Data-Ed: Business Value From MDM

Copyright 2013 by Data Blueprint

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)

•Utilizing reference & MDM in support of business strategy

Date: February 10, 2015 Time: 2:00 PM ET/11:00 AM PT Presenter: Peter Aiken, Ph.D.

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PETER AIKEN WITH JUANITA BILLINGSFOREWORD BY JOHN BOTTEGA

MONETIZINGDATA MANAGEMENT

Unlocking the Value in Your Organization’s

Most Important Asset.

The Case for theChief Data OfficerRecasting the C-Suite to LeverageYour Most Valuable Asset

Peter Aiken andMichael Gorman













Shannon Kempe


Executive Editor at DATAVERSITY.net

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Peter Aiken andMichael GormanPETER AIKEN WITH JUANITA BILLINGS

FOREWORD BY JOHN BOTTEGA




Peter Aiken, Ph.D.• 30+ years of experience in data

management • Multiple international awards &

recognition • Founder, Data Blueprint (datablueprint.com) • Associate Professor of IS, VCU (vcu.edu) • (Past) President, DAMA Int. (dama.org) • 9 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, Walmart, and the Commonwealth of Virginia

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Unlock Business Value

Through Reference & Master Data Management

10124 W. Broad Street, Suite C Glen Allen, Virginia 23060

804.521.4056


• 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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UsesReuses

What is data management?

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Sources

Data Governance

Data Engineering

Data Delivery

DataStorage

Specialized Team Skills

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 • Storage • Engineering • Delivery • Governance

When executed, engineering, storage, and delivery implement governance

Note: does not well-depict data reuse

Maslow's Hierarchiy 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

Maintain fit-for-purpose data, efficiently and effectively

DMM℠ Structure of 5 Integrated DM Practice Areas

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Manage data coherently

Manage data assets professionally

Data architecture implementation

Data engineering implementation

Organizational support


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


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

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#dataed










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Summary: Reference and MDM

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

Copyright 2013 by Data Blueprint 16

•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


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.

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Wikipedia: Golden Version

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• 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"

• In data management:

– It is the data value representing the "correct" answer to the business question


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.

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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

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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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– as opposed to mobile device management

• 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"

• 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

23Copyright 2015 by Data Blueprint


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


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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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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Questions?

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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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