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The Importance of Master Data Management

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Despite its immaterial nature, data has a tendency to pile up as time goes on, and can quickly be rendered unusable or obsolete without careful maintenance and streamlining of processes for its management. This presentation will provide you with an understanding of reference and Master Data Management (MDM), one such method for keeping mass amounts of business data organized and functional towards achieving business goals.

MDM’s guiding principles include the establishment and implementation of authoritative data sources and effective means of delivering data to various business processes, as well as increases to the quality of information used in organizational analytical functions (such as BI). To that end, attendees of this webinar will learn how to:

Structure their Data Management processes around these principles
Incorporate Data Quality engineering into the planning of reference and MDM
Understand why MDM is so critical to their organization’s overall data strategy
Discuss foundational MDM concepts based on “The DAMA Guide to the Data Management Body of Knowledge” (DAMA DMBOK)

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The Importance of Master Data Management

  1. 1. The Importance of Reference & MDM Eternal Management of the Data Mind Peter Aiken, Ph.D. Copyright 201 8 by Data Blueprint Slide # • 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 201 8 by Data Blueprint Slide #
  2. 2. Data Assets Win! Data 
 Assets Financial 
 Assets Real
 Estate Assets Inventory Assets Non- depletable Available for subsequent use Can be 
 used up Can be 
 used up Non- degrading √ √ Can degrade
 over time Can 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
  3. 3. 
 
 
 UsesUsesReuses What is data management? 5Copyright 2018 by Data Blueprint Slide # 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 activities

 Aiken, 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 6Copyright 2018 by Data Blueprint Slide # Sources 
 Data Engineering 
 Data 
 Delivery 
 Data
 Storage Specialized Team Skills 
 Resources
 (optimized for reuse)
 Data Governance AnalyticInsight Specialized Team Skills
  4. 4. Copyright 201 8 by Data Blueprint Slide # Maslow's Hierarchy of Needs 7Copyright 201 8 by Data Blueprint Slide # 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 
 greater
 risk
 (with thanks to Tom DeMarco) Data Management Practices Hierarchy Advanced 
 Data 
 Practices • MDM • Mining • Big Data • Analytics • Warehousing • SOA Foundational Data Management Practices 8 Copyright 2018 by Data Blueprint Data Platform/Architecture Data Governance Data Quality Data Operations Data Management Strategy Technologies Capabilities
  5. 5. Copyright 201 8 by Data Blueprint Slide # DMM℠ Structure of 
 5 Integrated 
 DM Practice Areas Data architecture implementation Data 
 Governanc e Data 
 Manageme nt
 Strategy Data 
 Operations Platform
 Architectur e Supporting
 Processes Maintain fit-for-purpose data, efficiently and effectively 9Copyright 201 8 by Data Blueprint Slide # Manage data coherently Manage data assets professionally Data life cycle management Organizational support Data 
 Quality Copyright 201 8 by Data Blueprint Slide # Data Management Strategy is often the weakest link Data architecture implementation Data 
 Governanc e Data 
 Manageme nt
 Strategy Data 
 Operations Platform
 Architectur e Supporting
 Processes Maintain fit-for-purpose data, efficiently and effectively 10Copyright 201 8 by Data Blueprint Slide # Manage data coherently Manage data assets professionally Data life cycle management Organizational support Data 
 Quality 3 3 33 1
  6. 6. Copyright 2013 by Data Blueprint The DAMA Guide to the Data Management Body of Knowledge 11 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 Copyright 2013 by Data Blueprint Summary: Reference and MDM 12 from The DAMA Guide to the Data Management Body of Knowledge © 2009 by DAMA International
  7. 7. 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
 13 + 1 Year 14 Copyright 2018 by Data Blueprint • 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
  8. 8. – 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 15 Copyright 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. 16Copyright 2018 by Data Blueprint Slide #
  9. 9. 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. 17Copyright 2018 by Data Blueprint Slide # Copyright 2013 by Data Blueprint 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 18 US United States GB (not UK) United Kingdom from The DAMA Guide to the Data Management Body of Knowledge © 2009 by DAMA International
  10. 10. Copyright 2013 by Data Blueprint 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 Copyright 2013 by Data Blueprint 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 20 from The DAMA Guide to the Data Management Body of Knowledge © 2009 by DAMA International
  11. 11. Copyright 2013 by Data Blueprint Reference Data versus Master Data 21 • 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 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
 22
  12. 12. Copyright 2013 by Data Blueprint 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 23 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. Copyright 2013 by Data Blueprint 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. 24 Source: http://www.igate.com/22926.aspx
  13. 13. Copyright 2013 by Data Blueprint Interdependencies 25 Data Governance Master DataData Quality interdependencies 26Copyright 2018 by Data Blueprint Slide # Data Governance Master DataData Quality makes the case and is responsible for is a necessary but insufficient prerequisite to success MD capabilities constrain governance effectiveness
  14. 14. Solution Framework 27Copyright 2018 by Data Blueprint Slide # SORs SOR 1 SOR 2 SOR 3 SOR 4 SOR 5 SOR 6 SOR 7 SOR 8 Repository Indicator
 Extraction
 Service
 (could be 
 segmented by
 day of week
 month, 
 system, etc.) Update
 Addresses Latency
 Check
 Service Ch 1 Ch 2 Ch 3 Ch 4 Ch 5 Ch 6 Channels Ch 7 Ch 8 External Address 
 Validation Processing Customer
 Contact Copyright 2013 by Data Blueprint 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(
  15. 15. Copyright 2013 by Data Blueprint 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 Copyright 2013 by Data Blueprint 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) 30 Multiple Sources of (for example) Customer Data
  16. 16. Copyright 2013 by Data Blueprint Vocabulary is Important-Tank, Tanks, Tankers, Tanked 31 Copyright 2013 by Data Blueprint Reference Data Architecture 32 from The DAMA Guide to the Data Management Body of Knowledge © 2009 by DAMA International
  17. 17. Copyright 2013 by Data Blueprint Master Data Architecture 33 Copyright 2013 by Data Blueprint Combined R/M Data Architecture 34
  18. 18. Copyright 2013 by Data Blueprint "180% Failure Rate" Fred Cohen, Patni 35 http://www.igatepatni.com/bfs/solutions/payments.aspx Copyright 2013 by Data Blueprint 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
  19. 19. Copyright 2013 by Data Blueprint Automating Business Process Discovery (qpr.com) 37 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 Copyright 2013 by Data Blueprint Traditional Engine 38
  20. 20. Copyright 2013 by Data Blueprint Prius Hybrid Engine 39 Copyright 2013 by Data Blueprint 40
  21. 21. MDM Business Process Overview 41Copyright 2018 by Data Blueprint Slide # Attributed to Steven Steinerman 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
 42
  22. 22. Copyright 2013 by Data Blueprint 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 Copyright 2013 by Data Blueprint 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
  23. 23. Copyright 2013 by Data Blueprint 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? Copyright 2013 by Data Blueprint 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
  24. 24. Copyright 2013 by Data Blueprint 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 Copyright 2013 by Data Blueprint Technology 48 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
  25. 25. 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
 49 Copyright 2013 by Data Blueprint Guiding Principles 1. 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
  26. 26. Copyright 2013 by Data Blueprint 10 Best Practices for MDM 1. 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 51 Source:http://www.mdmsource.com/master-data-management-tips-best-practices.html Copyright 2013 by Data Blueprint 10 Best Practices for MDM, cont’d 6. 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. 52 Source:http://www.mdmsource.com/master-data-management-tips-best-practices.html
  27. 27. 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
 53 Copyright 2013 by Data Blueprint 15 MDM Success Factors 1. 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. 54 [Source: unknown]
  28. 28. Copyright 2013 by Data Blueprint 15 MDM Success Factors 55 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] 56Copyright 2018 by Data Blueprint Slide #
  29. 29. Copyright 2013 by Data Blueprint Summary: Reference and MDM 57 from The DAMA Guide to the Data Management Body of Knowledge © 2009 by DAMA International Copyright 2018 by Data Blueprint Slide # March Webinar: The Importance of MDM
 March 13, 2018 @ 2:00 PM ET/11:00 AM PT April Webinar: Data Modeling Fundamentals
 April 10, 2018 @ 2:00 PM ET/11:00 AM PT Sign up at: www.datablueprint.com/webinar-schedule Enterprise Data World 2018 (San Diego) The First Year as a CDO
 April 24, 2018 @ 1:30 PM ET Upcoming Events 58Copyright 2018 by Data Blueprint Slide # Brought to you by:
  30. 30. Copyright 2013 by Data Blueprint Questions? 59 It’s your turn! Use the chat feature or Twitter (#dataed) to submit your questions to Peter now. + = Copyright 2013 by Data Blueprint References 60
  31. 31. Copyright 2013 by Data Blueprint 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 10124 W. Broad Street, Suite C Glen Allen, Virginia 23060 804.521.4056 Copyright 2018 by Data Blueprint Slide # 62

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