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Master Data Management - Aligning Data, Process, and Governance

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Master Data Management (MDM) can provide significant value to the organization in creating consistent key data assets such as Customer, Product, Supplier, Patient, and the list goes on. But getting MDM “right” requires a strategic mix of Data Architecture, business process, and Data Governance. Join this webinar to learn how to find the “sweet spot” between technology, design, process, and people for your MDM initiative.

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Master Data Management - Aligning Data, Process, and Governance

  1. 1. Copyright Global Data Strategy, Ltd. 2019 Master Data Management - Aligning Data, Process, and Governance Donna Burbank Global Data Strategy, Ltd. May 23rd 2019 Follow on Twitter @donnaburbank Twitter Event hashtag: #DAStrategies
  2. 2. Global Data Strategy, Ltd. 2019 Donna Burbank 2 Donna is a recognised industry expert in information management with over 20 years of experience in data strategy, information management, data modeling, metadata management, and enterprise architecture. Her background is multi-faceted across consulting, product development, product management, brand strategy, marketing, and business leadership. She is currently the Managing Director at Global Data Strategy, Ltd., an international information management consulting company that specializes in the alignment of business drivers with data-centric technology. In past roles, she has served in key brand strategy and product management roles at CA Technologies and Embarcadero Technologies for several of the leading data management products in the market. As an active contributor to the data management community, she is a long time DAMA International member, Past President and Advisor to the DAMA Rocky Mountain chapter, and was recently awarded the Excellence in Data Management Award from DAMA International in 2016. Donna is also an analyst at the Boulder BI Train Trust (BBBT) where she provides advice and gains insight on the latest BI and Analytics software in the market. She was on several review committees for the Object Management Group’s for key information management and process modeling notations. She has worked with dozens of Fortune 500 companies worldwide in the Americas, Europe, Asia, and Africa and speaks regularly at industry conferences. She has co- authored two books: Data Modeling for the Business and Data Modeling Made Simple with ERwin Data Modeler and is a regular contributor to industry publications. She can be reached at donna.burbank@globaldatastrategy.com Donna is based in Boulder, Colorado, USA. Follow on Twitter @donnaburbank Twitter Event hashtag: #DAStrategies
  3. 3. Global Data Strategy, Ltd. 2019 DATAVERSITY Data Architecture Strategies • January 24 - on demand Emerging Trends in Data Architecture – What’s the Next Big Thing? • February 18 - on demand Building a Data Strategy - Practical Steps for Aligning with Business Goals • March 28 - on demand Data Modeling at the Environment Agency of England - Case Study • April 25 - on demand Data Governance - Combining Data Management with Organizational Change • May 23 Master Data Management - Aligning Data, Process, and Governance • June 27 Enterprise Architecture vs. Data Architecture • July 25 Metadata Management: from Technical Architecture & Business Techniques • August 22 Data Quality Best Practices (w/ guest Nigel Turner) • Sept 26 Self Service BI & Analytics: Architecting for Collaboration • October 24 Data Modeling Best Practices: Business and Technical Approaches • December 3 Building a Future-State Data Architecture Plan: Where to Begin? 3 This Year’s Lineup
  4. 4. Global Data Strategy, Ltd. 2019 Today’s Topic Master Data Management (MDM) can provide significant value to the organization in creating consistent key data assets such as Customer, Product, Supplier, Patient, and the list goes on. But getting MDM “right” requires a strategic mix of Data Architecture, business process, and Data Governance. Join this webinar to learn how to find the “sweet spot” between technology, design, process, and people for your MDM initiative. 4
  5. 5. Global Data Strategy, Ltd. 2019 5 A Successful Data Strategy links Business Goals with Technology Solutions “Top-Down” alignment with business priorities “Bottom-Up” management & inventory of data sources Managing the people, process, policies & culture around data Coordinating & integrating disparate data sources Leveraging & managing data for strategic advantage Copyright 2018 Global Data Strategy, Ltd MDM is Part of a Wider Data Strategy www.globaldatastrategy.com
  6. 6. Global Data Strategy, Ltd. 2019 What is Master Data? • Master Data is the consistent and uniform set of identifiers and extended attributes that describes the core entities of the enterprise including customers, prospects, citizens, suppliers, sites, hierarchies and chart of accounts (sic). • Master data management (MDM) is a technology-enabled discipline in which business and IT work together to ensure the uniformity, accuracy, stewardship, semantic consistency and accountability of the enterprise's official shared master data assets. - Source Gartner 6 Definition
  7. 7. Global Data Strategy, Ltd. 2019 Popularity of Master Data Management • As more and more organizations struggle with obtaining a single, consistent view of core data such as Product, Customer, Vendor, Location, etc. Master Data Management (MDM) is seeing a rise in implementations. • In a recent DATAVERSITY survey, over 65% of respondents are pursuing a MDM strategy. 7 1 Trends in Data Architecture, 2017, DATAVERSITY, by Donna Burbank and Charles Roe
  8. 8. Global Data Strategy, Ltd. 2019 Many Organizations are Just Getting Started 8 1 Trends in Data Architecture, 2017, DATAVERSITY, by Donna Burbank and Charles Roe Many organizations are in the early stages of their Master Data Management (MDM) implementations.
  9. 9. Global Data Strategy, Ltd. 2019 Understanding Your Customer 9 A 360 Degree View through Data Stefan Krauss Age = 31 Occupation = Ski Instructor Purchased €500 in outdoor gear in 2015 100% of purchases online Top Finisher in Engadin Ski Marathon 2010-2015 Member of Loyalty Program since 2010 Prefers Text Message Address = Pontresina, Switzerland
  10. 10. Global Data Strategy, Ltd. 2019 10 Stefan Krauss Age = 62 Understanding Your Customer A 360 Degree View through Data Occupation = Banker Member of Loyalty Program since 1990 Football Fan Prefers Physical Mail 100% of spending in store 75% of spending is while on holiday Purchased €3.500 in outdoor gear in 2015 Address = Zurich, Switzerland
  11. 11. Global Data Strategy, Ltd. 2019 Transaction Data vs. Master Data Customer Date Product Code Price Quantity Location Stefan Kraus 1/2/2017 Scarpa Telemark Ski Boot SC1279 €250 1 St. Moritz, CH Donna Burbank 1/5/2017 Scarpa Telemark Ski Boot SCU1289 $150 1 Boulder, CO Stefan Kraus 1/2/2017 North Face Down Jacket NF8392 €450 1 Zurich, CH Stefan Kraus 1/2/2017 Garmin Sports Watch GM29384 €200 2 Zurich, CH Wendy Hu 3/4/2017 Prana Yoga Pant PN82734 $51 5 New York, NY Joe Smith 4/1/2017 Garmin Sports Watch GM29384 $150 1 Albany, NY 11 Consider the following retail transaction data Transaction Data • Describes an action (verb): E.g. “buy” • May include measurements about the action: (Who, When, What, How Many, Where, How Much, etc.) • E.g. Stefan Kraus, 1/2/2017/, Scarpa Telemark Ski Boot, St. Moritz, CH, €250 Master Data • Describes the key entities (nouns), e.g. Customer, Product, Location • Provides attributes & context for these nouns • e.g. Wendy Hu, age 25, female, resident of New York, NY, Customer since 2005, preferred customer card, etc.
  12. 12. Global Data Strategy, Ltd. 2019 Customer Date Product Code Price Quantity Location Stefan Kraus 1/2/2017 Scarpa Telemark Ski Boot SC1279 €250 1 St. Moritz, CH Donna Burbank 1/5/2017 Scarpa Telemark Ski Boot SCU1289 $150 1 Boulder, CO Stefan Kraus 1/2/2017 North Face Down Jacket NF8392 €450 1 Zurich, CH Stefan Kraus 1/2/2017 Garmin Sports Watch GM29384 €200 2 Zurich, CH Wendy Hu 3/4/2017 Prana Yoga Pant PN82734 $51 5 New York, NY Joe Smith 4/1/2017 Garmin Sports Watch GM29384 $150 1 Albany, NY Transaction Data vs. Master Data 12 Master Data: Customer Master Data: Product Master Data: Location Reference Data: Country Codes Reference Data: State Codes Transaction Data
  13. 13. Global Data Strategy, Ltd. 2019 ETL Master Data Overview 13 CRM In-Store Sales MarketingFinance Online Sales Supply Chain Each system has its own unique functionality and associated data model. MDM “Golden Record” Data Warehouse BI & Reporting Data Model Lookup End User Applications Reference Data Sets Data Quality & Matching Publish & Subscribe Data Stewardship Validation
  14. 14. Global Data Strategy, Ltd. 2019 ETL Master Data Overview 14 CRM In-Store Sales MarketingFinance Online Sales Supply Chain MDM “Golden Record” Data Warehouse BI & Reporting Data Model Lookup End User Applications Reference Data Sets Data Quality & Matching Publish & Subscribe The MDM data model is a selected super/sub set of the source system models.Data Stewardship Validation
  15. 15. Global Data Strategy, Ltd. 2019 ETL Master Data Overview 15 CRM In-Store Sales MarketingFinance Online Sales Supply Chain MDM “Golden Record” Data Warehouse BI & Reporting Data Model Lookup End User Applications Reference Data Sets Data Quality & Matching Publish & Subscribe Data Stewardship Validation Matching Rules help create a “Golden Record”
  16. 16. Global Data Strategy, Ltd. 2019 Data Model / Database Keys for Matching Rules • Candidate attribute combinations for matching are often aligned with the primary and alternate keys from the logical data model. 16 Ideally, if all systems use the same unique identifier, matching is easier. But this isn’t often realistic in “real world” systems. • First, match on Date of Birth + SSN • Then, match on SSN + Last Name • Etc. Matching on Primary Key Matching on Alternate Keys Customer Customer ID
  17. 17. Global Data Strategy, Ltd. 2019 Fuzzy Matching • Fuzzy matching logic can also be used, which is particularly helpful in matching string fields such as names and addresses, where human error or different entry standards between systems can cause slight variations in similar values, e.g. • “101 Main St” vs. “101 Main Street” • “John Smith” vs. “J Smith” • In addition synonyms can be created to assist with matching, for example • “St”, “St.”, “Street”, etc. for addresses • “Tim”, “Timothy” for names and nicknames. • When using fuzzing matching, data quality thresholds can be defined for auto approval. • Match scores are created for each fuzzy match, for example .9 would indicate a strong match and .2 a weak one. • Using these scores as a guide, thresholds can be defined for which matches can be auto-approved, which can be auto-rejected, and which need human review from a data steward. 17
  18. 18. Global Data Strategy, Ltd. 2019 ETL Master Data Overview 18 CRM In-Store Sales MarketingFinance Online Sales Supply Chain MDM “Golden Record” Data Warehouse BI & Reporting Data Model Lookup End User Applications Reference Data Sets Data Quality & Matching Publish & Subscribe “Human in the Loop” – Data Stewards can validate match candidates. Data Stewardship Validation
  19. 19. Global Data Strategy, Ltd. 2019 ETL Master Data Overview 19 CRM In-Store Sales MarketingFinance Online Sales Supply Chain MDM “Golden Record” Data Warehouse BI & Reporting Data Model Lookup End User Applications Reference Data Sets Data Quality & Matching Publish & Subscribe Applications can reference the “Golden Record” for lookup. Data Stewardship Validation
  20. 20. Global Data Strategy, Ltd. 2019 ETL Master Data Overview 20 CRM In-Store Sales MarketingFinance Online Sales Supply Chain MDM “Golden Record” Data Warehouse BI & Reporting Data Model Lookup End User Applications Reference Data Sets Data Quality & Matching Publish & Subscribe MDM can feed the dimensional model for the data warehouse (e.g. customer, location, etc.) Data Stewardship Validation
  21. 21. Global Data Strategy, Ltd. 2019 MDM is not Reporting or Analytics -- It can be a Source 21 MDM “Golden Record” Data Warehouse BI & Reporting e.g. Customer by Region Reference Data Sets Graph Database Social Network Analysis Data Lake Social Media Sentiment Analysis
  22. 22. Global Data Strategy, Ltd. 2019 Governance & Business Process for MDM • While the implementation of the hub and population strategies is complex, more complex is understanding the business processes and governance processes around the populating and publishing systems. • In fact, the top two reasons for failure of MDM systems cited by the Gartner analyst group1 are : 22 1 Top Four Reasons Your MDM Program Will Fail, and How to Avoid Them, Gartner, 2016, ID: G00223675, by Bill O’Kane. Note: The remaining two reasons are: Failure to Manage Initial Master Data Quality & Defining Transactional (Fact) Data as Master Data Failure of IT to Align With Business Process Improvements and Document Business Value Delaying or Mismanaging Information Governance Implementation
  23. 23. Global Data Strategy, Ltd. 2019 Master Data Management Data Architecture Data Governance & Stewardship Business Process Alignment • Accountability & stewardship • Business rule validation • Conflict resolution • Business Prioritization • Business process models • Data mapping to process • CRUD and usage matrices • Optimizing business process for data improvement • System Architecture & data flow • Data models & hierarchies • Match/merge and survivorship rules • Data integration & design Successful MDM Combines Data, Process, and Accountability
  24. 24. Global Data Strategy, Ltd. 2019 The Importance of Business Process • Process models are a helpful tool for describing core business processes (e.g. BPMN). • “Swimlanes” outline organizational considerations • Data can be mapped to key business processes to understand creation & usage of information. • Understanding business process is critical to Data Governance • Who is using data? • How is it used in business processes? • Are there redundancies, conflicts, etc.? 24 Identifying key data dependencies in core business processes
  25. 25. Global Data Strategy, Ltd. 2019 CRUD Matrix – Understanding Data Usage Product Development Supply Chain Accounting Marketing Finance Product Assembly Instructions C R Product Components C R Product Price C U R Product Name C U,D Etc. 25 Create, Read, Update, Delete • CRUD Matrices shows where data is Created, Read, Updated or Deleted across the various areas of the organization • This can be a helpful tool in data governance & data quality to determine route cause analysis. Data entities or attributes Users, Departments, and/or Systems
  26. 26. Global Data Strategy, Ltd. 2019 The Intersection of MDM and Data Governance Successful MDM require governed alignment between business & technical roles Enterprise-wide Business Prioritization Enterprise Conceptual Data Model Map to Customer & Logistics Journey Prioritize Subject Areas / MDM Domains (e.g. Product) Prioritize Geo Rollout Business & Technical Alignment & Implementation Logical Model o Business Rules o Match-Merge Criteria o Survivorship Rules o Harmonization Strategy Data Steward Tasks & Workflow Business Process Workflow Data Domain Steward Business Process Steward Regional/Geo Steward Physical Model Enterprise Data Architect Business Analyst MDM Architect Business-level Technical-level o Data Profiling o Data Quality Remediation o Augmentation (e.g. address) o Data Quality Dashboards o Data Integration o CRUD Matrix o Publish & Subscribe Data Quality Data Integration MDM Platform Admin o DB Admin o MDM Platform Admin Business Data Owners Enterprise Data Architect Business Analyst Roles Business Technical System Steward MDM Architect Data Quality Specialist Data Integration Specialist www.globaldatastrategy.com
  27. 27. Global Data Strategy, Ltd. 2019 Optimizing Restaurant Revenue through Menu Data • An international restaurant chain realized through its digital strategy that: • While menus are the core product that drives their business… • They had little control or visibility over their menu data • Menu data was scattered across multiple systems in the organization from supply chain to kitchen prep to marketing, restaurant operations, etc. • Menu data was consolidated & managed in a central hub: • Master Data Management created a “single view of menu” for business efficiency & quality control • Data Governance created the workflow & policies around managing menu data • Process Models & Data Mappings were critical • Business Process diagrams to identify the flow of information • CRUD Matrixes to understand usage, stewardship & ownership Managing the Data that Runs the Business Product Creation & Testing Menu Display & Marketing Supply Chain Point of Sale & Restaurant Operations www.globaldatastrategy.com
  28. 28. Global Data Strategy, Ltd. 2019 Summary • Interest in Master Data Management (MDM) is on the rise as more organizations look to gain a common, consistent source for their core data assets (Customer, Product, Supplier, Employee, etc.) • Successful MDM is part of a wider data strategy and requires integration with: • Data Architecture • Business Process Alignment • Data Governance & Stewardship • Getting this combination right can have a positive impact on the success of the business.
  29. 29. Global Data Strategy, Ltd. 2019 DATAVERSITY Data Architecture Strategies • January 24 Emerging Trends in Data Architecture – What’s the Next Big Thing? • February 18 Building a Data Strategy - Practical Steps for Aligning with Business Goals • March 28 Data Modeling at the Environment Agency of England - Case Study (w/ guest Becky Russell from the EA) • April 25 Data Governance - Combining Data Management with Organizational Change (w/ guest Nigel Turner) • May 23 Master Data Management - Aligning Data, Process, and Governance • June 27 Enterprise Architecture vs. Data Architecture • July 25 Metadata Management: from Technical Architecture & Business Techniques • August 22 Data Quality Best Practices (w/ guest Nigel Turner) • Sept 26 Self Service BI & Analytics: Architecting for Collaboration • October 24 Data Modeling Best Practices: Business and Technical Approaches • December 3 Building a Future-State Data Architecture Plan: Where to Begin? 29 Join Us Next Month
  30. 30. Global Data Strategy, Ltd. 2019 About Global Data Strategy, Ltd • Global Data Strategy is an international information management consulting company that specializes in the alignment of business drivers with data-centric technology. • Our passion is data, and helping organizations enrich their business opportunities through data and information. • Our core values center around providing solutions that are: • Business-Driven: We put the needs of your business first, before we look at any technology solution. • Clear & Relevant: We provide clear explanations using real-world examples. • Customized & Right-Sized: Our implementations are based on the unique needs of your organization’s size, corporate culture, and geography. • High Quality & Technically Precise: We pride ourselves in excellence of execution, with years of technical expertise in the industry. 30 Data-Driven Business Transformation Business Strategy Aligned With Data Strategy Visit www.globaldatastrategy.com for more information
  31. 31. Global Data Strategy, Ltd. 2019 Questions? 31 • Thoughts? Ideas?

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