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How Data and Analytics Have Changed Our Thinking About Organizations

How Data and Analytics Have Changed Our Thinking About Organizations. UMUC Analytics Summit. A Quiet Revolution. The advent of data and data systems to drive change and achieve goals Why has this happened? Entry point – the problem of accurate information

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How Data and Analytics Have Changed Our Thinking About Organizations

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  1. How Data and Analytics Have Changed Our Thinking About Organizations UMUC Analytics Summit

  2. A Quiet Revolution • The advent of data and data systems to drive change and achieve goals • Why has this happened? • Entry point – the problem of accurate information • F. Hayek’s “Fallacy of Complete Information” • Information Distortion

  3. Information DistortionThe inability to ascertain “ground truth”

  4. What is this Revolution? • Not about absolute availability of information • Tidal wave in total amount of data • What began as incremental change has reached a point of state change • Change in how we think about these things not simply how we do things • An Epistemological Revolution

  5. Characteristics of Analytics Systems • Granularity • Utility • Comprehensiveness • Timeliness • Interconnectedness • Accuracy (Quality of data)

  6. Relative sophistication for advanced data use • Technology • Data • People • Processes Highest “Culture” Lowest

  7. Relative sophistication for advanced data use • Technology • Data • People • Processes Highest “Culture” Lowest

  8. Practice and Practicality • Technology integration into daily activities • Technology far out-strips our systems for use • Why change what works? • Answer: This is how you do this.

  9. Information revolution: 1519 Change is being driven by what people can do now that they could not do before People, Systems and the Problems they address will dramatically lag technology’s capabilities

  10. Problem 1 • How do we reduce the lag between technological capabilities and ability of people and processes to incorporate those capabilities? • Lurking problem 1.5 • How do we help people begin to ask the new questions? • Goals • Integrated human and information systems • Creating self-evident answers to “how to do this?”

  11. The Move from Deduction and Induction Deduction Induction Grey areas must be deduced from existing Pattern Black areas must be induced from within all data

  12. The Role of Analytics • “Analytics” (or something looking much like it) • Discerning signal from noise • Aid to thinking about problems • Not about answering pre-planned questions

  13. Problem 2 • How do we think rigorously and inductively to maximize value added by new aids to thinking? • Look to those who have already thought about the new thinking

  14. Models How have we learned to integrate vast new data into our thinking • Evidence-Based Medicine • Supply Chain Management

  15. Evidence-based Medicine “The EBM Triad” External Evidence EBM Clinical Expertise Patient Expectations Source: Sackett et al. 1996

  16. Supply Chain Management Source: Koutsoukis et al. 2000

  17. A CENTRAL REPOSITORY FOR STUDENT AND WORKFORCE DATA MLDS Governing Board oversees compliance, coordination and research agenda MLDS Governing Board Reporting and Research Data Repository de-identifies data , warehouses data , provides for cross-agency reporting and oversees daily use “Data Warehouse” MSDE K-12 MHEC Post-Secondary DLLR Workforce Agencies conduct current and planned data collections and upload data set to Data Warehouse LEAS and Post-secondary Institutions provide linking identification information through transcripts LEAs Community Colleges Universities Work-places

  18. Problem 2 • How do we think rigorously and inductively to maximize value added by new aids to thinking? • Understanding what is and is not being captured in even vast data webs • Focused use, aids to thinking of certain problems or classes of problems

  19. Problem 3 • How do we use these tools and systems to make good decisions? • Information symmetry and cooperative games • The problem of power asymmetry • Competitive Games • Micromanagers

  20. Traditional Data Integration into Decision Making Outside information Operational Decisions Organization Policies, Plans, Programs, Regulation, Legislation Executive Leadership Leadership Frame Theory / Analysis Frame “Experts” Data environment

  21. Emerging Data Integration into Decision Making “Outside” Information Executive Leadership Operational Decisions Organization Policies, Plans, Programs, Regulation, Legislation Technology-Assisted Analysis Frame Leadership Frame “Experts” Data environment

  22. Problem 3 • How do we use these tools and systems to make good decisions? • Understanding when not to act • Common goals shared across the organizations • Opportunities to consider institutional change

  23. Our Problems • How do we reduce the lag between technological capabilities and ability of people and processes to incorporate those capabilities? • How do we think rigorously and inductively to maximize value added by new aids to thinking? • How do we use these tools and systems to make good decisions?

  24. Practical Takeaways • Interoperations teams • Data training for non-technical staff • Integrating their functional activities with analysis • Data is not opinion but it is imperfect and mediated • Understanding limitations and power of Information systems • Focus on your problem(s) • Train leaders to lead in the new environment • Create transparent and consensus-oriented outcomes structures

  25. Q&A Ben Passmore University System of Maryland passmore@usmd.edu

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