Successfully reported this slideshow.
We use your LinkedIn profile and activity data to personalize ads and to show you more relevant ads. You can change your ad preferences anytime.

DataEd Webinar: Reference & Master Data Management - Unlocking Business Value

435 views

Published on

Data tends to pile up and can be rendered unusable or obsolete without careful maintenance processes. Reference and Master Data Management (MDM) has been a popular Data Management approach to effectively gain mastery over not just the data but the supporting architecture for processing it. This webinar presents MDM as a strategic approach to improving and formalizing practices around those data items that provide context for many organizational transactions—its master data. Too often, MDM has been implemented technology-first and achieved the same very poor track record (one-third succeeding on-time, within budget, and achieving planned functionality). MDM success depends on a coordinated approach typically involving Data Governance and Data Quality activities.

Learning Objectives:
- Understand foundational reference and MDM concepts based on the Data Management Body of Knowledge (DMBOK)
- Understand why these are an important component of your Data Architecture
- Gain awareness of Reference and MDM Frameworks and building blocks
- Know what MDM guiding principles consist of and best practices
- Know how to utilize reference and MDM in support of business strategy

Published in: Data & Analytics
  • Be the first to comment

DataEd Webinar: Reference & Master Data Management - Unlocking Business Value

  1. 1. Peter Aiken, Ph.D. Reference 
 & 
 Master Data Management Copyright 2019 by Data Blueprint Slide # !1 Unlocking Business Value
  2. 2. • DAMA International President 2009-2013 / 2018 • DAMA International Achievement Award 2001 
 (with Dr. E. F. "Ted" Codd • DAMA International Community Award 2005 Peter Aiken, Ph.D. !2Copyright 2019 by Data Blueprint Slide # • I've been doing this a long time • My work is recognized as useful • Associate Professor of IS (vcu.edu) • Founder, Data Blueprint (datablueprint.com) • DAMA International (dama.org) • 10 books and dozens of articles • Experienced w/ 500+ data management practices worldwide • Multi-year immersions – US DoD (DISA/Army/Marines/DLA) – Nokia – Deutsche Bank – Wells Fargo – Walmart – … PETER AIKEN WITH JUANITA BILLINGS FOREWORD BY JOHN BOTTEGA MONETIZING DATA MANAGEMENT Unlocking the Value in Your Organization’s Most Important Asset.
  3. 3. !3Copyright 2019 by Data Blueprint Slide # • 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 Reference & Master Data Management - Unlocking Business Value Tweeting now: t#dataed
  4. 4. IT Business Data Perceived State of Data !4Copyright 2019 by Data Blueprint Slide #
  5. 5. Data Desired To Be State of Data !5Copyright 2019 by Data Blueprint Slide # IT Business
  6. 6. The Real State of Data !6Copyright 2019 by Data Blueprint Slide # Data IT Business
  7. 7. 
 
 
 UsesUsesReuses What is data management? !7Copyright 2019 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
  8. 8. 
 
 
 
 
 
 
 
 
 
 
 Data Management !8Copyright 2019 by Data Blueprint Slide # Sources 
 Data Engineering 
 Data 
 Delivery 
 Data
 Storage Specialized Team Skills 
 Resources
 (optimized for reuse)
 Data Governance AnalyticInsight Specialized Team Skills
  9. 9. !9Copyright 2019 by Data Blueprint Slide # • 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 Reference & Master Data Management - Unlocking Business Value Tweeting now: t#dataed
  10. 10. Data Management Practices Hierarchy 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) Advanced 
 Data 
 Practices • MDM • Mining • Big Data • Analytics • Warehousing • SOA Foundational Data Management Practices !10Copyright 2019 by Data Blueprint Slide # Data Platform/Architecture Data Governance Data Quality Data Operations Data Management Strategy Technologies Capabilities
  11. 11. Data$Management$ Strategy Data Management Goals Corporate Culture Data Management Funding Data Requirements Lifecycle Data Governance Governance Management Business Glossary Metadata Management Data Quality Data Quality Framework Data Quality Assurance Data Operations Standards and Procedures Data Sourcing Platform$&$ Architecture Architectural Framework Platforms & Integration Supporting$ Processes Measurement & Analysis Process Management Process Quality Assurance Risk Management Configuration Management Component Process$Areas DMM℠ Structure of 
 5 Integrated 
 DM Practice Areas Data 
 Governance Data 
 Management
 Strategy Data 
 Operations Platform
 Architecture Supporting
 Processes Maintain fit-for-purpose data, efficiently and effectively !11Copyright 2019 by Data Blueprint Slide # Manage data coherently Manage data assets professionally Data life cycle management Organizational support Data 
 QualityData$Management$ Strategy Data Management Goals Corporate Culture Data Management Funding Data Requirements Lifecycle Data Governance Governance Management Business Glossary Metadata Management Data Quality Data Quality Framework Data Quality Assurance Data Operations Standards and Procedures Data Sourcing Platform$&$ Architecture Architectural Framework Platforms & Integration Supporting$ Processes Measurement & Analysis Process Management Process Quality Assurance Risk Management Configuration Management Component Process$Areas Data$Management$ Strategy Data Management Goals Corporate Culture Data Management Funding Data Requirements Lifecycle Data Governance Governance Management Business Glossary Metadata Management Data Quality Data Quality Framework Data Quality Assurance Data Operations Standards and Procedures Data Sourcing Platform$&$ Architecture Architectural Framework Platforms & Integration Supporting$ Processes Measurement & Analysis Process Management Process Quality Assurance Risk Management Configuration Management Component Process$Areas Data$Management$ Strategy Data Management Goals Corporate Culture Data Management Funding Data Requirements Lifecycle Data Governance Governance Management Business Glossary Metadata Management Data Quality Data Quality Framework Data Quality Assurance Data Operations Standards and Procedures Data Sourcing Platform$&$ Architecture Architectural Framework Platforms & Integration Supporting$ Processes Measurement & Analysis Process Management Process Quality Assurance Risk Management Configuration Management Component Process$Areas Data$Management$ Strategy Data Management Goals Corporate Culture Data Management Funding Data Requirements Lifecycle Data Governance Governance Management Business Glossary Metadata Management Data Quality Data Quality Framework Data Quality Assurance Data Operations Standards and Procedures Data Sourcing Platform$&$ Architecture Architectural Framework Platforms & Integration Supporting$ Processes Measurement & Analysis Process Management Process Quality Assurance Risk Management Configuration Management Component Process$Areas Data$Management$ Strategy Data Management Goals Corporate Culture Data Management Funding Data Requirements Lifecycle Data Governance Governance Management Business Glossary Metadata Management Data Quality Data Quality Framework Data Quality Assurance Data Operations Standards and Procedures Data Sourcing Platform$&$ Architecture Architectural Framework Platforms & Integration Supporting$ Processes Measurement & Analysis Process Management Process Quality Assurance Risk Management Configuration Management Component Process$Areas Data architecture implementation
  12. 12. Your data foundation can only be as strong as its weakest link! Data architecture implementation Data 
 Governance Data 
 Management
 Strategy Data 
 Operations Platform
 Architecture Supporting
 Processes Maintain fit-for-purpose data, efficiently and effectively !12Copyright 2019 by Data Blueprint Slide # Manage data coherently Manage data assets professionally Data life cycle management Organizational support Data 
 Quality Data 
 Governance Data 
 Quality Platform
 Architecture Data 
 Operations Data 
 Management
 Strategy 3 3 33 1 Supporting
 Processes Optimized Measured Defined Managed Initial Optimized Measured Defined Managed Initial Optimized Measured Defined Managed Initial Optimized Measured Defined Managed Initial Optimized Measured Defined Managed Initial
  13. 13. The DAMA Guide to the Data Management Body of Knowledge 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 !13Copyright 2019 by Data Blueprint Slide # Data Management Functions
  14. 14. Summary: Reference and MDM !14Copyright 2019 by Data Blueprint Slide # from The DAMA Guide to the Data Management Body of Knowledge © 2009 by DAMA International
  15. 15. What is the CDMP? • Certified Data Management Professional • DAMA International • 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: – httphttps://dama.org/content/cdmp-0 !15Copyright 2019 by Data Blueprint Slide #
  16. 16. !16Copyright 2019 by Data Blueprint Slide # • 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 Reference & Master Data Management - Unlocking Business Value Tweeting now: t#dataed
  17. 17. + 1 Year • 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 !17Copyright 2019 by Data Blueprint Slide #
  18. 18. !18Copyright 2019 by Data Blueprint Slide # Garbage In ➜ Garbage Out
  19. 19. Definitions !19Copyright 2019 by Data Blueprint Slide # • 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 technology-based 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)
  20. 20. 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. !20Copyright 2019 by Data Blueprint Slide #
  21. 21. 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. !21Copyright 2019 by Data Blueprint Slide # Current Customer Ex-Custom er? Potential Customer VIP-Custom er? Residential
 Customer Commercial
 Customer Customer
  22. 22. 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 !22Copyright 2019 by Data Blueprint Slide # US United States GB (not UK) United Kingdom from The DAMA Guide to the Data Management Body of Knowledge © 2009 by DAMA International
  23. 23. 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. !23Copyright 2019 by Data Blueprint Slide #
  24. 24. 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 • Provide context for transactions • From the term "Master File" !24Copyright 2019 by Data Blueprint Slide # from The DAMA Guide to the Data Management Body of Knowledge © 2009 by DAMA International
  25. 25. Example Transaction Processing System !25Copyright 2019 by Data Blueprint Slide # $5 Balance=$100 Balance=$95
  26. 26. Reference Data versus Master Data • 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" !26Copyright 2019 by Data Blueprint Slide # from The DAMA Guide to the Data Management Body of Knowledge © 2009 by DAMA International Both provide the context for transaction data
  27. 27. !27Copyright 2019 by Data Blueprint Slide # • 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 Reference & Master Data Management - Unlocking Business Value Tweeting now: t#dataed
  28. 28. Data Facts • Poor quality of reference data continues to create major problems for financial institutions – To many firms surveyed still manage data locally • Home-grown reference data solutions predominate – Putting institutions at risk for meeting regulatory constraints • Risk management – More important business driver than cost • New and changing regulatory 
 requirements prompt many to 
 re-evaluate their approach !28Copyright 2019 by Data Blueprint Slide # Source: http://www.igate.com/22926.aspx
  29. 29. A good way to begin practicing data strategy • Select 3 data management functions (parts of the DM BoK) – Data Governance – Reference and Master Data Management – Data Quality Management !29Copyright 2019 by Data Blueprint Slide #
  30. 30. interdependencies !30Copyright 2019 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
  31. 31. Solution Framework !31Copyright 2019 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
  32. 32. Inextricably intertwined !32Copyright 2019 by Data Blueprint Slide # 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(
  33. 33. Interactions !33Copyright 2019 by Data Blueprint Slide # 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
  34. 34. Multiple Sources of (for example) Customer Data 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) Marketing Application
 (4rd GL, query facilities, 
 no reporting, very large) 
 Marketing Data (external database) 
 Finance Data (indexed) Finance Application
 (3rd GL, batch 
 system, no source) Personnel App.
 (20 years old,
 un-normalized data) 
 Personnel Data
 (database) !34Copyright 2019 by Data Blueprint Slide #
  35. 35. Vocabulary is Important-Tank, Tanks, Tankers !35Copyright 2019 by Data Blueprint Slide #
  36. 36. Reference Data Architecture !36Copyright 2019 by Data Blueprint Slide # from The DAMA Guide to the Data Management Body of Knowledge © 2009 by DAMA International
  37. 37. Master Data Architecture !37Copyright 2019 by Data Blueprint Slide #
  38. 38. Combined R/M Data Architecture !38Copyright 2019 by Data Blueprint Slide #
  39. 39. "180% Failure Rate" Fred Cohen, Patni !39Copyright 2019 by Data Blueprint Slide # http://www.igatepatni.com/bfs/solutions/payments.aspx
  40. 40. 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 !40Copyright 2019 by Data Blueprint Slide #
  41. 41. Automating Business Process Discovery (qpr.com) 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 !41Copyright 2019 by Data Blueprint Slide #
  42. 42. Traditional Engine !42Copyright 2019 by Data Blueprint Slide #
  43. 43. Prius Hybrid Engine !43Copyright 2019 by Data Blueprint Slide #
  44. 44. !44Copyright 2019 by Data Blueprint Slide #
  45. 45. MDM Business Process Overview !45Copyright 2019 by Data Blueprint Slide # Attributed to Steven Steinerman
  46. 46. !46Copyright 2019 by Data Blueprint Slide # • 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 Reference & Master Data Management - Unlocking Business Value Tweeting now: t#dataed
  47. 47. Goals and Principles 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. !47Copyright 2019 by Data Blueprint Slide # from The DAMA Guide to the Data Management Body of Knowledge © 2009 by DAMA International
  48. 48. Reference & MDM Activities • 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 !48Copyright 2019 by Data Blueprint Slide # from The DAMA Guide to the Data Management Body of Knowledge © 2009 by DAMA International
  49. 49. Specific Reference and MDM Investigations • 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? !49Copyright 2019 by Data Blueprint Slide # from The DAMA Guide to the Data Management Body of Knowledge © 2009 by DAMA International
  50. 50. 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 !50Copyright 2019 by Data Blueprint Slide # from The DAMA Guide to the Data Management Body of Knowledge © 2009 by DAMA International
  51. 51. Roles and Responsibilities • Suppliers: – Steering Committees – Business Data Stewards – Subject Matter Experts – Data Consumers – Standards Organizations – Data Providers – ...
 
 
 
 • 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 – ... !51Copyright 2019 by Data Blueprint Slide # from The DAMA Guide to the Data Management Body of Knowledge © 2009 by DAMA International
  52. 52. Technology • 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 !52Copyright 2019 by Data Blueprint Slide # from The DAMA Guide to the Data Management Body of Knowledge © 2009 by DAMA International
  53. 53. !53Copyright 2019 by Data Blueprint Slide # • 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 Reference & Master Data Management - Unlocking Business Value Tweeting now: t#dataed
  54. 54. Guiding Principles 1. Shared R/M data belong to the organization 2. R/M data management is an on-going data quality improvement 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 !54Copyright 2019 by Data Blueprint Slide # from The DAMA Guide to the Data Management Body of Knowledge © 2009 by DAMA International
  55. 55. 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 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. 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 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. 10 Best Practices for MDM !55Copyright 2019 by Data Blueprint Slide # Source:http://www.mdmsource.com/master-data-management-tips-best-practices.html https://www.ase.org.uk/bestpractice
  56. 56. !56Copyright 2019 by Data Blueprint Slide # • 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 Reference & Master Data Management - Unlocking Business Value Tweeting now: t#dataed
  57. 57. 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. 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 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. 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 15 MDM Success Factors !57Copyright 2019 by Data Blueprint Slide # [Source: unknown]
  58. 58. Seven Sisters (from British Telecom) http://www.datablueprint.com/thought-leaders/peter-aiken/book-monetizing-data-management/ [Thanks to Dave Evans] !58Copyright 2019 by Data Blueprint Slide #
  59. 59. Seven Sisters (from British Telecom) http://www.datablueprint.com/thought-leaders/peter-aiken/book-monetizing-data-management/ [Thanks to Dave Evans] !59Copyright 2019 by Data Blueprint Slide #
  60. 60. Summary: Reference and MDM !60Copyright 2019 by Data Blueprint Slide # from The DAMA Guide to the Data Management Body of Knowledge © 2009 by DAMA International
  61. 61. Upcoming Events Enterprise Data World
 How I Learned to Stop Worrying & Love My Data Warehouse
 Tuesday, 3/19/2019 @ 1:45 PM ET Data Management Brain Drain
 Wednesday, 3/20/2019 @ 2:45 PM ET April Webinar
 Approaching Data Management Technologies
 April 9, 2019 @ 2:00 PM ET May Webinar
 Data Management Maturity: 
 Achieving Best Practices using DMM
 May 14, 2019 @ 2:00 PM ET 
 Sign up for webinars at: 
 www.datablueprint.com/webinar-schedule !61Copyright 2019 by Data Blueprint Slide # Brought to you by:
  62. 62. + = !62Copyright 2019 by Data Blueprint Slide # It’s your turn! 
 Use the chat feature or Twitter (#dataed) to submit your questions now! Questions?
  63. 63. References !63Copyright 2019 by Data Blueprint Slide #
  64. 64. 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 !64Copyright 2019 by Data Blueprint Slide #
  65. 65. 10124 W. Broad Street, Suite C Glen Allen, Virginia 23060 804.521.4056 Copyright 2019 by Data Blueprint Slide # 65

×