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Full Description
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
Contents
Section - ONE: Setting the groundwork (days -14 to 0);
Chapter - 01: Building the AI-driven data team;
Chapter - 02: AI as a force-multiplier;
Chapter - 03: Mindset reset;
Chapter - 04: Skills and tools matrix;
Chapter - 05: Building your AI-ready team;
Chapter - 06: Upskill vs. hire vs. outsource;
Chapter - 07: Readiness audit;
Chapter - 08: The 90-day charter;
Section - TWO: Sprint 1 - foundations and upskills (days 1 to 30);
Chapter - 09: Kill the bottlenecks;
Chapter - 10: Coding copilots;
Chapter - 11: Analytics engineering reimagined;
Chapter - 12: Redesigned workflows;
Section - THREE: Sprint 2 - six quick-win pilots (days 31 to 60);
Chapter - 13: Selecting lighthouse use cases;
Chapter - 14: Minimum-viable AI stack;
Chapter - 15: Build, buy, or extend;
Chapter - 16: Pilot execution playbook;
Section - FOUR: Sprint 3 - scale and govern (days 61 to 90);
Chapter - 17: Lightweight machine learning;
Chapter - 18: Plain-English governance;
Chapter - 19: Bias, privacy, and security;
Chapter - 20: Storytelling that lands - explaining models everyone can trust;
Section - FIVE: The AI flywheel (day 90+);
Chapter - 21: Measuring impact - from hours saved to decision velocity;
Chapter - 22: Learning loops and communities;
Chapter - 23: The human in the loop;
