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DataEd Slides: Data Strategy — Plans Are Useless, but Planning Is Invaluable

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Too often, I hear the question, “Can you help me with our Data Strategy?” Unfortunately, for most, this is the wrong request because it focuses on the least valuable component — the Data Strategy itself. A more useful request is, “Can you help me apply data strategically?” Yes, at early maturity phases, the process of developing strategic thinking about data is more important than the actual product! Trying to write a good (much less perfect) Data Strategy on the first attempt is generally not productive — particularly giving the widespread acceptance of Mike Tyson’s truism: “Everybody has a plan until they get punched in the face.” Refocus on learning how to iteratively improve the way data is strategically applied. This will permit data-based strategy components to keep up with agile, evolving organizational strategies. This approach can also contribute to three primary organizational data goals. Learn how improving the following will help in ways never imagined:

• Your organization’s data
• The way your people use data
• The way your people use data to achieve your organizational strategy
Data is your sole non-depletable, non-degradable, durable strategic asset, and it is pervasively shared across every organizational area. Addressing existing challenges programmatically includes overcoming necessary but insufficient prerequisites and developing a disciplined, repeatable means of improving business objectives. This process (based on the theory of constraints) is where the strategic data work really occurs as organizations identify prioritized areas where better assets, literacy, and support (Data Strategy components) can help an organization better achieve specific strategic objectives. Then the process becomes lather, rinse, and repeat. Several complementary concepts are also covered, including:

• A cohesive argument for why Data Strategy is necessary for effective Data Governance
• An overview of prerequisites for effective strategic use of Data Strategy, as well as common pitfalls
• A repeatable process for identifying and removing data constraints
• The importance of balancing business operation and innovation

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DataEd Slides: Data Strategy — Plans Are Useless, but Planning Is Invaluable

  1. 1. Data Strategy © Copyright 2021 by Peter Aiken Slide # 1paiken@plusanythingawesome.com+1.804.382.5957 Peter Aiken, PhD Plans Are Useless but Planning is Invaluable 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 #https://plusanythingawesome.com 2
  2. 2. Context • Strategy – Inherently a repetitive process that can be easily improved • Dependency – Data strategy exists to support organizational strategy • Evolution – At early maturity phases, the process is more important than the product! • Output – Plans are of limited value anyway and always discount obstacles • Technology – People and process challenges are 95% of the problem • Nirvana – How do I get to Carnegie Hall? – Practice Practice Practice © Copyright 2021 by Peter Aiken Slide # 3https://plusanythingawesome.com A Musical Analogy © Copyright 2021 by Peter Aiken Slide # 4https://plusanythingawesome.com + = https://plusanythingawesome.com https://www.youtube.com/watch?v=4n1GT-VjjVs&frags=pl%2Cwn | only played 3 times according to https://www.setlist.fm/stats/bruce-springsteen-2bd6dcce.html
  3. 3. 5 Program © Copyright 2021 by Peter Aiken Slide #https://plusanythingawesome.com • A data strategy specifies how data assets are to be used to support the organizational strategy – What is strategy? – What is a data strategy? – How do they work together? • A data strategy is necessary for effective data governance – Improve your organization’s data – Improve the way people use their data – Improving how people use data to support their organizational strategy • Effective Data Strategy Prerequisites – Lack of organizational readiness – Failure to compensate for the lack of data competencies – Eliminating the barriers to leveraging data, the seven deadly data sins • Data Strategy Development Phase II–Iterations – Lather, rinse, repeat – A balanced approach is required • Q&A Data Strategy Plans Are Useless but Planning is Invaluable What is Strategy? • Current use derived from military - a pattern in a stream of decisions [Henry Mintzberg] © Copyright 2021 by Peter Aiken Slide # 6https://plusanythingawesome.com A thing An outcome
  4. 4. Every Day Low Price Former Walmart Business Strategy © Copyright 2021 by Peter Aiken Slide # 7https://plusanythingawesome.com Wayne Gretzky’s Definition of Strategy © Copyright 2021 by Peter Aiken Slide # He skates to where he thinks the puck will be ... 8https://plusanythingawesome.com
  5. 5. Strategy in Action: Napoleon faces a larger enemy • Question? – How do 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 # 9https://plusanythingawesome.com Supply Line Metadata (as part of a divide and conquer strategy) © Copyright 2021 by Peter Aiken Slide # 10https://plusanythingawesome.comhttps://plusanythingawesome.com
  6. 6. First Divide © Copyright 2021 by Peter Aiken Slide # 11https://plusanythingawesome.comhttps://plusanythingawesome.com Then Conquer © Copyright 2021 by Peter Aiken Slide # 12https://plusanythingawesome.comhttps://plusanythingawesome.com
  7. 7. Complex Strategy • First – Hit both armies hard at just the right spot • Then – Turn right and defeat the Prussians • Then – Turn left and defeat the British © Copyright 2021 by Peter Aiken Slide # Whilesomeoneisshootingatyou! 13https://plusanythingawesome.com Strategy Example 1 © Copyright 2021 by Peter Aiken Slide # Good Guys (Us) Bad Guys (Them) 14https://plusanythingawesome.com
  8. 8. Strategy Example 2 © Copyright 2021 by Peter Aiken Slide # Good Guys (Us) Bad Guys (Them) 15https://plusanythingawesome.com Strategy Example 3 © Copyright 2021 by Peter Aiken Slide # Good Guys (Us) Bad Guys (Them) 16https://plusanythingawesome.com
  9. 9. General Dwight D. Eisenhower © Copyright 2021 by Peter Aiken Slide # 17https://plusanythingawesome.com • “In preparing for battle I have always found that plans are useless, but planning is indispensable …” – https://quoteinvestigator.com/2017/11/18/planning/ • “In preparing for battle I have always found that plans are useless, but planning is indispensable …” Strategy that winds up only on a shelf is not useful © Copyright 2021 by Peter Aiken Slide #https://plusanythingawesome.com Data Strategy Or is it? 18
  10. 10. Mike Tyson “Everybody has a plan until they get punched in the face.” http://f--f.info/?p=23071 © Copyright 2021 by Peter Aiken Slide # 19https://plusanythingawesome.com Your Data Strategy • Highest level data guidance available ... • Focusing data activities on business-goal achievement ... • Providing guidance when faced with a stream of decisions or uncertainties © Copyright 2021 by Peter Aiken Slide # 20https://plusanythingawesome.com
  11. 11. By the Book © Copyright 2021 by Peter Aiken Slide # Data Strategy Data Governance Strategy Metadata Strategy Data Quality Strategy BI/ Warehouse Strategy Data Architecture Strategy Master/ Reference Data Strategy Document/ Content Strategy Database Strategy Data Acquisition Strategy x 21https://plusanythingawesome.com Metadata Management © Copyright 2021 by Peter Aiken Slide # 22https://plusanythingawesome.com DataManagement BodyofKnowledge(DMBoKV2) Practice Areas from The DAMA Guide to the Data Management Body of Knowledge 2E © 2017 by DAMA International
  12. 12. Iteration 1 © Copyright 2021 by Peter Aiken Slide # 23https://plusanythingawesome.com Data Strategy Data Governance BI/ Warehouse Perfecting operations in 3 data management practice areas 1X 1X 1X Metadata Data Quality Iteration 2 © Copyright 2021 by Peter Aiken Slide # 24https://plusanythingawesome.com Data Strategy Data Governance BI/ Warehouse Perfecting operations in 3 data management practice areas Metadata 2X 2X 1X
  13. 13. Iteration 3 © Copyright 2021 by Peter Aiken Slide # 25https://plusanythingawesome.com Data Strategy Data Governance BI/ Warehouse Reference & Master Data Perfecting operations in 3 data management practice areas 1X 3X 3X Planning Options • Plan the entire process before beginning – One attempt • Use iterative strategy cycles – Incorporate corrective feedback on initial assumptions © Copyright 2021 by Peter Aiken Slide # Alleviate the Strategy Cycle Alleviate the Strategy Cycle Alleviate the Strategy Cycle Alleviate the Strategy Cycle Note: Numerous upfront assumptions are required because plans must be detailed, specifying end objectives 26https://plusanythingawesome.com
  14. 14. Focus evolve from reactive to proactive Strategy helps your data program © Copyright 2021 by Peter Aiken Slide # 27https://plusanythingawesome.com Over time increase capacity and improve Strategy Cycle Data Strategy Menu © Copyright 2021 by Peter Aiken Slide # 28https://plusanythingawesome.com WHY A data strategy is important to the Org. HOW It will impact the organization WHAT The future look like (Paint a picture) WHEN Can we make it happen • Forces an understanding of data importance • Creates a vision for the organization • Identifies the strategic imperatives • Defines the benefits and key measures • Describes the data management improvements needed • Outlines the approach and activities • Estimates the level of effort and investment
  15. 15. Data Strategy Measures • Effectiveness – Over time • Volume (length) – Should be shorter than the organizational strategy • Versions – Should be sequential (with score keeping) • Understanding – Common agreement can be measured © Copyright 2021 by Peter Aiken Slide # 29https://plusanythingawesome.com Managing Data with Guidance • How should data be used and in which business processes? • How is data shared among users, divisions, geographies and partners? • What processes and procedures allow for data to be changed? • Who manages approval processes? • What processes ensure compliance? • Most importantly, in what order should I approach the above list? © Copyright 2021 by Peter Aiken Slide # 30https://plusanythingawesome.com
  16. 16. Data Strategy in Context – THIS IS WRONG! © Copyright 2021 by Peter Aiken Slide # Organizational Strategy IT Strategy Data Strategy x 31https://plusanythingawesome.com Organizational Strategy IT Strategy This is correct … © Copyright 2021 by Peter Aiken Slide # Data Strategy 32https://plusanythingawesome.com
  17. 17. Other recent data "strategies" • Big Data • Data Science • Analytics • SAP • Microsoft • Google • AWS • ... © Copyright 2021 by Peter Aiken Slide # 33https://plusanythingawesome.com Recap • A data strategy specifies how data assets are to be used to support strategy – What is strategy? – What is a data strategy? – How do they work together? • Strategy evolves periodically © Copyright 2021 by Peter Aiken Slide # 34https://plusanythingawesome.com Strategic Focus A pattern in a stream of decisions Constraints limiting goal achievement
  18. 18. 35 Program © Copyright 2021 by Peter Aiken Slide #https://plusanythingawesome.com • A data strategy specifies how data assets are to be used to support the organizational strategy – What is strategy? – What is a data strategy? – How do they work together? • A data strategy is necessary for effective data governance – Improve your organization’s data – Improve the way people use their data – Improving how people use data to support their organizational strategy • Effective Data Strategy Prerequisites – Lack of organizational readiness – Failure to compensate for the lack of data competencies – Eliminating the barriers to leveraging data, the seven deadly data sins • Data Strategy Development Phase II–Iterations – Lather, rinse, repeat – A balanced approach is required • Q&A Data Strategy Plans Are Useless but Planning is Invaluable 7 Data Governance Definitions • The formal orchestration of people, process, and technology to enable an organization to leverage data as an enterprise asset – The MDM Institute • A convergence of data quality, data management, business process management, and risk management surrounding the handling of data in an organization – Wikipedia • A system of decision rights and accountabilities for information-related processes, executed according to agreed-upon models which describe who can take what actions with what information, and when, under what circumstances, using what methods – Data Governance Institute • The execution and enforcement of authority over the management of data assets and the performance of data functions – KiK Consulting • A quality control discipline for assessing, managing, using, improving, monitoring, maintaining, and protecting organizational information – IBM Data Governance Council • Data governance is the formulation of policy to optimize, secure, and leverage information as an enterprise asset by aligning the objectives of multiple functions – Sunil Soares • The exercise of authority and control over the management of data assets – DM BoK © Copyright 2021 by Peter Aiken Slide # 36https://plusanythingawesome.com
  19. 19. What is Data Governance? © Copyright 2021 by Peter Aiken Slide # Managing Data Decisions with Guidance Would you want your sole, non- depletable, non- degrading, durable, strategic asset managed without guidance? 37https://plusanythingawesome.com 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!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 # 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] 38https://plusanythingawesome.com
  20. 20. Organizational Assets • Cash & other financial instruments • Real property • Inventory • Intellectual Property • Human – Knowledge – Skills – Abilities • Financial • Organizational reputation • Good will • Brand name • Data!!! © Copyright 2021 by Peter Aiken Slide # 39https://plusanythingawesome.com Separating the Wheat from the Chaff • Better organized data increases in value © Copyright 2021 by Peter Aiken Slide #https://plusanythingawesome.com 40https://plusanythingawesome.comhttps://plusanythingawesome.com
  21. 21. Separating the Wheat from the Chaff • Better organized data increases in value • 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 41https://plusanythingawesome.comhttps://plusanythingawesome.com Data Strategy and Data Governance in Context © Copyright 2021 by Peter Aiken Slide #https://plusanythingawesome.com Organizational Strategy Data Strategy IT Projects Organizational Operations Data Governance Data asset support for organizational strategy What the data assets do to support strategy How well the data strategy is working Operational feedback How data is delivered by IT How IT supports strategy Other aspects of organizational strategy 42
  22. 22. Data Strategy & Data Governance © Copyright 2021 by Peter Aiken Slide #https://plusanythingawesome.com Data Strategy Data Governance What the data assets do to support strategy How well the data strategy is working (Business Goals) (Metadata) 43 Data Strategy Motivation • Because data points to where valuable things are located • Because data has intrinsic value by itself • Because data has inherent combinatorial value • Valuing Data – Use data to measure change – Use data to manage change – Use data to motivate change • Creating a competitive advantage with data © Copyright 2021 by Peter Aiken Slide # 44https://plusanythingawesome.com Improve your organization’s data Improve the way your people use its data Improve the way your data and your people support your organizational strategy
  23. 23. What did Rolls Royce Learn © Copyright 2021 by Peter Aiken Slide # 45https://plusanythingawesome.com from Nascar? • Old model - Sell jet engines • New model - Sell hours of powered thrust - “Power-by-the-hour” - No payment for down time - Wing to wing - When was this new model invented? https://www.youtube.com/watch?v=RRy_73ivcms 46 Program © Copyright 2021 by Peter Aiken Slide #https://plusanythingawesome.com • A data strategy specifies how data assets are to be used to support the organizational strategy – What is strategy? – What is a data strategy? – How do they work together? • A data strategy is necessary for effective data governance – Improve your organization’s data – Improve the way people use their data – Improving how people use data to support their organizational strategy • Effective Data Strategy Prerequisites – Lack of organizational readiness – Failure to compensate for the lack of data competencies – Eliminating the barriers to leveraging data, the seven deadly data sins • Data Strategy Development Phase II–Iterations – Lather, rinse, repeat – A balanced approach is required • Q&A Data Strategy Plans Are Useless but Planning is Invaluable
  24. 24. Data Strategy is Implemented in 2 Phases © Copyright 2021 by Peter Aiken Slide # 47https://plusanythingawesome.com Data Strategy What the data assets do to support strategy Phase I-Prerequisites 1) Prepare for dramatic change and determine how to do the work 2) Recruit a qualified, knowledgeable enterprise data executive (and other qualified talent) 3) Eliminate the Seven Deadly Data Sins Phase II-Iterations (Lather, Rinse, Repeat) Data Strategy is Implemented in 2 Phases © Copyright 2021 by Peter Aiken Slide # 48https://plusanythingawesome.com Data Strategy What the data assets do to support strategy Phase I-Prerequisites 1) Prepare for dramatic change and determine how to do the work 2) Recruit a qualified, knowledgeable enterprise data executive (and other qualified talent) 3) Eliminate the Seven Deadly Data Sins Phase II-Iterations (Lather, Rinse, Repeat)
  25. 25. © Copyright 2021 by Peter Aiken Slide # CIOs aren't 49https://plusanythingawesome.com © Copyright 2021 by Peter Aiken Slide # 50https://plusanythingawesome.com
  26. 26. © Copyright 2021 by Peter Aiken Slide # Chief Data Officer Combat Recasting the executive team. make full use of the most valuable assets 51https://plusanythingawesome.com © Copyright 2021 by Peter Aiken Slide # Credit: Image credit: Matt Vickers https://plusanythingawesome.com 52https://plusanythingawesome.com
  27. 27. Change the status quo! © Copyright 2021 by Peter Aiken Slide # 53https://plusanythingawesome.com • Keep in mind that the appointment of a CDO typically comes from a high-level decision. In practice, it can trigger an array of problematic reactions within the organization including: – Confusion, – Uncertainty, – Doubt, – Resentment and – Resistance. • CDOs need to rise to the challenge of changing the status quo if they expect to lead the business in making data a strategic asset. – from What Chief Data Officers Need to Do to Succeed by Mario Faria https://www.forbes.com/sites/gartnergroup/2016/04/11/what-chief-data-officers-need-to-do-to-succeed/#734d53a8434a Change Management & Leadership © Copyright 2021 by Peter Aiken Slide # 54https://plusanythingawesome.com
  28. 28. Diagnosing Organizational Readiness © Copyright 2021 by Peter Aiken Slide # 55https://plusanythingawesome.com adapted from the Managing Complex Change model by Dr. Mary Lippitt, 1987 Culture is the biggest impediment to a shift in organizational thinking about data! No cost, no registration case study download © Copyright 2021 by Peter Aiken Slide # 56https://plusanythingawesome.com 8 EXPERIENCE: Succeeding at Data Management—BigCo Attempts to Leverage Data PETER AIKEN, Virginia Commonwealth University/Data Blueprint In a manner similar to most organizations, BigCompany (BigCo) was determined to benefit strategically from its widely recognized and vast quantities of data. (U.S. government agencies make regular visits to BigCo to learn from its experiences in this area.) When faced with an explosion in data volume, increases in complexity, and a need to respond to changing conditions, BigCo struggled to respond using a traditional, information technology (IT) project-based approach to address these challenges. As BigCo was not data knowledgeable, it did not realize that traditional approaches could not work. Two full years into the initiative, BigCo was far from achieving its initial goals. How much more time, money, and effort would be required before results were achieved? Moreover, could the results be achieved in time to support a larger, critical, technology-driven challenge that also depended on solving the data challenges? While these questions remain unaddressed, these considerations increase our collective understanding of data assets as separate from IT projects. Only by reconceiving data as a strategic asset can organizations begin to address these new challenges. Transformation to a data-driven culture requires far more than technology, which remains just one of three required “stool legs” (people and process being the other two). Seven prerequisites to effectively leveraging data are necessary, but insufficient awareness exists in most organizations—hence, the widespread misfires in these areas, especially when attempting to implement the so-called big data initiatives. Refocusing on foundational data management practices is required for all organizations, regardless of their organizational or data strategies. Categories and Subject Descriptors: H.2.0 [Information Systems]: Database Management—General; E.0 [Data]: General General Terms: Management, Performance, Design Additional Key Words and Phrases: Data management, data governance, data stewardship, organizational design, CDO, CIO, chief data officer, chief information officer, data, data architecture, enterprise data exec- utive, IT management, strategy, policy, enterprise architecture, information systems, conceptual modeling, data integration, data warehousing, analytics, and business intelligence, BigCo ACM Reference Format: Peter Aiken. 2016. Experience: Succeeding at data management—BigCo attempts to leverage data. J. Data and Information Quality 7, 1–2, Article 8 (May 2016), 35 pages. DOI: http://dx.doi.org/10.1145/2893482 1. CASE INTRODUCTION Good technology in the hands of an inexperienced user rarely produces positive results. Everyone wants to “leverage” data. Today, this is most often interpreted as invest- ments in warehousing, analytics, business intelligence (BI), and so on. After all, that is what you do with an asset—you leverage it—so the asset can help you to attain strategic objectives; see Redman [2008] and Ladley [2010]. Widespread and pervasive Author’s address: P. Aiken, 10124C West Broad Street, Glen Allen, VA 23060; email: peter.aiken@vcu.edu. Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from Permissions@acm.org. 2016 Copyright is held by the owner/author(s). Publication rights licensed to ACM. ACM 1936-1955/2016/05-ART8 $15.00 DOI: http://dx.doi.org/10.1145/2893482 ACM Journal of Data and Information Quality, Vol. 7, No. 1–2, Article 8, Publication date: May 2016. • Download – http://dl.acm.org/citation.cfm?doid=2888577.2893482 or http://tinyurl.com/PeterStudy • Download Here!
  29. 29. Data Strategy is Implemented in 2 Phases © Copyright 2021 by Peter Aiken Slide # 57https://plusanythingawesome.com Data Strategy What the data assets do to support strategy Phase I-Prerequisites 1) Prepare for dramatic change and determine how to do the work 2) Recruit a qualified, knowledgeable enterprise data executive (and other qualified talent) 3) Eliminate the Seven Deadly Data Sins Phase II-Iterations (Lather, Rinse, Repeat) Data Strategy Framework (Part 1) © Copyright 2021 by Peter Aiken Slide # 58https://plusanythingawesome.com • Benefits & Success Criteria • Capability Targets • Solution Architecture • Organizational Development Solution • Organization Mission • Strategy & Objectives • Organizational Structures • Performance Measures Business Needs • Organizational / Readiness • Business Processes • Data Management Practices • Data Assets • Technology Assets Current State • Business Value Targets • Capability Targets • Tactics • Data Strategy Vision Strategic Data Imperatives Business Needs Existing Capabilities Execution
  30. 30. © Copyright 2021 by Peter Aiken Slide #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 Put simply, organizations: 59 What do we teach knowledge workers about data? © Copyright 2021 by Peter Aiken Slide # 60https://plusanythingawesome.com What percentage of the deal with it daily?
  31. 31. What do we teach IT professionals about data? © Copyright 2021 by Peter Aiken Slide # 61https://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 Bad Data Decisions Spiral © Copyright 2021 by Peter Aiken Slide # 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 62https://plusanythingawesome.com
  32. 32. Hiring Panels Are Often Challenged to Help © Copyright 2021 by Peter Aiken Slide # 63https://plusanythingawesome.com Top Data Job © Copyright 2021 by Peter Aiken Slide # 64https://plusanythingawesome.com • Dedicated solely to data asset leveraging • Unconstrained by an IT project mindset • Reporting to the business Top Operations Job Top Job Top Finance Job Top IT Job Top Marketing Job Data Governance Organization Top Data Job Enterprise Data Executive Chief Data Officer
  33. 33. The Enterprise Data Executive Takes One for the Team © Copyright 2021 by Peter Aiken Slide # 65https://plusanythingawesome.comhttps://plusanythingawesome.com Data Strategy is Implemented in 2 Phases © Copyright 2021 by Peter Aiken Slide # 66https://plusanythingawesome.com Data Strategy What the data assets do to support strategy Phase I-Prerequisites 1) Prepare for dramatic change and determine how to do the work 2) Recruit a qualified, knowledgeable enterprise data executive (and other qualified talent) 3) Eliminate the Seven Deadly Data Sins Phase II-Iterations (Lather, Rinse, Repeat)
  34. 34. Exorcising the Seven Deadly Data Sins © Copyright 2021 by Peter Aiken Slide # 67https://plusanythingawesome.com Not Understanding Data-Centric Thinking Lacking Qualified Data Leadership Not implementing a Robust, Programmatic Means of Developing Shared Data Not Aligning The Data Program with IT Projects Failing to Adequately Manage Expectations Not Sequencing Data Strategy Implementation Failing To Address Cultural And Change Management Challenges Not Understanding Data-Centric Thinking Lacking Qualified Data Leadership Not implementing a Robust, Programmatic Means of Developing Shared Data Not Aligning The Data Program with IT Projects Failing to Adequately Manage Expectations Not Sequencing Data Strategy Implementation Failing To Address Cultural And Change Management Challenges Not Understanding Data-Centric Thinking Lacking Qualified Data Leadership Not implementing a Robust, Programmatic Means of Developing Shared Data Not Aligning The Data Program with IT Projects Failing to Adequately Manage Expectations Not Sequencing Data Strategy Implementation Failing To Address Cultural And Change Management Challenges Not Understanding Data-Centric Thinking Lacking Qualified Data Leadership Not implementing a Robust, Programmatic Means of Developing Shared Data Not Aligning The Data Program with IT Projects Failing to Adequately Manage Expectations Not Sequencing Data Strategy Implementation Failing To Address Cultural And Change Management Challenges Not Understanding Data-Centric Thinking Lacking Qualified Data Leadership Not implementing a Robust, Programmatic Means of Developing Shared Data Not Aligning The Data Program with IT Projects Failing to Adequately Manage Expectations Not Sequencing Data Strategy Implementation Failing To Address Cultural And Change Management Challenges Not Understanding Data-Centric Thinking Lacking Qualified Data Leadership Not implementing a Robust, Programmatic Means of Developing Shared Data Not Aligning The Data Program with IT Projects Failing to Adequately Manage Expectations Not Sequencing Data Strategy Implementation Failing To Address Cultural And Change Management Challenges Not Understanding Data-Centric Thinking Lacking Qualified Data Leadership Not implementing a Robust, Programmatic Means of Developing Shared Data Not Aligning The Data Program with IT Projects Failing to Adequately Manage Expectations Not Sequencing Data Strategy Implementation Failing To Address Cultural And Change Management Challenges g Data- ng Lacking Qualified Data Leadership Failing to Implement a Programmatic Way to Share Data Not Aligning the Data Program with IT Projects ailing to Adequately anage Expectations Not Sequencing Data Strategy Implementation Not Addressing Cultural and Change Management Challenges 2 3 4 5 6 7 Not Understanding Data- Centric Thinking Lacking Qualified Data Leadership Failing to Implement a Programmatic Way to Share Data Not Aligning the Data Program with IT Projects Failing to Adequately Manage Expectations Not Sequencing Data Strategy Implementation Not Addressing Cultural and Change Management Challenges 1 2 3 4 5 6 7 Not Understanding Data- Centric Thinking Lacking Qualified Data Leadership Failing to Implement a Programmatic Way to Share Data Not Aligning the Data Program with IT Projects Failing to Adequately Manage Expectations Not Sequencing Data Strategy Implementation Not Addressing Cultural and Change Management Challenges 1 2 3 4 5 6 7t Understanding Data- Centric Thinking Lacking Qualified Data Leadership Failing to Implement a Programmatic Way to Share Data Not Aligning the Data Program with IT Projects Failing to Adequately Manage Expectations Not Sequencing Data Strategy Implementation Not Addressing Cultural and Change Management Challenges 1 2 3 4 5 6 7 acking Qualified Data Leadership Failing to Implement a Programmatic Way to Share Data Not Aligning the Data Program with IT Projects ately tions Not Sequencing Data Strategy Implementation Not Addressing Cultural and Change Management Challenges 2 3 4 6 7 Not Understanding Data- Centric Thinking Lacking Qualified Data Leadership Failing to Implement a Programmatic Way to Share Data Not Aligning the Data Program with IT Projects Failing to Adequately Manage Expectations Not Sequencing Data Strategy Implementation Not Addressing Cultural and Change Management Challenges 1 2 3 4 5 6 7 Not Understanding Data- Centric Thinking Lacking Qualified Data Leadership Failing to Implement a Programmatic Way to Share Data Not Aligning the Data Program with IT Projects Failing to Adequately Manage Expectations Not Sequencing Data Strategy Implementation Not Addressing Cultural and Change Management Challenges 1 2 3 4 5 6 7 © Copyright 2021 by Peter Aiken Slide # 68https://plusanythingawesome.com http://www.thedatadoctrine.com Introducing The Data Doctrine
  35. 35. 69 Program © Copyright 2021 by Peter Aiken Slide #https://plusanythingawesome.com • A data strategy specifies how data assets are to be used to support the organizational strategy – What is strategy? – What is a data strategy? – How do they work together? • A data strategy is necessary for effective data governance – Improve your organization’s data – Improve the way people use their data – Improving how people use data to support their organizational strategy • Effective Data Strategy Prerequisites – Lack of organizational readiness – Failure to compensate for the lack of data competencies – Eliminating the barriers to leveraging data, the seven deadly data sins • Data Strategy Development Phase II–Iterations – Lather, rinse, repeat – A balanced approach is required • Q&A Data Strategy Plans Are Useless but Planning is Invaluable Data Strategy is Implemented in 2 Phases © Copyright 2021 by Peter Aiken Slide # 70https://plusanythingawesome.com Data Strategy Phase I-Prerequisites 1) Prepare for dramatic change and determined how to do the work 2) Recruit a qualified, knowledgeable enterprise data executive (and other qualified talent) 3) Eliminate the Seven Deadly Data Sins Phase II-Iterations (Lather, Rinse, Repeat) You are here 1) Identify the primary constraint keeping data from fully supporting strategy 2) Exploit organizational efforts to remove this constraint 3) Subordinate everything else to this exploitation decision 4) Elevate the data constraint 5) Repeat the above steps to address the new constraint
  36. 36. © Copyright 2021 by Peter Aiken Slide # 71https://plusanythingawesome.com https://en.wikipedia.org/wiki/Theory_of_constraints (TOC) • A management paradigm that views any manageable system as being limited in achieving more of its goals by a small number of constraints(Eliyahu M. Goldratt) • 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 Theory of Constraints - Generic © Copyright 2021 by Peter Aiken Slide # 72https://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 Alleviate
  37. 37. Theory of Constraints at work improving your data © Copyright 2021 by Peter Aiken Slide # 73https://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 Alleviate Focus evolve from reactive to proactive Strategy helps your data program © Copyright 2021 by Peter Aiken Slide # 74https://plusanythingawesome.com Over time increase capacity and improve Strategy Cycle
  38. 38. Data Strategy Framework (Part 2) © Copyright 2021 by Peter Aiken Slide # 75https://plusanythingawesome.com • Benefits & Success Criteria • Capability Targets • Solution Architecture • Organizational Development Solution • Leadership & Planning • Project Dev. & Execution • Cultural Readiness Road Map • Organization Mission • Strategy & Objectives • Organizational Structures • Performance Measures Business Needs • Organizational / Readiness • Business Processes • Data Management Practices • Data Assets • Technology Assets Current State • Business Value Targets • Capability Targets • Tactics • Data Strategy Vision Strategic Data Imperatives Business Needs Existing Capabilities ExecutionBusiness Value New Capabilities Strategic Context • Strategy – Inherently a repetitive process that can be easily improved • Dependency – Data strategy exists to support organizational strategy • Evolution – At early maturity phases, the process is more important than the product! • Output – Plans are of limited value anyway and always discount obstacles • Technology – People and process challenges are 95% of the problem • Nirvana – How do I get to Carnegie Hall? – Practice Practice Practice © Copyright 2021 by Peter Aiken Slide # 76https://plusanythingawesome.com
  39. 39. Event Pricing © Copyright 2021 by Peter Aiken Slide # 77https://plusanythingawesome.com • 20% off directly from the publisher on select titles • My Book Store @ http://plusanythingawwsome.com • Enter the code "anythingawesome" at the Technics bookstore checkout where it says to "Apply Coupon" paiken@plusanythingawesome.com +1.804.382.5957 Questions? Thank You! © Copyright 2021 by Peter Aiken Slide # 78Book a call with Peter to discuss anything - https://plusanythingawesome.com/OfficeHours.html + =

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