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TITLE PRODUCED BY DATA BLUEPRINT 10124-C W. BROAD ST, GLEN ALLEN, VA 23060 CLASSIFICATION EDUCATION DATE SLIDE 06/12/12 06/06/12 © Copyright this and previous years by Data Blueprint - all rights reserved! Welcome! Date: June 12, 2012 Time: 2:00 PM ET Presenter: Dr. Peter Aiken Twitter: #dataed 1 MDM: Quality is Not an Option but a Requirement

MDM and Data Quality: Not an Option but a Requirement

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Page 1: MDM and Data Quality: Not an Option but a Requirement

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Welcome!

Date: June 12, 2012Time: 2:00 PM ETPresenter: Dr. Peter AikenTwitter: #dataed

1

MDM: Quality is Not an Option but a Requirement

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Commonly Asked Questions1) Will I get copies of the slides after the

event?

YES*

2) Is this being recorded so I can view it afterwards?

YES*

*Standard registrations

2

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New Features: Live Twitter Feed & Facebook

Join the conversation on Twitter!

Follow us @datablueprint and @paiken

Ask questions and submit your comments: #dataed

3

www.facebook.com/datablueprint

Post questions and comments

Find industry news, insightful content

and event updates

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Meet Your Presenter: Dr. Peter Aiken

4

• 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

Page 5: MDM and Data Quality: Not an Option but a Requirement

06/12/12DATA BLUEPRINT 10124-C W. BROAD ST, GLEN ALLEN, VA 23060 EDUCATION

Dr. Peter Aiken: Reference & Master Data Management Webinar

Text MDM:

Integration with Quality and Governance is not an

Option but a Requirement

Page 6: MDM and Data Quality: Not an Option but a Requirement

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Outline

1. Data Management Overview2. What is Reference and MDM?3. Why is Reference and MDM

important? 4. Reference & MDM Building

Blocks5. Guiding Principles & Best

Practices6. Take Aways, References &

Q&A

6

Tweeting now: #dataed

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The DAMA Guide to the Data Management Body of Knowledge

7

Data Management Functions

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

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The DAMA Guide to the Data Management Body of Knowledge

8

Environmental Elements

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

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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

9

#dataed

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Data Management Basics

10

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Data Management Basics

11

Manage data coherently.

Share data across boundaries.

Assign responsibilities for data.Engineer data delivery systems.

Maintain data availability.

Data Program Coordination

Organizational Data Integration

Data Stewardship Data Development

Data Support Operations

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Data Management Basics

12

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Outline

1. Data Management Overview2. What is Reference and MDM?3. Why is Reference and MDM

important? 4. Reference & MDM Building

Blocks5. Guiding Principles & Best

Practices6. Take Aways, References &

Q&A

13

Tweeting now: #dataed

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Summary: Reference and MDM

14

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

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15

• 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).

MDM Definition

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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

16

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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

17

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Definition: Reference Data ManagementControl 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.

18

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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.

19

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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

20

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

21

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

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Reference Data versus Master Data

22

• 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"

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

Both provide the context for transaction data

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Outline

1. Data Management Overview2. What is Reference and MDM?3. Why is Reference and MDM

important? 4. Reference & MDM Building

Blocks5. Guiding Principles & Best

Practices6. Take Aways, References &

Q&A

23

Tweeting now: #dataed

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Reference Data Facts 2012

• 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

24

Source: http://www.igate.com/22926.aspx

• 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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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 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.

25

Source: http://www.igate.com/22926.aspx

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

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

26

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Interdependencies

27

Data Governance

Master Data Data Quality

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Inextricably intertwined

28

Organized Knowledge 'Data'

Improved Quality Data

Data Organization Practices

Operational Data

Data Quality Engineering

Master Data Management

Practices

Suspected/ Identified

Data Quality

Problems

Routine Data Scans

Master Data Catalogs

Routine Data Scans

Knowledge Management

Practices

Data that might benefit from Master Management

Sources( (Metadata(Governance(

(

Metadata(Engineering(

(

Metadata(Delivery( Uses(

Metadata(Prac8ces((dashed lines not in existence)

Metadata(Storage(

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Interactions

29

Improved Quality Data

Master Data

Monitoring

Data Governance

Practices

Master Data Management

Practices

Governance Violations Monitoring

Data Quality Engineering

Practices

Data Quality

Monitoring

Monitoring Results:

Suspected/ Identified

Data Quality

Problems Data Quality Rules

Monitoring Results:

Suspected/ Master Data &

Characteristics

Routine Data

Scans

Master Data

Catalogs

Governance Rules

Routine Data

Scans

Monitoring Rules

Focused Data

Scans

Operational Data

Data Harvesting

Quality Rules

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R& D Applications(researcher supported, no documentation)

Finance Application(3rd GL, batch system, no source)

Payroll Application(3rd GL)

Payroll Data(database)

FinanceData

(indexed)

Personnel Data(database)

R & DData(raw)

Mfg. Data(home grown

database) Mfg. Applications(contractor supported)

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

Marketing Data(external database)

Personnel App.(20 years old,

un-normalized data)

30

Multiple Sources of (for example) Customer Data

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

31

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Reference Data Architecture

32

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

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Master Data Architecture

33

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Combined R/M Data Architecture

34

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"180% Failure Rate" Fred Cohen, Patni

35http://www.igatepatni.com/bfs/solutions/payments.aspx

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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

36

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Outline

1. Data Management Overview2. What is Reference and MDM?3. Why is Reference and MDM

important? 4. Reference & MDM Building

Blocks5. Guiding Principles & Best

Practices6. Take Aways, References &

Q&A

37

Tweeting now: #dataed

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Automating Business Process

38

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

39

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Prius Hybrid Engine

40

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Data Security Management Overview

42

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

üü ü üü ü ü

üü ü üü ü ü

üü ü üü ü ü

üü ü üü ü ü

üü ü üü ü ü

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Goals and Principles

43

1. Provide authoritative source of reconciled, high-quality 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

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Reference & MDM Activities

44

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

• 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

45

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

• 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?

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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

46

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

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Roles and Responsibilities

47

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

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

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

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Technology

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

• 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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Outline

1. Data Management Overview2. What is Reference and MDM?3. Why is Reference and MDM

important? 4. Reference & MDM Building

Blocks5. Guiding Principles & Best

Practices6. Take Aways, References &

Q&A

49

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Guiding Principles1. Shared R/M data belong to

the organization.2. R/M data management is an

on-going data quality improve-ment 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

50

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

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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 possible3. Use a holistic approach 4. Test at least once

51

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10 Best Practices for MDM1. Active, involved executive

sponsorship2. 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

52

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

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10 Best Practices for MDM, cont’d6. 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.

53

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

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Outline

1. Data Management Overview2. What is Reference and MDM?3. Why is Reference and MDM

important? 4. Reference & MDM Building

Blocks5. Guiding Principles & Best

Practices6. Take Aways, References &

Q&A

54

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15 MDM Success Factors1. 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.

55[Source:    unknown]

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15 MDM Success Factors

56

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.

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Seven Sisters from British Telecom

57Thanks  to  Dave  Evans

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Summary: Reference and MDM

58

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

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Questions?

59

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

your questions to Peter now.

+ =

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References

60

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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

61

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Upcoming Events

62

July Webinar:Practical Applications for Data Warehousing, Analytics, BI, and Meta-Integration TechnologiesJuly 10, 2012 @ 2:00 PM ET/11:00 AM PT

August Webinar:Your Documents and Other Content: Managing Unstructured DataAugust 14, 2012 @ 2:00 PM ET/11:00 AM PT

Sign up here:• www.datablueprint.com/webinar-schedule • www.Dataversity.net

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