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DAS Slides: Master Data Management – Aligning Data, Process, and Governance

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DAS Slides: Master Data Management – Aligning Data, Process, and Governance

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Master Data Management (MDM) provides organizations with an accurate and comprehensive view of their business-critical data such as Customers, Products, Vendors, and more. While mastering these key data areas can be a complex task, the value of doing so can be tremendous – from real-time operational integration to data warehousing and analytic reporting. This webinar provides practical strategies for gaining value from your MDM initiative, while at the same time assuring a solid architectural and governance foundation that will ensure long-term, enterprise-wide success.

Master Data Management (MDM) provides organizations with an accurate and comprehensive view of their business-critical data such as Customers, Products, Vendors, and more. While mastering these key data areas can be a complex task, the value of doing so can be tremendous – from real-time operational integration to data warehousing and analytic reporting. This webinar provides practical strategies for gaining value from your MDM initiative, while at the same time assuring a solid architectural and governance foundation that will ensure long-term, enterprise-wide success.

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DAS Slides: Master Data Management – Aligning Data, Process, and Governance

  1. 1. Copyright Global Data Strategy, Ltd. 2020 Master Data Management Aligning Data, Process, and Governance Donna Burbank Global Data Strategy, Ltd. April 23rd , 2020 Follow on Twitter @donnaburbank Twitter Event hashtag: #DAStrategies
  2. 2. Simplifying Advanced Data Workloads with NoSQL Data Management for Modern Data Demands David Jones-Gilardi Developer Advocate @DataStax
  3. 3. 2 NoSQL and Cassandra: Foundational Capability LEGACY DATA INTEGRATION REAL-TIME, STREAMING, EVENTS DISPARATE, SILO’D DATA
  4. 4. 3 Modern Data Diversity and Complexity LEGACY DATA INTEGRATION REAL-TIME, STREAMING, EVENTS DISPARATE, SILO’D DATA DATA SECURITY / SOVEREIGNTY UNPREDICTABLE SCALE HYBRID, MULTI, INTER-CLOUD
  5. 5. Modern Data Brings Workload Complexity Today’s Related Data and Complex Workloads Traditional Siloed Data and Workload Management >
  6. 6. Data Management Evolution { : } 2020’s2000’s1980’s
  7. 7. So, how do we solve for mixed workloads? 6 How complex is your query? • Simple - Single Partition/Single Index Lookup, Single Iteration • Complex - Full Scan, Large Aggregation, Unknown Iterations • In between - Multiple Partition/Indexes, Aggregations, or Multiple Iterations How fast do you need it? • Machine Time < the time it takes to interrupt a user process • Human Time < time a user will wait • Offline Time is Everything else
  8. 8. Mixed Workload Coverage – Customer 360 Queries Offline fast Human fast Machine fast CQL Search Analytics Response time Simple Complex 1. Find me Dave 2. Find me all people with similar names to ‘Dave’ 3. Tell me if there are duplicate Daves 4. Find how Dave and Jenn are connected 5. Find how influential Dave is in my application 6. Show Dave what songs are trending for anyone with the same preferences while he looks for a song to play in his mobile app 1 DSE Graph 4 5 3 2 Stream Processing 6
  9. 9. 8 Fraud Anomaly detection and connected components IoT Act on sensors and analyze the network Recommendation Systems Your preferences and your network’s preferences Law Enforcement Bad actor identification and criminal network activity Fleet Management Vehicle tracking and path optimization New Opportunities BLENDED WORKLOADS AT SCALE WITH DATASTAX Manage Seamlessly with one Database Graph, Analytics, Search, Advanced Security, Stream Processing, In-memory Engine
  10. 10. 9 Simplified Data Complexity A Single Data Platform Mixed Workloads at Scale Scale apps for complex data & workloads with ease Real-time Intelligence Access the full value of your ever-changing data Multi-DC, Multi- Platform Deployment and operations wherever you choose to deploy SINGLE CLOUD MULTI- / INTER-CLOUD HYBRID CLOUD ON PREMISES
  11. 11. Learn about Cassandra and TinkerPop today! 10 academy.datastax.co m @DataStaxDevs datastax.com/download s @DataStax
  12. 12. Thank you
  13. 13. Copyright Global Data Strategy, Ltd. 2020 Master Data Management Aligning Data, Process, and Governance Donna Burbank Global Data Strategy, Ltd. April 23rd , 2020 Follow on Twitter @donnaburbank Twitter Event hashtag: #DAStrategies
  14. 14. Global Data Strategy, Ltd. 2020 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 awarded the Excellence in Data Management Award from DAMA International. 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
  15. 15. Global Data Strategy, Ltd. 2020 DATAVERSITY Data Architecture Strategies • January 23 Emerging Trends in Data Architecture – What’s the Next Big Thing? • February 27 Building a Data Strategy - Practical Steps for Aligning with Business Goals • March 26 Cloud-Based Data Warehousing – What's New and What Stays the Same • April 23 Master Data Management – Aligning Data, Process, and Governance • May 28 Data Governance and Data Architecture – Alignment and Synergies • June 25 Enterprise Architecture vs. Data Architecture • July 22 Best Practices in Metadata Management • August 27 Data Quality Best Practices • September 24 Data Virtualization – Separating Myth from Reality • October 22 Data Architect vs. Data Engineer vs. Data Modeler • December 1 Graph Databases: Practical Use Cases 3 This Year’s Lineup
  16. 16. Global Data Strategy, Ltd. 2020 What We’ll Cover Today • Master Data Management (MDM) provides organizations with an accurate and comprehensive view of business-critical data such as Customers, Products, Vendors, and more. • While mastering these key data areas can be a complex task, the value of doing so can be tremendous – from real-time operational integration to data warehousing & analytic reporting. • This webinar provides practical strategies for gaining value from your MDM initiative, while at the same time assuring a solid architectural and governance foundation that will ensure long- term, enterprise-wide success. 4
  17. 17. Global Data Strategy, Ltd. 2020 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 2020 Global Data Strategy, Ltd MDM is Part of a Wider Data Strategy www.globaldatastrategy.com
  18. 18. Global Data Strategy, Ltd. 2020 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
  19. 19. Global Data Strategy, Ltd. 2020 What is Master Data? 7 Real-world examples The “dead” living organismThe $1M cheese slice The $2M baby bottle Which Dr. Smith is credentialled for heart surgery? Which Michael Jones is the high-net worth customer? How do we define Regions, Markets, Locations, Catchments, Sites, etc.?
  20. 20. Global Data Strategy, Ltd. 2020 What is Master Data? What is Reference Data? 8 How do we define Regions, Markets, Locations, Catchments, Sites, etc.? One person’s Master Data is another person’s Reference Data… vs. Address Line 1 Address Line 2 City State AL AK AR AZ CA CO ..etc. Master Data Reference Data
  21. 21. Global Data Strategy, Ltd. 2020 … but Don’t make this overly Pedantic … 9
  22. 22. Global Data Strategy, Ltd. 2020 Understanding Your Customer 10 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
  23. 23. Global Data Strategy, Ltd. 2020 11 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 2019 Address = Zurich, Switzerland
  24. 24. Global Data Strategy, Ltd. 2020 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 12 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.
  25. 25. Global Data Strategy, Ltd. 2020 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 13 Master Data: Customer Master Data: Product Master Data: Location Reference Data: Country Codes Reference Data: State Codes Transaction Data
  26. 26. Global Data Strategy, Ltd. 2020 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 Data Stewardship Validation
  27. 27. Global Data Strategy, Ltd. 2020 ETL Master Data Overview 15 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 First Name Family Name Address Line 1 Address Line 2 City State Phone Email Spouse First Name Postal Code Customer ID First Name Family Name Account # Credit Balance First Name Middle Name Family Name Email Twitter ID Gender First Name Family Name Address Line 1 Address Line 2 City State Credit Card # Phone First Name Family Name Address Line 1 Address Line 2 City State Country Phone
  28. 28. Global Data Strategy, Ltd. 2020 ETL Master Data Overview 16 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/subset of the source system models. Data Stewardship Validation Customer ID First Name Family Name Address Line 1 Address Line 2 City State Country Phone Email Country Codes State Codes First Name Family Name Address Line 1 Address Line 2 City State Phone Email Spouse First Name Postal Code Customer ID First Name Family Name Account # Credit Balance First Name Middle Name Family Name Email Twitter ID Gender First Name Family Name Address Line 1 Address Line 2 City State Credit Card # Phone First Name Family Name Address Line 1 Address Line 2 City State Country Phone
  29. 29. Global Data Strategy, Ltd. 2020 ETL Master Data Overview 17 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 Publish & Subscribe Data Stewardship Validation First Name = John Family Name = Smith Address Line 1 = 101 Main ST Address Line 2 City = Anywhere State = Texas ZIP = 10101 Phone = 555 927 1212 Email = johns@gmail.com Spouse = Mary First Name = Jack Postal Code = 10101 Customer ID = 123 First Name = John Family Name = Smith Account # Credit Balance First Name = J. Middle Name Family Name = Smith Email = goaway@me.net Twitter ID = @johns Gender = Male First Name = Johnn Family Name Address Line 1 = 101 Main Street Address Line 2 = Apt 2 City = Plano State = TX Credit Card # Phone = +1 555 927 1212 First Name = John Family Name = Smith Address Line 1 = 101 Main St Address Line 2 City = Anywhere State = TX Country = USA Phone = x1212 Customer ID = 123 First Name = John Family Name = Smith Address Line 1 = 101 Main ST Address Line 2 = Apt 2 City = Anywhere State = TX Postal Code = 10101 Country = USA Phone = +1 555 927 1212 Email = johns@gmail.com Matching Rules help create a “Golden Record” Data Quality & Matching
  30. 30. Global Data Strategy, Ltd. 2020 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. 18 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
  31. 31. Global Data Strategy, Ltd. 2020 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. 19
  32. 32. Global Data Strategy, Ltd. 2020 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 “Human in the Loop” – Data Stewards can validate match candidates. Data Stewardship Validation
  33. 33. Global Data Strategy, Ltd. 2020 ETL Master Data Overview 21 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
  34. 34. Global Data Strategy, Ltd. 2020 ETL Master Data Overview 22 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
  35. 35. Global Data Strategy, Ltd. 2020 MDM is not Reporting or Analytics -- It can be a Source 23 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
  36. 36. Global Data Strategy, Ltd. 2020 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 : 24 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
  37. 37. Global Data Strategy, Ltd. 2020 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
  38. 38. Global Data Strategy, Ltd. 2020 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.? 26 Identifying key data dependencies in core business processes
  39. 39. Global Data Strategy, Ltd. 2020 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. 27 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
  40. 40. Global Data Strategy, Ltd. 2020 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
  41. 41. Global Data Strategy, Ltd. 2020 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
  42. 42. Global Data Strategy, Ltd. 2020 Summary 30 • 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.
  43. 43. Global Data Strategy, Ltd. 2020 DATAVERSITY Data Architecture Strategies • January 23 Emerging Trends in Data Architecture – What’s the Next Big Thing? • February 27 Building a Data Strategy - Practical Steps for Aligning with Business Goals • March 26 Cloud-Based Data Warehousing – What's New and What Stays the Same • April 23 Master Data Management – Aligning Data, Process, and Governance • May 28 Data Governance and Data Architecture – Alignment and Synergies • June 25 Enterprise Architecture vs. Data Architecture • July 22 Best Practices in Metadata Management • August 27 Data Quality Best Practices • September 24 Data Virtualization – Separating Myth from Reality • October 22 Data Architect vs. Data Engineer vs. Data Modeler • December 1 Graph Databases: Practical Use Cases 31 Join us next month
  44. 44. Global Data Strategy, Ltd. 2020 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. 32 Data-Driven Business Transformation Business Strategy Aligned With Data Strategy Visit www.globaldatastrategy.com for more information
  45. 45. Global Data Strategy, Ltd. 2020 Questions? 33 • Thoughts? Ideas? www.globaldatastrategy.com

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