differ?


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different sources?

sources differ?

• How can inconsistencies
be reconciled?


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different sources?

sources differ?

• How can inconsistencies
be reconciled?

• How should valid values be shared?

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Primary Deliverables


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• Data Cleansing Services


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• Master and Reference
Data Requirements


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Data Requirements
• Data Models and Documentation


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Data Requirements
• Reliable Reference and Master Data


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Data Requirements
• "Golden Record" Data Lineage


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Data Requirements
• "Golden Record" Data Lineage
• Data Quality Metrics and Reports

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Roles and Responsibilities


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Suppliers:
• Steering Committees
• Business Data Stewards
• Subject Matter Experts
• Data Consumers
• Standards Organizations
• Data Providers


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Suppliers: Consumers:
• Steering Committees • Application Users
• Business Data Stewards • BI and Reporting Users
• Application Developers and
• Subject Matter Experts Architects
• Data Consumers • Data integration Developers and
• Standards Organizations Architects
• Data Providers • 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

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Technology


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Technology
• ETL


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Technology
• ETL
• Reference Data Management
Applications


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Technology
• ETL
Applications
• Master Data Management
Applications


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Technology
• ETL
Applications
Applications
• Data Modeling Tools


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Technology
• ETL
Applications
Applications
• Process Modeling Tools


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Technology
• ETL
Applications
Applications
• Meta-data Repositories


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Technology
• ETL
Applications
Applications
• Data Profiling Tools


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Technology
• ETL
Applications
Applications
• Data Cleansing Tools


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Technology
• ETL
Applications
Applications
• Data Integration Tools


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Technology
• ETL
Applications
Applications
• Business Process and Rule Engines


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Technology
• ETL
Applications
Applications
• Business Process and Rule Engines
• Change Management Tools

Outline
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important?
Blocks
Practices
#dataed
Q&A

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Guiding Principles


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Guiding Principles
1. Shared R/M data belong to
the organization.


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Guiding Principles
the organization.
2. R/M data management is an
on-going data quality improve-
ment program – goals cannot
be achieved by 1 project alone.


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Guiding Principles
the organization.
3. Business data stewards are the authorities
accountable at determining the golden values.


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Guiding Principles
the organization.
4. Golden values represent the "best" sources.


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Guiding Principles
the organization.
5. Replicate master data values only from golden
sources.


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Guiding Principles
the organization.
5. Replicate master data values only from golden
sources.
6. Reference data changes require formal change
management

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Polling Question #2
Which of these is a MDM best practice?

1. Build your processes to be static
2. Customize whenever possible
3. Use a holistic approach
4. Test at least once


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10 Best Practices for MDM

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

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1. Active, involved executive
sponsorship


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sponsorship
2. The business should own the data
governance process and the MDM or
CDI project


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sponsorship
CDI project
3. Strong project management and
organizational change management


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sponsorship
CDI project
4. Use a holistic approach - people,
process, technology and information:


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sponsorship
CDI project
4. Use a holistic approach - people,
process, technology and information:
5. Build your processes to be ongoing
and repeatable, supporting
continuous improvement

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10 Best Practices for MDM, cont’d


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6. Management needs to recognize the
importance of a dedicated team of
data stewards


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data stewards

7. Understand your MDM hub's data
model and how it integrates with your
internal source systems and external
content providers


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data stewards

content providers

8. Resist the urge to customize


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data stewards

content providers


9. Stay current with vendor-provided
patches


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data stewards

content providers


9. Stay current with vendor-provided
patches

10. Test, test, test and then test again.

Outline
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important?
Blocks
Practices
#dataed
Q&A

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15 MDM Success Factors

DATA BLUEPRINT 10124-C W. BROAD ST, GLEN ALLEN, VA 23060 [Source: unknown]
EDUCATION 06/12/12 55

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1. Success is more likely and
more frequently observed
once users and prospects
understand the limitations and
strengths of MDM.


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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.


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strengths of MDM.
the MDM market and
with MDM.
3. Set the right expectations for
MDM initiative to help assure
long-term success.


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strengths of MDM.
the MDM market and
with MDM.
long-term success.
4. Long-term MDM success
requires the involvement of
the information architect.


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1. Success is more likely and 5. Create a governance
more frequently observed framework to ensure that
once users and prospects individuals manage master
understand the limitations and data in a desirable manner.
strengths of MDM.
the MDM market and
with MDM.
long-term success.


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strengths of MDM. 6. Strong alignment with the
2. Taking small steps and organization's business vision,
remaining educated on where demonstrated by measuring
the MDM market and the program's ongoing value,
technology vendors are will will underpin MDM success.
with MDM.
long-term success.


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strengths of MDM. 6. Strong alignment with the
2. Taking small steps and organization's business vision,
remaining educated on where demonstrated by measuring
the MDM market and the program's ongoing value,
technology vendors are will will underpin MDM success.
increase longer-term success 7. Use a strategic MDM
with MDM. framework through all stages
3. Set the right expectations for of the MDM program activity
MDM initiative to help assure cycle — strategize, evaluate,
long-term success. execute and review.


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8. Gain high-level business
sponsorship for the MDM
program, and build strong
stakeholder support.


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9. Start by creating an MDM
vision and a strategy that
closely aligns to the
organization’s business
vision.


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vision.
10. Use an MDM metrics
hierarchy to communicate
standards for success, and to
objectively measure
progress.


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vision.
objectively measure
progress.
11. Use a business case
development process to
increase business
engagement.

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8. Gain high-level business 12. Get the business to propose
sponsorship for the MDM and own the KPIs; articulate
program, and build strong the success of this scenario.
vision.
objectively measure
progress.
increase business
engagement.

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stakeholder support. 13. Measure the situation before
9. Start by creating an MDM and after the MDM
vision and a strategy that implementation to determine
closely aligns to the the change.
vision.
objectively measure
progress.
increase business
engagement.

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organization’s business 14. Translate the change in
vision. metrics into financial results.
objectively measure
progress.
increase business
engagement.

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organization’s business 14. Translate the change in
vision. metrics into financial results.
10. Use an MDM metrics 15. The business and IT
hierarchy to communicate organization should work
standards for success, and to together to achieve a single
objectively measure view of master data.
progress.
increase business
engagement.

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Seven Sisters from British Telecom

Thanks to Dave Evans

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Summary: Reference and MDM


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Questions?

+ =

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


TITLE
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-
expert-devises-business-transformation-template
• http://www.itbusinessedge.com/cm/blogs/lawson/free-report-shows-businesses-fed-up-
with-bad-data/?cs=50416
• http://www.itbusinessedge.com/cm/blogs/lawson/whats-ahead-for-master-data-
management/?cs=50082
• http://www.itbusinessedge.com/cm/blogs/vizard/master-data-management-reaches-
for-the-cloud/?cs=49264
• http://www.information-management.com/channels/master-data-management.html
• http://www.dataversity.net/applying-six-sigma-to-master-data-management-mdm-
framework-for-integrating-mdm-into-ea-part-2/
• http://www.dataqualityfirst.com/getting_master_data_facts_straight_is_hard.htm


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Data-Ed Online: MDM: Quality is not an Option but a Requirement

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Data-Ed Online: MDM: Quality is not an Option but a Requirement

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