Data-Ed Online Webinar: Business Value from MDM

1. 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. 1 PETER AIKEN WITH JUANITA BILLINGS FOREWORD BY JOHN BOTTEGA MONETIZING DATA MANAGEMENT Unlocking the Value in Your Organization’s Most Important Asset. The Case for the Chief Data Officer Recasting the C-Suite to Leverage Your MostValuable Asset Peter Aiken and Michael Gorman PETER AIKEN WITH JUANITA BILLINGS FOREWORD BY JOHN BOTTEGA MONETIZING DATA MANAGEMENT Unlocking the Value in Your Organization’s Most Important Asset. The Case for the Chief Data Officer Recasting the C-Suite to Leverage Your MostValuable Asset Peter Aiken and Michael Gorman PETER AIKEN WITH JUANITA BILLINGS FOREWORD BY JOHN BOTTEGA MONETIZING DATA MANAGEMENT Unlocking the Value in Your Organization’s Most Important Asset. The Case for the Chief Data Officer Recasting the C-Suite to Leverage Your MostValuable Asset Peter Aiken and Michael Gorman

4. Copyright 2013 by Data Blueprint Get Social With Us! Live Twitter Feed Join the conversation! Follow us: @datablueprint @paiken Ask questions and submit your comments: #dataed 4 Like Us on Facebook www.facebook.com/ datablueprint Post questions and comments Find industry news, insightful content and event updates. Join the Group Data Management & Business Intelligence Ask questions, gain insights and collaborate with fellow data management professionals

5. The Case for the Chief Data Officer Recasting the C-Suite to Leverage Your MostValuable Asset Peter Aiken and Michael Gorman PETER AIKEN WITH JUANITA BILLINGS FOREWORD BY JOHN BOTTEGA MONETIZING DATA MANAGEMENT Unlocking the Value in Your Organization’s Most Important Asset. 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 5 Copyright 2015 by Data Blueprint The Case for the Chief Data Officer Recasting the C-Suite to Leverage Your MostValuable Asset Peter Aiken and Michael Gorman

6. Unlock Business Value Through Reference & Master Data Management 10124 W. Broad Street, Suite C Glen Allen, Virginia 23060 804.521.4056

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  Tweeting now: #dataed 7 Tweeting now: #dataed

8. UsesReuses What is data management? 8 Copyright 2015 by Data Blueprint Sources Data Governance   Data Engineering   Data   Delivery   Data  Storage Specialized Team Skills 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 • Storage • Engineering • Delivery • Governance When executed,   engineering, storage, and   delivery implement governance Note: does not well-depict data reuse

10. 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 10 Copyright 2015 by Data Blueprint Data Platform/Architecture Data Governance Data Quality Data Operations Data Management Strategy Technologies Capabilities

11. Maintain fit-for-purpose data, efficiently and effectively DMM℠ Structure of   5 Integrated   DM Practice Areas 11 Copyright 2015 by Data Blueprint Manage data coherently Manage data assets professionally Data architecture implementation Data engineering implementation Organizational support

12. Copyright 2013 by Data Blueprint The DAMA Guide to the Data Management Body of Knowledge 12 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

13. Copyright 2013 by Data Blueprint 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 13 #dataed

16. 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

17. Copyright 2013 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. 17

18. Wikipedia: Golden Version 18 Copyright 2015 by Data Blueprint • 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

19. Copyright 2013 by Data Blueprint 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. 19

20. 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 20 US United States GB (not UK) United Kingdom from The DAMA Guide to the Data Management Body of Knowledge © 2009 by DAMA International

21. 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. 21

22. 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 22 from The DAMA Guide to the Data Management Body of Knowledge © 2009 by DAMA International

23. – 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 23 Copyright 2015 by Data Blueprint

24. Copyright 2013 by Data Blueprint Reference Data versus Master Data 24 • 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

26. 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 26 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.

27. 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. 27 Source: http://www.igate.com/22926.aspx

29. Copyright 2013 by Data Blueprint Inextricably intertwined 29 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(

30. Copyright 2013 by Data Blueprint Interactions 30 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

31. 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) 31 Multiple Sources of (for example) Customer Data

37. 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 37

38. Copyright 2013 by Data Blueprint Automating Business Process Discovery (qpr.com) 38 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

42. Copyright 2013 by Data Blueprint Goals and Principles 42 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

43. Copyright 2013 by Data Blueprint Reference & MDM Activities 43 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

44. Copyright 2013 by Data Blueprint Specific Reference and MDM Investigations 44 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?

45. 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 45 from The DAMA Guide to the Data Management Body of Knowledge © 2009 by DAMA International

46. Copyright 2013 by Data Blueprint Roles and Responsibilities 46 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

47. Copyright 2013 by Data Blueprint Technology 47 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

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

50. 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 50 Source:http://www.mdmsource.com/master-data-management-tips-best-practices.html

51. 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. 51 Source:http://www.mdmsource.com/master-data-management-tips-best-practices.html

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. 53 [Source: unknown]

54. Copyright 2013 by Data Blueprint 15 MDM Success Factors 54 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]

59. 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 59

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