Building Analytics Teams

Book description

Master the skills necessary to hire and manage a team of highly skilled individuals to design, build, and implement applications and systems based on advanced analytics and AI

Key Features

  • Learn to create an operationally effective advanced analytics team in a corporate environment
  • Select and undertake projects that have a high probability of success and deliver the improved top and bottom-line results
  • Understand how to create relationships with executives, senior managers, peers, and subject matter experts that lead to team collaboration, increased funding, and long-term success for you and your team

Book Description

In Building Analytics Teams, John K. Thompson, with his 30+ years of experience and expertise, illustrates the fundamental concepts of building and managing a high-performance analytics team, including what to do, who to hire, projects to undertake, and what to avoid in the journey of building an analytically sound team. The core processes in creating an effective analytics team and the importance of the business decision-making life cycle are explored to help achieve initial and sustainable success.

The book demonstrates the various traits of a successful and high-performing analytics team and then delineates the path to achieve this with insights on the mindset, advanced analytics models, and predictions based on data analytics. It also emphasizes the significance of the macro and micro processes required to evolve in response to rapidly changing business needs.

The book dives into the methods and practices of managing, developing, and leading an analytics team. Once you've brought the team up to speed, the book explains how to govern executive expectations and select winning projects.

By the end of this book, you will have acquired the knowledge to create an effective business analytics team and develop a production environment that delivers ongoing operational improvements for your organization.

What you will learn

  • Avoid organizational and technological pitfalls of moving from a defined project to a production environment
  • Enable team members to focus on higher-value work and tasks
  • Build Advanced Analytics and Artificial Intelligence (AA&AI) functions in an organization
  • Outsource certain projects to competent and capable third parties
  • Support the operational areas that intend to invest in business intelligence, descriptive statistics, and small-scale predictive analytics
  • Analyze the operational area, the processes, the data, and the organizational resistance

Who this book is for

This book is for senior executives, senior and junior managers, and those who are working as part of a team that is accountable for designing, building, delivering and ensuring business success through advanced analytics and artificial intelligence systems and applications. At least 5 to 10 years of experience in driving your organization to a higher level of efficiency will be helpful.

Table of contents

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

Product information

  • Title: Building Analytics Teams
  • Author(s): John K. Thompson, Douglas B. Laney
  • Release date: June 2020
  • Publisher(s): Packt Publishing
  • ISBN: 9781800203167