Детальная информация
| Название | Building analytics teams: harnessing analytics and artificial intelligence for business improvement |
|---|---|
| Авторы | Thompson John K. ((Business intelligence consultant),) |
| Коллекция | Электронные книги зарубежных издательств ; Общая коллекция |
| Тематика | Quantitative research. ; Business planning. ; EBSCO eBooks |
| Тип документа | Другой |
| Тип файла | |
| Язык | Английский |
| Права доступа | Доступ по паролю из сети Интернет (чтение, печать, копирование) |
| Ключ записи | on1201192103 |
| Дата создания записи | 21.10.2020 |
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- 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
