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DataEd Slides: Data Management + Data Strategy = Interoperability

Few organizations operate without having to exchange data. (Many do it professionally and well!) The larger the data exchange burden (DEB), the greater the organizational overhead incurred. This death by 1,000 cuts must be factored into each organization’s calculations. Unfortunately, most organizations do not know if their organization’s DEB is great or small. A somewhat greater number of organizations have organized Data Management practices. Focusing Data Management efforts on increasing interoperability by decreasing the DEB friction is a good area to “practice.”

Learning Objectives:

• Gaining a good understanding of both important topics
• Understanding that data only operates at a very intricate, specifically dependent intent and what this means
• Understand state-of-the-practice
• Coordination is key, requiring necessary but insufficient interdependencies and sequencing
• Practice makes perfect

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DataEd Slides: Data Management + Data Strategy = Interoperability

  1. 1. Data Management + Data Strategy = Interoperability © Copyright 2021 by Peter Aiken Slide # 1 paiken@plusanythingawesome.com+1.804.382.5957 Peter Aiken, PhD ? Peter Aiken, Ph.D. • I've been doing this a long time • My work is recognized as useful • Associate Professor of IS (vcu.edu) • Institute for Defense Analyses (ida.org) • DAMA International (dama.org) • MIT CDO Society (iscdo.org) • Anything Awesome (plusanythingawesome.com) • 11 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 … © Copyright 2021 by Peter Aiken Slide # 2 https://plusanythingawesome.com
  2. 2. 3 Questions 1. If things are not designed to work together – What are the chances that these things will happen to work together? 2. Data – literally the most atomic element of the collection of organizational data assets – is simultaneously the – most underutilized – least well managed What can be done to increase organizational data leverage? 3. If we have not been teaching students to design for integration for the past 30 years, where must we look to find expertise in these areas? © Copyright 2021 by Peter Aiken Slide # 3 https://plusanythingawesome.com G-Army Logistic project D e p e n d e n c i e s Purposefulness Intricacies 4 Program Data Management + Data Strategy • Context – Important data properties – Reversing data debit – Lack of correct educational focus • Data Management – What is it? – Why is it important? – State of the practice – Functions required for effective data management • Data Strategy – Structural Approach – Need for simplicity – Foundational prerequisites – The Theory of Constraints at work improving your data • Take Aways/Q&A – In Action In Concert = Interoperability – Coordination is the necessary prerequisite © Copyright 2021 by Peter Aiken Slide # https://plusanythingawesome.com = Interoperability
  3. 3. How much Data (by the minute?) For the entirety of 2020, every minute of every day: • Zoom hosted 208,000+ participants • Netflix streamed 400,000+ hours of video (697,000 in 2019) • YouTube users uploaded 500 hours of video • Consumers spent $1M online ($3,805 w/ mobile apps) • LinkedIn users applied for 69,000+ jobs • Spotify added 28 songs • Amazon shipped 6,659 packages © Copyright 2021 by Peter Aiken Slide # 5 https://plusanythingawesome.com https://www.domo.com/learn/data-never-sleeps-8 Pre-Information Age Metadata • Examples of information architecture achievements that happened well before the information age: – Page numbering – Alphabetical order – Table of contents – Indexes – Lexicons – Maps – Diagrams © Copyright 2021 by Peter Aiken Slide # 6 https://plusanythingawesome.com Example from: How to make sense of any mess by Abby Covert (2014) ISBN: 1500615994 "While we can arrange things with the intent to communicate certain information, we can't actually make information. Our users do that for us." https://www.youtube.com/watch?v=60oD1TDzAXQ&feature=emb_logo https://www.youtube.com/watch?v=r10Sod44rME&t=1s https://www.youtube.com/watch?v=XD2OkDPAl6s https://plusanythingawesome.com https://plusanythingawesome.com
  4. 4. Remove the structure and things fall apart rapidly • Better organized data increases in value © Copyright 2021 by Peter Aiken Slide # https://plusanythingawesome.com 7 https://plusanythingawesome.com https://plusanythingawesome.com Separating the Wheat from the Chaff • Data that is better organized increases in value • Poor data management practices are costing organizations money/time/effort • 80% of organizational data is ROT – Redundant – Obsolete – Trivial • The question is which data to eliminate? – Most enterprise data is never analyzed © Copyright 2021 by Peter Aiken Slide # https://plusanythingawesome.com 8 https://plusanythingawesome.com https://plusanythingawesome.com
  5. 5. Data Assets Financial Assets Real Estate Assets Inventory Assets Non- depletable Available for subsequent use Can be used up Can be used up Non- degrading √ √ Can degrade over time Can degrade over time Durable Non-taxed √ √ Strategic Asset √ √ √ √ Data Assets Win! • Today, data is the most powerful, yet underutilized and poorly managed organizational asset • Data is your – Sole – Non-depletable – Non-degrading – Durable – Strategic • Asset – Data is the new oil! – Data is the new (s)oil! – Data is the new bacon! • As such, data deserves: – It's own strategy – Attention on par with similar organizational assets – Professional ministration to make up for past neglect © Copyright 2021 by Peter Aiken Slide # 9 https://plusanythingawesome.com Asset: A resource controlled by the organization as a result of past events or transactions and from which future economic benefits are expected to flow [Wikipedia] Data Assets Win! Organizational Data Machine © Copyright 2021 by Peter Aiken Slide # 10 https://plusanythingawesome.com Inputs (from Citizens) Outputs (to Citizens and others) Organizational Data Machine (ODM) The Data Machine
  6. 6. © Copyright 2021 by Peter Aiken Slide # 11 https://plusanythingawesome.com All inputs are data All outputs are data All inputs are data All inputs are data All inputs are data All outputs are data All outputs are data All outputs are data All inputs are data All outputs are data All inputs are data All outputs are data All inputs are data All outputs are data The Data Machine How to determine what to manage formally? Too much requires expensive and slow bureaucracy ––––––––––––––––––––– Too little misses opportunities Interoperability is the primary value determinant The Data Machine The Data Matrix © Copyright 2021 by Peter Aiken Slide # 12 https://plusanythingawesome.com Inputs (from data machines) Outputs (to data machines) Data Matrix (n dimensions) Process Data machine-data can be, and often is, analyzed for purposes beyond its original collection purpose. These unspecified, unknown, complex interactions comprise the data matrix. Much more of this is type of data sharing is happening than most are aware.
  7. 7. © Copyright 2021 by Peter Aiken Slide # 13 Data Properties that should be more widely known • General low data literacy exists – Even among data specialists • Lots of data exists – Most of it is not valuable – The good stuff is uniquely-valuable – Most of what exists has been created is relatively recently • Organizations have not cared well for data in the past – Two worlds exist – Second world data challenges https://plusanythingawesome.com Data Debit – Getting Back to Zero • Data debit – The time and effort it will take to return your data to a governed state from its likely current state of ungoverned. • Getting back to zero – Involves undoing existing stuff – Likely new skills are required • At zero-must start from scratch – Typically requires annual proof of value • Now you need to get good at both – Almost all data challenges involve interoperability – Little guidance at optimizing data management practices – Very little at getting back to zero © Copyright 2021 by Peter Aiken Slide # 14 https://plusanythingawesome.com
  8. 8. What do we teach knowledge workers about data? © Copyright 2021 by Peter Aiken Slide # 15 https://plusanythingawesome.com What percentage of the deal with it daily? What do we teach IT professionals about data? © Copyright 2021 by Peter Aiken Slide # 16 https://plusanythingawesome.com • 1 course – How to build a new database • What impressions do IT professionals get from this education? – Data is a technical skill that is needed when developing new databases
  9. 9. Confusion • IT thinks data is a business problem – "If they can connect to the server, then my job is done!" • The business thinks IT is managing data adequately – "Who else would be taking care of it?" © Copyright 2021 by Peter Aiken Slide # 17 https://plusanythingawesome.com Bad Data Decisions Spiral © Copyright 2021 by Peter Aiken Slide # 18 https://plusanythingawesome.com Bad data decisions Technical deci- sion makers are not data knowledgable Business decision makers are not data knowledgable Poor organizational outcomes Poor treatment of organizational data assets Poor quality data
  10. 10. Put simply, organizations: © Copyright 2021 by Peter Aiken Slide # 19 https://plusanythingawesome.com • Have little idea what data they have • Do not know where it is (and) • Do not know what their knowledge workers do with it https://plusanythingawesome.com Quality data work products do not happen accidentally! • Data management happens 'pretty well' at the workgroup level – Defining characteristic of a workgroup – Without guidance, what are the chances that all workgroups are pulling toward the same objectives? – Consider the time spent attempting informal practices • Data chaff becomes sand – Preventing smooth interoperation and exchanges – Death by 1,000 cuts that have been difficult to account for • Organizations and individuals lack – Knowledge – Skills • Data Management (how) • Data Strategy (why) – Pain by lots of unnecessary cuts that have been difficult to account for © Copyright 2021 by Peter Aiken Slide # 20 https://plusanythingawesome.com
  11. 11. 21 Program Data Management + Data Strategy • Context – Important data properties – Reversing data debit – Lack of correct educational focus • Data Management – What is it? – Why is it important? – State of the practice – Functions required for effective data management • Data Strategy – Structural Approach – Need for simplicity – Foundational prerequisites – The Theory of Constraints at work improving your data • Take Aways/Q&A – In Action In Concert = Interoperability – Coordination is the necessary prerequisite © Copyright 2021 by Peter Aiken Slide # https://plusanythingawesome.com = Interoperability Data Management - Wikipedia Definition Note: This is a broad definition and encompasses professions with no technical contact data management technologies such as database management systems "Data Resource Management is the development and execution of architectures, policies, practices and procedures that properly manage the full data lifecycle needs of an enterprise." http://dama.org © Copyright 2021 by Peter Aiken Slide # 22 https://plusanythingawesome.com
  12. 12. © Copyright 2021 by Peter Aiken Slide # 23 https://plusanythingawesome.com Misunderstanding Data Management https://plusanythingawesome.com © Copyright 2021 by Peter Aiken Slide # https://plusanythingawesome.com Metadata Management 24 Data Management Body of Knowledge (DM BoK V2) Practice Areas from The DAMA Guide to the Data Management Body of Knowledge 2E © 2017 by DAMA International
  13. 13. Data model focus is typically domain specific © Copyright 2021 by Peter Aiken Slide # 25 https://plusanythingawesome.com Program A Program C Program B Focus of a software engineering effort Underutilized data modeling effort Database Architecture Focus Can Vary © Copyright 2021 by Peter Aiken Slide # 26 https://plusanythingawesome.com Application domain 1 Program A Program C Program B Focus of a software engineering effort Underutilized data modeling effort Better utilized data modeling effort ERPs and COTS are marketed as being similarly integrated! Program F Program E Program G Program H Program I Application domain 2 Application domain 3 Program D
  14. 14. D a t a D a t a D a t a D a t a D a t a D a t a D a t a Program A Program B Program C Program F Program E Program D Program G Program H Program I Application domain 1 Application domain 2 Application domain 3 D a t a D a t a D a t a Data Focus has Greater Potential Business Value • Broader focus than either software architecture or database architecture • Analysis scope is on the system wide use of data • Problems caused by data exchange or interface problems • Architectural goals more strategic than operational © Copyright 2021 by Peter Aiken Slide # 27 https://plusanythingawesome.com Data Management Context • Organization wide focus • Requirement is to "understand" • Understanding is of both current and future needs • Making data effective and efficient • Leverage data to support organizational activities © Copyright 2021 by Peter Aiken Slide # 28 https://plusanythingawesome.com Less ROT Technologies Process People
  15. 15. "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) © Copyright 2021 by Peter Aiken Slide # 29 https://plusanythingawesome.com Data Management "Understanding the current and future data needs of an enterprise and making that data effective and efficient in supporting business activities" Blind Persons and the Elephant © Copyright 2021 by Peter Aiken Slide # 30 https://plusanythingawesome.com http://www.dailymirror.lk/print/opinion/editorial-we-need-to-become-channels-of-peace/172-27164 It is like a fan! It is like a snake! It is like a wall! It is like a rope! It is like a tree!
  16. 16. © Copyright 2021 by Peter Aiken Slide # 31 https://plusanythingawesome.com Unrefined data management definition Sources Uses Data Management © Copyright 2021 by Peter Aiken Slide # 32 https://plusanythingawesome.com More refined data management definition Sources Reuse Data Management ➜ ➜
  17. 17. Data Management © Copyright 2021 by Peter Aiken Slide # 33 https://plusanythingawesome.com Sources ➜ Use ➜ Reuse ➜ Formal Data Reuse Management Data Why is data management so important? © Copyright 2021 by Peter Aiken Slide # 34 https://plusanythingawesome.com Garbage In ➜ Garbage Out! +
  18. 18. © Copyright 2021 by Peter Aiken Slide # 35 https://plusanythingawesome.com Perfect Model Garbage Data Garbage Results Data Warehouse Machine Learning Business Intelligence Block Chain AI MDM Data Governance Analytics Technology GI➜GO! © Copyright 2021 by Peter Aiken Slide # 36 https://plusanythingawesome.com Perfect Model Garbage Data Garbage Results Data Warehouse Machine Learning Block Chain AI MDM Analytics Technology Data Governance GI➜GO! Business Intelligence
  19. 19. © Copyright 2021 by Peter Aiken Slide # 37 https://plusanythingawesome.com Perfect Model Quality Data Garbage Results Data Warehouse Machine Learning Business Intelligence Block Chain AI MDM Analytics Technology Data Governance GI➜GO! © Copyright 2021 by Peter Aiken Slide # 38 https://plusanythingawesome.com Perfect Model Quality Data Good Results Data Warehouse Machine Learning Business Intelligence Block Chain AI MDM Analytics Technology Data Governance Quality In ➜ Quality Out!
  20. 20. Data challenges • Data management is consumed with interoperability • We assume all datasets to be perfect - just as in class • We have not been teaching the skills required to undo the mess that we were left with © Copyright 2021 by Peter Aiken Slide # 39 https://plusanythingawesome.com © Copyright 2021 by Peter Aiken Slide # 40 https://plusanythingawesome.com Data Management Body of Knowledge (DM BoK V2) Practice Areas from The DAMA Guide to the Data Management Body of Knowledge 2E © 2017 by DAMA International
  21. 21. 41 Program Data Management + Data Strategy • Context – Important data properties – Reversing data debit – Lack of correct educational focus • Data Management – What is it? – Why is it important? – State of the practice – Functions required for effective data management • Data Strategy – Structural Approach – Need for simplicity – Foundational prerequisites – The Theory of Constraints at work improving your data • Take Aways/Q&A – In Action In Concert = Interoperability – Coordination is the necessary prerequisite © Copyright 2021 by Peter Aiken Slide # https://plusanythingawesome.com = Interoperability Strategy challenges • Most attempt to write the world's best pop song on the very first try – To much focus on the document – Not enough on the processes required • TOC Cycle scope should be correcting an interoperability challenge © Copyright 2021 by Peter Aiken Slide # 42 https://plusanythingawesome.com
  22. 22. Recent data "strategies" • Data science • Big data • Analytics • SAP • Microsoft • Google • AWS • ... © Copyright 2021 by Peter Aiken Slide # 43 https://plusanythingawesome.com undefined technologies Data Strategy in Context – THIS IS WRONG! © Copyright 2021 by Peter Aiken Slide # https://plusanythingawesome.com Organizational Strategy IT Strategy Data Strategy x 44
  23. 23. Organizational Strategy IT Strategy This is correct … © Copyright 2021 by Peter Aiken Slide # https://plusanythingawesome.com Data Strategy 45 What is Strategy? • Current use derived from military - a pattern in a stream of decisions [Henry Mintzberg] © Copyright 2021 by Peter Aiken Slide # 46 https://plusanythingawesome.com A thing
  24. 24. Every Day Low Price Former Walmart Business Strategy © Copyright 2021 by Peter Aiken Slide # 47 https://plusanythingawesome.com © Copyright 2021 by Peter Aiken Slide # 48 https://plusanythingawesome.com https://plusanythingawesome.com Wayne Gretzky’s Definition of Strategy He skates to where he thinks the puck will be ...
  25. 25. Strategy in Action: Napoleon defeats a larger enemy • Question? – How to I defeat the competition when their forces are bigger than mine? • Answer: – Divide and conquer! – “a pattern in a stream of decisions” © Copyright 2021 by Peter Aiken Slide # 49 https://plusanythingawesome.com Supply Line Metadata © Copyright 2021 by Peter Aiken Slide # 50 https://plusanythingawesome.com https://plusanythingawesome.com
  26. 26. First Divide © Copyright 2021 by Peter Aiken Slide # 51 https://plusanythingawesome.com https://plusanythingawesome.com Then Conquer © Copyright 2021 by Peter Aiken Slide # 52 https://plusanythingawesome.com https://plusanythingawesome.com
  27. 27. Strategy that winds up only on a shelf is not useful © Copyright 2021 by Peter Aiken Slide # 53 https://plusanythingawesome.com https://plusanythingawesome.com Data Strategy Strategy © Copyright 2021 by Peter Aiken Slide # 54 https://plusanythingawesome.com A pattern in a stream of decisions
  28. 28. Our barn had to pass a foundation inspection • Before further construction could proceed • No IT equivalent © Copyright 2021 by Peter Aiken Slide # 55 https://plusanythingawesome.com https://plusanythingawesome.com You can accomplish Advanced Data Practices without becoming proficient in the Foundational Data Practices however this will: • Take longer • Cost more • Deliver less • Present greater risk (with thanks to Tom DeMarco) Data Management Practices Hierarchy © Copyright 2021 by Peter Aiken Slide # Advanced Data Practices • MDM • Mining • Big Data • Analytics • Warehousing • SOA Foundational Data Practices Data Platform/Architecture Data Governance Data Quality Data Operations Data Management Strategy T e c h n o l o g i e s C a p a b i l i t i e s 56 https://plusanythingawesome.com https://plusanythingawesome.com
  29. 29. © Copyright 2021 by Peter Aiken Slide # 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$Areas Data Governance Data Management Strategy Data Operations Platform Architecture Supporting Processes Maintain fit-for-purpose data, efficiently and effectively 57 https://plusanythingawesome.com Manage data coherently Manage data assets professionally Data life cycle management Organizational support Data Quality Data$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$Areas Data$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 DMM℠ Structure of 5 Integrated DM Practice Areas © Copyright 2021 by Peter Aiken Slide # Data architecture implementation Data Governance Data Management Strategy Data Operations Platform Architecture Supporting Processes Maintain fit-for-purpose data, efficiently and effectively 58 https://plusanythingawesome.com 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 3 3 1 Supporting Processes Optimized Measured Defined Managed Initial Optimized Measured Defined Managed Initial Optimized Measured Defined Managed Initial Optimized Measured Defined Managed Initial Your data foundation can only be as strong as its weakest link! Optimized Measured Defined Managed Initial
  30. 30. • A management paradigm that views any manageable system as being limited in achieving more of its goals by a small number of constraints • There is always at least one constraint, and TOC uses a focusing process to identify the constraint and restructure the rest of the organization to address it • TOC adopts the common idiom "a chain is no stronger than its weakest link," processes, organizations, etc., are vulnerable because the weakest component can damage or break them or at least adversely affect the outcome © Copyright 2021 by Peter Aiken Slide # 59 https://plusanythingawesome.com https://en.wikipedia.org/wiki/Theory_of_constraints (TOC) Theory of Constraints - Generic © Copyright 2021 by Peter Aiken Slide # 60 https://plusanythingawesome.com Identify the current constraints, the components of the system limiting goal realization Make quick improvements to the constraint using existing resources Review other activities in the process facilitate proper alignment and support of constraint If the constraint persists, identify other actions to eliminate the constraint Repeat until the constraint is eliminated
  31. 31. Theory of Constraints at work improving your data © Copyright 2021 by Peter Aiken Slide # 61 https://plusanythingawesome.com In your analysis of how organization data can best support organizational strategy one thing is blocking you most - identify it! Try to fix it rapidly with out restructuring (correct it operationally) Improve existing data evolution activities to ensure singular focus on the current objective Restructure to address constraint Repeat until data better supports strategy Data Management + Data Strategy = Interoperability © Copyright 2021 by Peter Aiken Slide # 62 https://plusanythingawesome.com Organizational Strategy Data Strategy IT Projects Organizational Operations Data Management Data asset support for organizational strategy What the data assets need to do to support strategy How well data is supporting strategy Operational feedback How IT supports strategy Other aspects of organizational strategy
  32. 32. 63 Program Data Management + Data Strategy • Context – Important data properties – Reversing data debit – Lack of correct educational focus • Data Management – What is it? – Why is it important? – State of the practice – Functions required for effective data management • Data Strategy – Structural Approach – Need for simplicity – Foundational prerequisites – The Theory of Constraints at work improving your data • Take Aways/Q&A – In Action In Concert = Interoperability – Coordination is the necessary prerequisite © Copyright 2021 by Peter Aiken Slide # https://plusanythingawesome.com = Interoperability • This discipline has not had 8,000 years to formalize practices ➡ GAAP • Your data is a mess and requires professional ministration to make up for past neglect • Your folks don't know how to use or improve it effectively • You likely require a new business data program • Data strategy and data management are major data program components, in concert, they must focus on 1. Improving organizational data 2. Improving the way people use data 3. Improving how people use better data to support strategy Take Aways © Copyright 2021 by Peter Aiken Slide # 64 This can only be accomplished incrementally using an iterative, approach focusing on one aspect at a time and applying formal transformation methods data program! business https://plusanythingawesome.com
  33. 33. Data Architecture and Data Modeling Differences: Achieving a common understanding 11 May 2021 Why Data Modeling Is Fundamental 8 June 2021 Business Value through Reference & Master Data Strategies 13 July 2021 Upcoming Events © Copyright 2021 by Peter Aiken Slide # 65 https://plusanythingawesome.com Brought to you by: Time: 19:00 UTC (2:00 PM NYC) | Presented by: Peter Aiken, PhD paiken@plusanythingawesome.com +1.804.382.5957 Questions? Thank You! © Copyright 2021 by Peter Aiken Slide # 66 + =

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