Data-Ed Online: Unlock Business Value through Reference & MDM

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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 include:

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

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Data-Ed Online: Unlock Business Value through Reference & MDM

  1. 1. Unlock Business Value Through Reference & Master Data Management 10124 W. Broad Street, Suite C Glen Allen, Virginia 23060 804.521.4056
  2. 2. 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) MONETIZING DATA MANAGEMENT • Utilizing reference & MDM in support of business strategy Date: Time: Presenter: November 12, 2013 2:00 PM ET/11:00 AM PT Peter Aiken, Ph.D. Unlocking the Value in Your Organization’s Most Important Asset. PETER AIKEN WITH JUANITA BILLINGS FOREWORD BY JOHN BOTTEGA 2 Copyright 2013 by Data Blueprint
  3. 3. Get Social With Us! Live Twitter Feed Like Us on Facebook Join the Group Join the conversation! www.facebook.com/ datablueprint Data Management & Business Intelligence Follow us: @datablueprint @paiken Ask questions and submit your comments: #dataed Post questions and comments Ask questions, gain insights Find industry news, insightful and collaborate with fellow data management content professionals and event updates. 3 Copyright 2013 by Data Blueprint
  4. 4. Peter Aiken, PhD • • • • • • • • 25+ years of experience in data management Multiple international awards & recognition Founder, Data Blueprint (datablueprint.com) Associate Professor of IS, VCU (vcu.edu) President, DAMA International (dama.org) 8 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, and the Commonwealth of Virginia 4 Copyright 2013 by Data Blueprint 2
  5. 5. Unlocking Business Value Through Reference & Master Data Management • 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 5 Copyright 2013 by Data Blueprint
  6. 6. 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 Data Management Functions 6 Copyright 2013 by Data Blueprint
  7. 7. The DAMA Guide to the Data Management Body of Knowledge Amazon: http://www.amazon.com/ DAMA-Guide-ManagementKnowledge-DAMA-DMBOK/ dp/0977140083 Or enter the terms "dama dm bok" at the Amazon search engine Environmental Elements 7 Copyright 2013 by Data Blueprint
  8. 8. 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 #dataed 8 Copyright 2013 by Data Blueprint
  9. 9. Data Management 9 Copyright 2013 by Data Blueprint
  10. 10. Five Interrelated Data Management Practice Areas Manage data coherently. Data Program Coordination Share data across boundaries. Organizational Data Integration Data Development Data Stewardship Assign responsibilities for data. Engineer data delivery systems. Data Support Operations Maintain data availability. 10 Copyright 2013 by Data Blueprint
  11. 11. Five Integrated DM Practice Areas Data management processes and infrastructure Organizational Strategies Implementation Data Program Coordination Guidance Goals Organizational Data Integration Combining multiple assets to produce extra value Integrated Models Achieve sharing of data within a business area Organizational-entity subject area data integration Data Stewardship Standard Data Application Models & Designs Provide reliable data access Direction Data Support Operations Feedback Leverage data in organizational activities Data Development Business Data Data Asset Use Business Value 11 Copyright 2013 by Data Blueprint
  12. 12. Unlocking Business Value Through Reference & Master Data Management • 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 12 Copyright 2013 by Data Blueprint
  13. 13. Summary: Reference and MDM from The DAMA Guide to the Data Management Body of Knowledge © 2009 by DAMA International 13 Copyright 2013 by Data Blueprint
  14. 14. MDM Definition • 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). 14 Copyright 2013 by Data Blueprint
  15. 15. 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 15 Copyright 2013 by Data Blueprint
  16. 16. Reference/Master Data Management • Definition – Planning, implementation and control activities to ensure consistency with a "golden version" of contextual data values. from The DAMA Guide to the Data Management Body of Knowledge © 2009 by DAMA International 16 Copyright 2013 by Data Blueprint
  17. 17. 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. 17 Copyright 2013 by Data Blueprint
  18. 18. 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. 18 Copyright 2013 by Data Blueprint
  19. 19. 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 US United States GB (not UK) United Kingdom from The DAMA Guide to the Data Management Body of Knowledge © 2009 by DAMA International 19 Copyright 2013 by Data Blueprint
  20. 20. 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 from The DAMA Guide to the Data Management Body of Knowledge © 2009 by DAMA International 20 Copyright 2013 by Data Blueprint
  21. 21. 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" Both provide the context for transaction data from The DAMA Guide to the Data Management Body of Knowledge © 2009 by DAMA International 21 Copyright 2013 by Data Blueprint
  22. 22. Unlocking Business Value Through Reference & Master Data Management • 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 22 Copyright 2013 by Data Blueprint
  23. 23. Reference Data Facts 2012 • Global industry-wide survey of reference data professionals • Results show: Poor quality of reference data continues to create major problems for financial institutions. • 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 Source: http://www.igate.com/22926.aspx 23 Copyright 2013 by Data Blueprint
  24. 24. 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 reevaluate 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. Source: http://www.igate.com/22926.aspx 24 Copyright 2013 by Data Blueprint
  25. 25. Interdependencies Data Governance Data Quality Master Data 25 Copyright 2013 by Data Blueprint
  26. 26. Inextricably intertwined Knowledge Management Practices Data Organization Practices Organized Knowledge 'Data' Routine Data Scans Metadata(Prac8ces((dashed lines not in existence) ( Sources( Suspected/ Identified Data Quality Problems Metadata( Engineering( ( Metadata( Metadata( Delivery( Storage( ( Metadata(Governance( Uses( Data that might benefit from Master Management Master Data Catalogs Master Data Management Practices Data Quality Engineering Routine Data Scans Improved Quality Data Operational Data 26 Copyright 2013 by Data Blueprint
  27. 27. Interactions Governance Violations Monitoring Routine Data Scans Master Data Monitoring Data Quality Monitoring Governance Rules Monitoring Rules Data Governance Practices Quality Rules Routine Data Scans Data Harvesting Monitoring Results: Suspected/ Identified Data Quality Problems Monitoring Results: Suspected/ Master Data & Characteristics Master Data Catalogs Data Quality Rules Data Quality Engineering Practices Focused Data Scans Master Data Management Practices Improved Quality Data Operational Data 27 Copyright 2013 by Data Blueprint
  28. 28. Finance Multiple Sources of (for example) CustomerApplication Data (3rd GL, batch system, no source) Payroll Data (database) Payroll Application (3rd GL) Finance Data (indexed) Marketing Data (external database) Marketing Application (4rd GL, query facilities, no reporting, very large) Personnel Data (database) R&D Data (raw) Personnel App. (20 years old, un-normalized data) R& D Applications (researcher supported, no documentation) Mfg. Data (home grown Mfg. Applications database) (contractor supported) 28 Copyright 2013 by Data Blueprint
  29. 29. Vocabulary is Important-Tank, Tanks, Tankers, Tanked 29 Copyright 2013 by Data Blueprint
  30. 30. Reference Data Architecture from The DAMA Guide to the Data Management Body of Knowledge © 2009 by DAMA International 30 Copyright 2013 by Data Blueprint
  31. 31. Master Data Architecture 31 Copyright 2013 by Data Blueprint
  32. 32. Combined R/M Data Architecture 32 Copyright 2013 by Data Blueprint
  33. 33. "180% Failure Rate" Fred Cohen, Patni http://www.igatepatni.com/bfs/solutions/payments.aspx Copyright 2013 by Data Blueprint 33
  34. 34. 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 34 Copyright 2013 by Data Blueprint
  35. 35. 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 35 Copyright 2013 by Data Blueprint
  36. 36. Traditional Engine 36 Copyright 2013 by Data Blueprint
  37. 37. Prius Hybrid Engine 37 Copyright 2013 by Data Blueprint
  38. 38. 38 Copyright 2013 by Data Blueprint
  39. 39. Goals and Principles 1. Provide authoritative source of reconciled, highquality 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 39 Copyright 2013 by Data Blueprint
  40. 40. 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 from The DAMA Guide to the Data Management Body of Knowledge © 2009 by DAMA International 40 Copyright 2013 by Data Blueprint
  41. 41. 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? from The DAMA Guide to the Data Management Body of Knowledge © 2009 by DAMA International 41 Copyright 2013 by Data Blueprint
  42. 42. 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 from The DAMA Guide to the Data Management Body of Knowledge © 2009 by DAMA International 42 Copyright 2013 by Data Blueprint
  43. 43. 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 from The DAMA Guide to the Data Management Body of Knowledge © 2009 by DAMA International 43 Copyright 2013 by Data Blueprint
  44. 44. 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 from The DAMA Guide to the Data Management Body of Knowledge © 2009 by DAMA International 44 Copyright 2013 by Data Blueprint
  45. 45. Unlocking Business Value Through Reference & Master Data Management • 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 45 Copyright 2013 by Data Blueprint
  46. 46. 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 from The DAMA Guide to the Data Management Body of Knowledge © 2009 by DAMA International 46 Copyright 2013 by Data Blueprint
  47. 47. 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 Source:http://www.mdmsource.com/master-data-management-tips-best-practices.html 47 Copyright 2013 by Data Blueprint
  48. 48. 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. Source:http://www.mdmsource.com/master-data-management-tips-best-practices.html 48 Copyright 2013 by Data Blueprint
  49. 49. Unlocking Business Value Through Reference & Master Data Management • 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 49 Copyright 2013 by Data Blueprint
  50. 50. 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. [Source: unknown] 50 Copyright 2013 by Data Blueprint
  51. 51. 15 MDM Success Factors 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] 51 Copyright 2013 by Data Blueprint
  52. 52. Seven Sisters (from British Telecom) http://www.datablueprint.com/thought-leaders/peter-aiken/book-monetizing-data-management/ 52 Copyright 2013 by Data Blueprint Thanks to Dave Evans
  53. 53. Summary: Reference and MDM from The DAMA Guide to the Data Management Body of Knowledge © 2009 by DAMA International 53 Copyright 2013 by Data Blueprint
  54. 54. Questions? + = It’s your turn! Use the chat feature or Twitter (#dataed) to submit your questions to Peter now. 54 Copyright 2013 by Data Blueprint
  55. 55. References 55 Copyright 2013 by Data Blueprint
  56. 56. 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-expertdevises-business-transformation-template • http://www.itbusinessedge.com/cm/blogs/lawson/free-report-shows-businesses-fed-upwith-bad-data/?cs=50416 • http://www.itbusinessedge.com/cm/blogs/lawson/whats-ahead-for-master-datamanagement/?cs=50082 • http://www.itbusinessedge.com/cm/blogs/vizard/master-data-management-reaches-for-thecloud/?cs=49264 • http://www.information-management.com/channels/master-data-management.html • http://www.dataversity.net/applying-six-sigma-to-master-data-management-mdmframework-for-integrating-mdm-into-ea-part-2/ • http://www.dataqualityfirst.com/getting_master_data_facts_straight_is_hard.htm 56 Copyright 2013 by Data Blueprint
  57. 57. Upcoming Events December Webinar: Unlock Business Value Through Document & Content Management December 10, 2013 @ 2:00 PM ET/11:00 AM PT Brought to you by: 57 Copyright 2013 by Data Blueprint

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