# Intelligent Data

#### The Data Roadmaps Blog

**How an Analytics Translation Effort Pivoted to Data Roadmap Definition**  
What began as a collection of pain points, frustrations, and wish lists evolved into a collaborative effort to define future-state objectives, prioritize use cases, identify gaps, and establish a roadmap for execution.

**Highlights from My Talk at Data Summit**  
Last week, I presented “There’s No AI Roadmap Without a Data Roadmap” at the Data Summit AI + Leadership Forum in Boston. The presentation focused on a challenge many organizations are now facing: How do we transition from AI experimentation mode to AI accountability?

**The Seven Patterns of AI Projects (Infographic)**  
An Infographic showing the 7 patterns of AI projects.

**The Seven Patterns of AI Projects**  
AI projects can be categorized into seven different patterns. Each pattern follows its own objectives, development iterations, considerations, risks, and complexities.

**Busting AI Myths**  
There are many misconceptions on what AI can or cannot do.

**A Conversation with DigiKey’s CDAO**  
Earlier this week, I had the privilege of speaking with Sridher Arumugham, Chief Data & Analytics Officer at DigiKey. Sridher was recently recognized as one of the Top 100 Chief Data Officers globally by HotTopics.

**Michelin’s Rubber Meets the Road of Innovation with Data and AI**  
“This is where the rubber meets the road” is a phrase we toss around often. At Michelin, it’s their literal strategy: embedding over 200 AI use cases directly into operations to modernize core business processes.

**The $99 Box and the Valuation Wake-Up**  
A dominant hardware business unlocks outsized valuation by shifting from selling devices to monetizing data, outcomes, and a learning platform that compounds value over time.

**Does Philosophy Eat or Pervade AI?**  
Philosophy doesn’t consume AI; it permeates it—shaping intent, ethics, and leadership clarity so technology serves purpose, strategy, and wiser decision-making rather than replacing them.

**Indispensability of Inspired People and Intelligent Data**  
Data alone doesn’t drive better decisions; human judgment and imagination do. AI informs direction, but leadership philosophy determines outcomes, innovation, and responsible strategic choices.

**Data-For-Purpose vs. Purpose-For-Data**  
In today’s data-rich environment, organizations often find themselves inundated with information, leading to a common pitfall: leveraging available data to find a purpose, rather than identifying a clear purpose and seeking the necessary data to support it.

**Pressure vs. Purpose: Leading Through the Noise**  
Leadership under pressure demands inner clarity and courage over mere data or instinct—enabling aligned, purposeful decisions instead of fear-driven compromises.