The AI-Driven Data Team
eBook - ePub

The AI-Driven Data Team

Improve Your Analytics with AI

  1. 267 pages
  2. English
  3. ePUB (mobile friendly)
  4. Available on iOS & Android
eBook - ePub

The AI-Driven Data Team

Improve Your Analytics with AI

About this book

Rapidly transform your analytics teams to deliver AI-driven insights.

The AI-Driven Data Team
by Nicholas Kelly is a proven-to-work playbook for data and analytics leaders who want to align analytics capabilities with the demands of an AI-powered business environment. Written for leaders accountable for data, analytics and business intelligence, this book provides tools for diagnosing capability gaps among data analytics teams, modernizing legacy stacks and delivering AI-driven insights to help organizations make better decisions.

You'll learn how to:
- Diagnose skills gaps and map AI-augmented career paths
- Integrate modern AI tools with existing analytics stacks
- Launch six revenue-driving and cost-focused pilots in 90 days
- Embed governance without slowing innovation
- Apply an ROI framework, governance checklist and 90-Day Charter

Drawing on expert insights and real-world applications, this book helps you upskill your analysts, strengthen AI governance and provide AI-driven insights that drive real results.

Themes include: AI strategy, data governance, analytics leadership, ROI from AI, organizational transformation, executive decision-making

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Information

Publisher
Kogan Page
Year
2026
Print ISBN
9781398627574
Edition
1
eBook ISBN
9781398627581

Table of contents

  1. About the author
  2. Acknowledgments
  3. PART ONE Setting the Groundwork (Days -14 to 0)
  4. 1 Building the AI-Driven Data Team
  5. 2 AI as a Force-Multiplier
  6. 3 Mindset Reset
  7. 4 Skills and Tools Matrix
  8. 5 Building Your AI-Ready Team
  9. 6 Upskill vs. Hire vs. Outsource
  10. 7 Readiness Audit
  11. 8 The 90-Day Charter
  12. PART TWO Sprint 1: Foundations and Upskills (Days 1 to 30)
  13. 9 Kill the Bottlenecks
  14. 10 Coding Copilots
  15. 11 Analytics Engineering Reimagined
  16. 12 Redesigned Workflows
  17. PART THREE Sprint 2: Six Quick-Win Pilots (Days 31 to 60)
  18. 13 Selecting Lighthouse Use Cases
  19. 14 Minimum-Viable AI Stack
  20. 15 Build, Buy, or Extend
  21. 16 Pilot Execution Playbook
  22. PART FOUR Sprint 3: Scale and Govern (Days 61 to 90)
  23. 17 Lightweight Machine Learning
  24. 18 Plain-English Governance
  25. 19 Bias, Privacy, and Security
  26. 20 Storytelling That Lands: Explaining Models Everyone Can Trust
  27. PART FIVE The AI Flywheel (Day 90+)
  28. 21 Measuring Impact: From Hours Saved to Decision Velocity
  29. 22 Learning Loops and Communities
  30. 23 The Human in the Loop
  31. Index

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