Data-Ed Online: Unlock Business Value through Reference & MDM

Unlock Business Value
Through Reference & Master Data Management

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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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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

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• 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
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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

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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Summary:
Reference
and MDM

from The DAMA Guide to the Data Management Body of Knowledge © 2009 by DAMA International
13

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

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

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
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

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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

20

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
21



Tweeting now:
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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

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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Interdependencies
Data Governance

Data Quality

Master Data

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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

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
27

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

Vocabulary is Important-Tank, Tanks, Tankers, Tanked

29

Reference Data Architecture

30

Master Data Architecture

31

Combined R/M Data Architecture

32

"180% Failure Rate" Fred Cohen, Patni

http://www.igatepatni.com/bfs/solutions/payments.aspx

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

34

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

Traditional Engine

36

Prius Hybrid Engine

37

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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.

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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
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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?
41

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
42

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
43

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

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Tweeting now:
#dataed
45

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

46

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

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

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Tweeting now:
#dataed
49

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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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

Seven Sisters (from British Telecom)

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

52

Thanks to Dave Evans

Summary:
Reference
and MDM

53

Questions?

+

=

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

54

References

55

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

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