The Importance of Master Data Management

The Importance of Reference & MDM

Eternal Management of the Data Mind

Peter Aiken, Ph.D.

Copyright 2018

by Data Blueprint

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• DAMA International President 2009-2013

• DAMA International Achievement Award 2001 (with Dr. E. F. "Ted" Codd

• DAMA International Community Award 2005

Peter Aiken, Ph.D.• 33+ years in data management • Repeated international recognition • Founder, Data Blueprint (datablueprint.com) • Associate Professor of IS (vcu.edu) • DAMA International (dama.org) • 10 books and dozens of articles • Experienced w/ 500+ data

management practices • Multi-year immersions:

– US DoD (DISA/Army/Marines/DLA)– Nokia – Deutsche Bank– Wells Fargo – Walmart– …

2Copyright 2018

by Data Blueprint

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Data Assets Win!

Data Assets

Financial Assets

RealEstate Assets

Inventory Assets

Non-depletable

Available for subsequent

use

Can be used up

Can be used up

Non-degrading √ √ Can degrade

over timeCan degrade

over time

Durable Non-taxed √ √

Strategic Asset √ √ √ √

Data Assets Win!• Today, data is the most powerful, yet underutilized and poorly

managed organizational asset • Data is your

– Sole – Non-depletable – Non-degrading – Durable – Strategic

• Asset – Data is the new oil! – Data is the new (s)oil! – Data is the new bacon!

• As such, data deserves: – It's own strategy – Attention on par with similar organizational assets – Professional ministration to make up for past neglect

3Copyright 2018 by Data Blueprint Slide #

Asset: A resource controlled by the organization as a result of past events or transactions and from which future economic benefits are expected to flow [Wikipedia]

Copyright 2013 by Data Blueprint

• 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

Unlocking Business Value Through Reference & Master Data Management

4

UsesUsesReuses

What is data management?


Sources

Data Engineering

Data Delivery

Data

Storage

Specialized Team Skills

Data Governance

Understanding the current and future data needs of an enterprise and making that data effective and efficient in supporting business activitiesAiken, P, Allen, M. D., Parker, B., Mattia, A., "Measuring Data Management's Maturity: A Community's Self-Assessment" IEEE Computer (research feature April 2007)

Data management practices connect data sources and uses in an organized and efficient manner • Engineering • Storage • Delivery • Governance

When executed, engineering, storage, and delivery implement governance

Note: does not well-depict data reuse

Data Management


Sources

Data Engineering

Data Delivery

Data

Storage


Resources

(optimized for reuse)

Data Governance

Ana

lytic

Insi

ght


Copyright 2018

by Data Blueprint

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Maslow's Hierarchy of Needs

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You can accomplish Advanced Data Practices without becoming proficient in the Foundational Data Management Practices however this will: • Take longer • Cost more • Deliver less • Present

greaterrisk(with thanks to Tom DeMarco)

Data Management Practices Hierarchy

Advanced Data

Practices • MDM • Mining • Big Data • Analytics • Warehousing • SOA

Foundational Data Management Practices

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Data Platform/Architecture

Data Governance Data Quality

Data Operations

Data Management Strategy

Technologies

Capabilities

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DMM℠ Structure of 5 Integrated DM Practice Areas

Data architecture implementation

Data Governanc

e

Data Manageme

ntStrategy

Data Operations

PlatformArchitectur

e

SupportingProcesses

Maintain fit-for-purpose data, efficiently and effectively

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Manage data coherently

Manage data assets professionally

Data life cycle management

Organizational support

Data Quality

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Data Management Strategy is often the weakest link

Data architecture implementation

Data Governanc

e

Data Manageme

ntStrategy

Data Operations

PlatformArchitectur

e

SupportingProcesses

Maintain fit-for-purpose data, efficiently and effectively

10Copyright 2018

by Data Blueprint

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Manage data coherently

Manage data assets professionally

Data life cycle management

Organizational support

Data Quality

3 3

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The DAMA Guide to the Data Management Body of Knowledge

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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


Summary: Reference and MDM

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









13

+ 1 Year

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• Confusion as to the system's value – Users lack confidence – Business did not know how to use

"the MDM"

• General agreement – Restart the effort

• "Root cause" analysis – Consensus – Poor quality data

• Response – Get data quality-ing!

• Inexperienced – Immature data quality practices – Tool/technological focus – Purchased a data quality tool

– as opposed to mobile device management

• Gartner holds that MDM is a discipline – "… 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"

• Sold as solution • Official, consistent set of identifiers - examples of these core

entities include: – Parties (customers, prospects, people, citizens, employees, vendors, suppliers,

trading partners, individuals, organizations, citizens, patients, vendors, supplies, business partners, competitors, students, products, financial structures *LEI*)

– Places (locations, offices, regional alignments, geographies) – Things (accounts, assets, policies, products, services)

• Provide context for transactions • From the term "Master File"

Master Data Management Definition

15Copyright 2018 by Data Blueprint

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

• Definition-Reference/Master Data Management

– Planning, implementation and control activities to ensure consistency with a "golden version" of contextual data values.


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.



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

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US United States

GB (not UK) United Kingdom



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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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

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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

• 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









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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

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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.


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.

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


Interdependencies

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Data Governance

Master Data Data Quality

interdependencies


Data Governance

Master Data Data Quality

makes the case and is

responsible for

is a necessary but insufficient prerequisite

to success

MD capabilities constrain governance

effectiveness

Solution Framework


SORs

SOR 1

SOR 2

SOR 3

SOR 4

SOR 5

SOR 6

SOR 7

SOR 8

Repository

IndicatorExtraction

Service (could be

segmented byday of week

month, system, etc.)

UpdateAddresses

LatencyCheckService

Ch 1

Ch 2

Ch 3

Ch 4

Ch 5

Ch 6

Channels

Ch 7

Ch 8

External Address Validation Processing

CustomerContact


Inextricably intertwined

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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(


Interactions

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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


Payroll Application(3rd GL)Payroll Data

(database)

R& D Applications(researcher supported, no documentation)

R & D Data (raw) Mfg. Data

(home grown database)

Mfg. Applications(contractor supported)

Finance

Data (indexed)

Finance Application(3rd GL, batch

system, no source)

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

Marketing Data

(external database)

Personnel App.(20 years old,

un-normalized data)

Personnel Data

(database)

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Multiple Sources of (for example) Customer Data


Vocabulary is Important-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

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


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

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Automating Business Process Discovery (qpr.com)

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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


Traditional Engine

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Prius Hybrid Engine

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Copyright 2013 by Data Blueprint 40

MDM Business Process Overview


Attributed to Steven Steinerman









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Goals and Principles

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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.



Reference & MDM Activities

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• 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


Specific Reference and MDM Investigations

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• 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?


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

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Roles and Responsibilities

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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



Technology

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• 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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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

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10 Best Practices for MDM1. 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

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


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.

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









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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.

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[Source: unknown]


15 MDM Success Factors

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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]

Seven Sisters (from British Telecom)

http://www.datablueprint.com/thought-leaders/peter-aiken/book-monetizing-data-management/ [Thanks to Dave Evans]



Summary: Reference and MDM

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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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