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Thompson, John K. Building analytics teams: harnessing analytics and artificial intelligence for business improvement / John K. Thompson. — 1 online resource (1 volume) : illustrations. — Includes index. — <URL:http://elib.fa.ru/ebsco/2514610.pdf>.

Дата создания записи: 21.10.2020

Тематика: Quantitative research.; Business planning.

Коллекции: EBSCO

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Оглавление

  • Cover
  • Copyright
  • Packt Page
  • In Praise of
  • Foreword
  • Contributors
  • Prologue
  • Table of contents
  • Preface
  • Introduction
    • Becoming data and analytically driven
    • An analytical mindset
    • Building an analytics team and an environment for collaboration
    • Collaborators in the analytics journey
    • Selecting successful projects
    • Organizational dynamics
    • Competitive advantage or simply staying competitive
    • The core collaboration/innovation cycle
    • Focusing on self-renewing processes, not projects – an example
    • Summary
  • Chapter 1: An Overview of Successful and High-Performing Analytics Teams
    • Introduction
    • AI in the education system
    • We are different
    • The original sin
    • The right home
    • Ethics
    • Summary
    • Chapter 1 footnotes
  • Chapter 2: Building an Analytics Team
    • Organizational context and consideration
    • Internships and co-op programs
    • Diversity and inclusion
    • Neurodiversity
    • Disciplinary action
    • Labor market dynamics
    • A fit to be found
    • Evolved leadership is a requirement for success
    • Continual learning and data literacy at the organizational level
    • Defining a high-performing analytical team
    • The general data science process
    • Team architecture/structure options
    • The implications of proprietary versus open source tools
    • Summary
    • Chapter 2 footnotes
  • Chapter 3: Managing and Growing an Analytics Team
    • Managerial focus and balance
    • Sponsor and stakeholder management
    • An open or fixed mindset?
    • Productivity premium
    • The rhythm of work
    • Personal project portfolio
    • Managing team dynamics
    • The front end of the talent pipeline
    • It takes a team
    • Simply the best
    • Organizational maxims
    • Summary
    • Chapter 3 footnotes
  • Chapter 4: Leadership for Analytics Teams
    • Artificial intelligence and leadership
    • Traits of successful analytics leaders
    • Building a supportive and engaged team
    • Managing team cohesion
    • Being the smartest person in the room
    • Good (and bad) ideas can come from anywhere
    • Emerging leadership roles – Chief Data Officer and Chief Analytics Officer
    • Hiring the Chief Data Officer or Chief Analytics Officer – where to start?
    • Summary
    • Chapter 4 footnotes
  • Chapter 5: Managing Executive Expectations
    • You are not the only game in town
    • Know what to say
    • Know how to say it
    • Shaping and directing the narrative
    • Know before you go
    • How many of us are out there?
    • There is a proven path to success
    • What are you hoping to accomplish?
    • Outsourcing
    • Elephants and squirrels
    • Daily operations
    • Summary
    • Chapter 5 footnotes
  • Chapter 6: Ensuring Engagement with Business Professionals
    • Overcoming roadblocks to analytics adoption
    • Organizational culture
    • Data or algorithms – the knee of the curve or the inflection point
    • A managerial mindset
    • The skills gap
    • Linear and non-linear thinking
    • Do you really need a budget?
    • Not big data but lots of small data
    • Introductory projects
    • Value realization
    • Summary
    • Chapter 6 footnotes
  • Chapter 7: Selecting Winning Projects
    • Analytics self determination
    • Communicating the value of analytics
    • Relative value of analytics
    • The value of analytics, made easy
    • Enabling understanding
    • Enterprise-class project selection process
    • Understanding and communicating the value of projects
    • Delegation of decision making
    • Technical or organizational factors
    • Guidance to end users
    • Where is the value in a project?
    • Operational considerations
    • Selling a project – vision, value, or both?
    • Don't make all the decisions
    • Do the subject matter experts know what "good" looks like?
    • The project mix – small and large
    • Opportunity and responsibility
    • Summary
  • Chapter 8: Operationalizing Analytics – How to Move from Projects to Production
    • The change management process
    • Getting to know the business
    • Change management
    • Analytics and discovery
    • Analytical and production cycles and systems – initial projects
    • Summary
  • Chapter 9: Managing the New Analytical Ecosystem
    • Stakeholder engagement – your primary purpose
    • Bias – accounting for it and minimizing it
    • Ethics
    • Summary
  • Chapter 10: The Future of Analytics – What Will We See Next?
    • Data
    • AI today
    • Quantum computing and AI
    • Artificial General Intelligence
    • Today, we are failing
    • Teaching children to love numbers, patterns, and math
    • Blending rote memorization with critical thinking as a teaching paradigm
    • Summary
    • Chapter 10 footnotes
  • Other Books You May Enjoy
  • Index

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