The document outlines strategies for managing data quality within master data management (MDM) initiatives, emphasizing the importance of prioritizing data quality throughout migration processes. It highlights common data quality issues, critical success factors, and best practices for effective MDM, while also discussing the role of data governance and the integration of global and local data quality teams. The presentation concludes with lessons learned and next steps for implementing robust data quality management strategies.

1. Master Data Management
Strategies – Data Quality
Building a Practical Strategy for Managing Data Quality
Alex Fiteni CMA, Fiteni International LLC
http://www.fiteni.com
http://blog.fiteni.com
Presentation # 1683

2. Alex Fiteni CMA
• Alex Fiteni CMA is a professional accountant whose
career include comptrollership, business process
improvement and business software development.
• Alex is currently provides professional services in
Master Data Management, ERP implementations,
Project Management and Transaction Based Taxes.
• Recent projects include:
– R12 MDM Strategy, Data Quality & Data Conversion
– Oracle E-Business Tax implementation for Canada
– R12 Oracle E-Business Suite Solution Architecture & Project
Plan Implementation
#2

3. Topic Overview
•
When global MDM strategies are implemented,
Data Quality is often a low priority until conversion is
at hand. Here is a practical approach to making
data quality a central theme of your migration
strategy.
1. Identify the data quality issues facing enterprise during
migration to a central master data hub
2. Define the critical success factors of a well crafted data
quality strategy during migration
3. Provide insights into building the business case to ensure
data quality is a priority
4. Recap Lessons Learned
#3

4. What is Master Data Management?
• “is application
infrastructure (not a
data warehouse,
enterprise application,
data integration or
middleware), designed
to manage master
data and provide it to
applications via
business services. “ (1)‫‏‬
• Customers (and
prospects)‫‏‬
• Products (new, current,
obsolete)
• Suppliers (prospective
and current)
• Future, Present and
Past Employees,
Contractors, Retirees
• Research
• Tangible Assets & IPR
#4

5. Data Quality Is A Key Success
Factor for Migration
Applications
Integrations
Inventory
Technology
Standards
Develop
Integration
Strategy
Data Model
Standards
Data Quality
Standards
Integration
Tools
Survey
Choose
Tool Set
Choose
Reference
Models
Phase In Approach for Integration Inventory
Install Integration Automation Tools
As organization is able to support them
Integration
Platform
Choose
Dev/Support
Model
Integration
Accountabilities
Map to
Reference
Models
Build
&
Depl
oy
Support &
Maintenance
Hire, Mentor, Train Staff across Enterprise
Deploy Data Quality Standards Policies (Globally)
While Managing Data Quality (Locally)þ
#5

6. 1. Data Quality Issues &
Migration
•
1.
2.
3.
4.
5.
6.
“Lack of cross-organizational communication and consultation has its
consequences
A lack of cross-organizational data governance structures, policymaking, risk calculation or data asset appreciation, causing a
disconnect between business goals and IT programs.
Governance policies are not linked to structured requirements
gathering, forecasting and reporting.
Risks are not addressed from a lifecycle perspective with common data
repositories, policies, standards and calculation processes.
Metadata and business glossaries are not used as to track data quality,
bridge semantic differences and demonstrate the business value of
data.
Few technologies exist today to assess data values, calculate risk and
support the human process of governing data usage in an enterprise.
Controls, compliance and architecture are deployed before long-term
consequences are modeled.(1)”
#6

7. Master Data Quality - Problem
• Lack of a clear mandate to change the
current situation
– No clear business accountability
– Ownership vs stewardship – is it an IT
issue only?
• Lack of understanding of the issue on a
global basis
– Lack of a process to address the issues
locally or globally
• Merging the master data repositories
adds a new level of complexity
#7

8. Consistency Issues in DQM
Practices
• Agreeing to disagree
– Supplier Name and Address standards different from Customer
standards
• Suppliers, employees and customers often have multiple contact roles,
so ensuring cross-repository standards reduces the error correction
costs
• Product Names in local language
– Global Product Listing managed locally and in each local language,
when 98% of products were the same in every country
• Global companies must set global language based standards, then act
locally to enforce them
• Conversion will clean it Up
– Data conversion is not a panacea for data cleansing activities.
• Leverage human expertise via local data cleansing activities, ad make
them accountable
#8

9. 2. Critical success factors for a
Data Quality Strategy
• Take a Global, Strategic Approach to Master
Data Management and to Data Quality
– Best Practices
– Governance Roles and Responsibilities
– Key Elements of a MDM Quality Program
#9

10. 10 best Practices in MDM
(4)
1 Ensure the active involvement by senior executives, appoint a Data Czar
2 The Business must own the stewardship of its own data throughout the MDM
life cycle, not IT, and not just during the project
3 Any Change Management program must address the Nay Sayers
4 Tie financial and time investments to the end result, not just to the project
outcomes
5 Develop programs that are easy to understand, implement and deploy with
measurable results
6 Make Data Quality a full time job
7 A corporate Data Model is not just a pretty face … it shows where the bodies
are buried
8 What really costs is customization … keep to the basics
9 Plan for at least one upgrade during the implementation
10 Test …Test …Test again
# 10

11. What is Data Governance?
• “Data governance is the orchestration of people,
process and technology to enable an organization to
leverage information as an enterprise asset. Data
governance manages, safeguards, improves and
protects organizational information. Effective data
governance can enhance the quality, availability and
integrity of your data by enabling crossorganizational collaboration and structured policymaking. “ (1)‫‏‬
# 11

12. Why is Data Governance Important?
• Regulatory Compliance
• Corporate Compliance
• Data Quality
–
–
–
–
–
Data Cleansing
Duplicates/replicate data merge
Quality Checks
Initial Load
Coverage to include original, production, test, and
archived data
• Data Provenance and Change Management
# 12

13. Data Management Roles
Role >
Group
Data Quality
Management
Database
Management
CrossApplication
Integration
Information & Application
Data Access
Management
Primary
Process
Owner
RA
C
C
RA
RA
Indirect
Stakeholders
CI
I
I
CI
CI
Technology
Service
Group
CI
RA
RA
C
I
Fiduciary,
Compliance
Management
**
CI
I
I
I
I
Legend: R=Responsible; A=Accountable; C=Consult; I=Inform
** - Required for any repositories that have or provide a financial component
# 13

14. Involvement in the DQM
Process
• The following groups must be involved:
– The Business groups owning master data
– The Compliance groups
– Key users of the master data
– Information Technology, including project team
• Global Data Quality organization
– A Senior Manager for Quality, Compliance, or similar
– A Business Process Lead familiar with the data repository
– An appointed Global Data Quality Lead for the master data repository
– Local Data Quality teams must include key end users from key
departments
– Project support provides a data management Lead for best practices
# 14

15. How do I build an MDM
program?
•
•
•
•
•
Key Elements
Critical Success Factors
Process driven MDM
Build MDM into daily operations
Continuous Improvement programs and MDM
# 15

16. A MDM Quality program
•
Define the MDM Quality Strategy
– Estimate, formulate, and get approval, funding from senior management
– Define Global Master Repositories and Standards for each
•
Establish and Build Global/Local Data Quality teams
– Agree on approach and guidelines
– Engage local teams in Data Quality Initiatives
– Establish a Lean DQM cross-disciplinary team in each Locale
• Define Master Data Quality projects and guidelines
• Review project progress and results
• Post results to Global Master Data Quality dashboard
•
Get IT support
– Leverage the current legacy systems’ capabilities to enforce compliance
– Consider Alerts, triggers where available to monitor post-clean up
compliance
– Dashboards and reports
# 16

17. Data Quality Standards
• Working Principles
– People, Resources, Funding, Governance
• Standards by Repository
– Comprehensive, focused, automatable, simple to
deploy
• Establish a MDM Glossary
# 17

18. Data Quality & Consistency
Rules
• Data Consistency Rules
–
–
–
–
–
–
–
–
•
Object Identifiers – external and internal
Naming conventions for abbreviations, letter cases, suffixes, prefixes, etc.
Special terms(glossary)
Language differences
Search criteria
Date/time stamping across time zones
Manual replication rules
Data Cleansing resources – data content repositories, software, real-time
DQM
Duplicated data within a repository
– Synonyms, short form names
– Numbering
•
Replicated data across Repositories
– Identifying global master reference base
– Defining replication rules
– Building synchronization protocols
# 18

19. Data Quality Principles
– Consistency
• Naming and Numbering Conventions for Primary Identifiers, Proper
Names and Searchable Descriptions
• Classification and Code assignments are current and internally
consistent
– Accuracy
• New or Obsolete Resources are approved by a manager
• The Resource descriptive and control data are reviewed by a colleague
• Run data quality check programs periodically
– Timeliness
• New Resources are added
• Changes are approved quickly
• Old Resources are made obsolete or disabled
# 19

20. Data Quality Dashboard
Data Quality by Type of Issue
Dup/Rep/ Archive
7%
Merge
20%
Entry,
Val'n
13%
Coding
7%
Obsolete
53%
# 20

21. 3. Building the business case
for Data Duality
• Focus on the value of information as a key
strategic investment
• Develop a model with the 4 dimensions of
Data Quality Programs
– Consistency - Standards reduce errors
– Timeliness - Time to Market Value,
– Expertise – Knowledge, Multi-Lingual – the way to
Global/Local Synergy
– Risk – Compliance, Loss and Opportunity
# 21

22. Cost Benefit Profiles
• Reduced costs:
– Errors cost time to correct, but also lost
opportunity due to mis-matching, duplication, etc.
• Increased revenues, market opportunities:
– Increased integration of customer, products
improves insights into buying habits though
improved data mining
• Reduced Inventory, time to market:
– Increased integration of buying habits with supply
chain data reduces waste, inventory, better timeto-market reaction times
# 22

23. The Data Quality Impact Wave
Investing Early reduces effort at time
critical Go Live date
Investing Late forces programmatic or manual
intervention
2500
2500
2000
2000
Errors
Effort
Program Mods
Days Available
Tsunami
1500
1500
1000
1000
500
500
0
0
Design
Build
Test
Go Live
Design Build
Test
Go
Live
# 23

24. Lessons Learned
1. Make Data Quality a formal Key Success Factor of
the overall project
2. Senior management must own and invest in the
data stewardship role
3. Establish DQM Leadership and teams, leverage Six
Sigma and related BPI soft technologies to improve
data quality processes
4. Build Data Quality Standards across organizational
boundaries
5. This is NOT a technology problem, so do not
‘automate a mess’
6. Leverage Data Quality Management technology to
clean and standardize key data repositories
# 24

25. Next Steps – MDM Sessions with
Customer focus
Title
Presenter
Date & Time
#1683 – Building a Practical Strategy for
managing Data Quality
Alexander Fiteni
May 6, 2009
Fiteni International,
L.L.C
11:00 AM – 12:00 PM
#2762 - Rapid ROI with Oracle Master Data
Management for Oracle E-Business Suite
customers
Pascal Laik
May 6, 2009
Oracle
01:30 PM – 02:30 PM
#2251 - Master Data Management for ERP
Suites
Bill Swanton
May 6. 2009
AMR Research
03:15 PM – 04:15 PM
#2911 – Re-Introducing Oracle Customer to an
Organization, Customer Data Management
Tanya Andghuladze
May 6, 2009
Forsythe Technology
04:30 PM – 05:30 P)M
#1499 – The lunatic, the lover & the poet Beyond
Imagining Data Management How to Make
Something of Nothing
Brent Zionic
May 7, 2009
Sun Microsystems
08:30 AM – 09:30 PM
#1660 – Top 10 Mistakes Companies make in
forming Enterprise Data Governance
William McKnight
May 7, 2009
Lucidity Consulting
Group
09:45 AM – 10:45 PM
#2378 – Customer Intelligence: Proactive
Approaches to Cleanup and Maintaining
Customer Master Data
Rita Popp
May 7, 2009
Jibe Consulting, Inc.
11:00 AM – 12:00 PM
# 25

26. CDM SIG – To Become a Member
Do one of
• You can also join CDM SIG from OAUG site at
http://www.oaug.com
• Send a blank email to cdmsig-subscribe@yahoogroups.com
• Go to CDMSIG Yahoo group at
http://groups.yahoo.com/group/cdmsig and click on ‘Join
this Group’:
• Or send an email to mmanda@rhaptech.com expressing
your interest in becoming CDMSIG member.
You will receive membership application in reply. Upon
sending the completed form to mmanda@rhaptech.com,
your membership will be enabled.
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27. Q&A
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•
•
•
•
•
Alex Fiteni CMA
alex@fiteni.com
http://www.fiteni.com
http://blog.fiteni.com
Fiteni International LLC
WHQ:
– Suite 500, 3960 Howard Hughes Pkwy
– Las Vegas, NV,USA 89169
•
•
•
•
Office: 702-990-3869
eFax: 603-590-2598
US Cell: 650-799-5949
CA Cell: 604-902-2782
# 27