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

# 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

# 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

# 5

Data Quality Is A Key Success

Factor for Migration

Develop

Integration

Strategy

Technology

Standards

Data Quality Standards

Applications

Integrations

Inventory

Choose

Tool Set

Integration

Accountabilities

Data Model

Standards

Map to

Reference

Models

Integration

Tools

Survey

Choose

Reference

Models

Build

&

Depl

oy

Support &

Maintenance

Choose

Dev/Support

Model

Hire, Mentor, Train Staff across Enterprise

Deploy Data Quality Standards Policies (Globally)

While Managing Data Quality (Locally)þ

Phase In Approach for Integration Inventory

Install Integration Automation Tools

As organization is able to support them

Integration

Platform

# 6

1. Data Quality Issues &

Migration

• “Lack of cross-organizational communication and consultation has its consequences

1. A lack of cross-organizational data governance structures, policy-making, risk calculation or data asset appreciation, causing a disconnect between business goals and IT programs.

2. Governance policies are not linked to structured requirements gathering, forecasting and reporting.

3. Risks are not addressed from a lifecycle perspective with common data repositories, policies, standards and calculation processes.

4. Metadata and business glossaries are not used as to track data quality, bridge semantic differences and demonstrate the business value of data.

5. Few technologies exist today to assess data values, calculate risk and support the human process of governing data usage in an enterprise.

6. Controls, compliance and architecture are deployed before long-term consequences are modeled.(1)”

# 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

# 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

# 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

# 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

# 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 cross-organizational collaboration and structured policy-making. “ (1)‏

# 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

# 13

Data Management Roles

I I I I C I Fiduciary,

Compliance

Management

**

I C R A R A C I Technology

Service

Group

C I C I I I C I Indirect

Stakeholders

R A R A C C R A Primary

Process

Owner

Application

Management

Information &

Data Access

Cross-

Application

Integration

Database

Management

Data Quality

Management Role >

Group

Legend: R=Responsible; A=Accountable; C=Consult; I=Inform

** - Required for any repositories that have or provide a financial component

# 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

# 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

# 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

# 17

Data Quality Standards

• Working Principles

– People, Resources, Funding, Governance

• Standards by Repository

– Comprehensive, focused, automatable, simple to

deploy

• Establish a MDM Glossary

# 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

# 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

# 20

Data Quality Dashboard

Data Quality by Type of Issue

Entry, Val'n13%

Coding7%

Obsolete53%

Dup/Rep/Merge20%

Archive7%

# 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

# 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 time-to-market reaction times

# 23

The Data Quality Impact Wave

0

500

1000

1500

2000

2500

Design Build Test GoLive

Errors

Effort

Program Mods

Days Available

Tsunami

0

500

1000

1500

2000

2500

Design Build Test Go Live

Investing Late forces programmatic or manual intervention

Investing Early reduces effort at time critical Go Live date

# 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

# 25

Next Steps – MDM Sessions with

Customer focus Title Presenter Date & Time

#1683 – Building a Practical Strategy for

managing Data Quality

Alexander Fiteni

Fiteni International,

L.L.C

May 6, 2009

11:00 AM – 12:00 PM

#2762 - Rapid ROI with Oracle Master Data

Management for Oracle E-Business Suite

customers

Pascal Laik

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May 6, 2009

01:30 PM – 02:30 PM

#2251 - Master Data Management for ERP

Suites

Bill Swanton

AMR Research

May 6. 2009

03:15 PM – 04:15 PM

#2911 – Re-Introducing Oracle Customer to an

Organization, Customer Data Management

Tanya Andghuladze

Forsythe Technology

May 6, 2009

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

Sun Microsystems

May 7, 2009

08:30 AM – 09:30 PM

#1660 – Top 10 Mistakes Companies make in

forming Enterprise Data Governance

William McKnight

Lucidity Consulting

Group

May 7, 2009

09:45 AM – 10:45 PM

#2378 – Customer Intelligence: Proactive

Approaches to Cleanup and Maintaining

Customer Master Data

Rita Popp

Jibe Consulting, Inc.

May 7, 2009

11:00 AM – 12:00 PM

#1845 – Customer Data Hub - Process Controls

of the Customer Registration - Do you have them

in Place?

Sandeep Kumar

Potlapally

May 7, 2009

12:15 PM – 01:15 PM

# 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.

• Members can post their questions, comments, etc., by sending an email to cdmsig@yahoogroups.com. You will have to become member in order to post to this forum.

# 27

Q&A

• 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