Intelligent Data

The Data Roadmaps Blog

An infographic shows four stages from analytics translation to roadmap definition: Ambiguity Gap, The Catalyst, The Realization, and The Shared North Star, each with icons, descriptions, and a note on the importance of teams and technology.

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.

A speaker presents at the DATA+AI Leadership Forum, with a sign reading "There's no AI roadmap without a data roadmap!" and an audience listening to a presentation in a conference room. Event details: DATA Summit, May 6-7, Boston, MA.

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?

Infographic titled "The 7 Fundamental Patterns of AI" showing categories for AI: Conversational, Patterns & Anomalies, Recognition, Predictive Analytics, Personalization, Goal-Driven Systems, and Autonomous Systems.

The Seven Patterns of AI Projects (Infographic)

An Infographic showing the 7 patterns of AI projects.

A digital illustration showing a central human eye surrounded by data streams, animal images, facial recognition outlines, and brain scans, representing AI project types and data analysis through advanced technological graphics.

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. 

A humanoid robot with a shiny white and metallic body touches its temple with one finger, appearing to think, against a dark background with blue highlights and glowing accents.

Busting AI Myths

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

Several DigiKey packages move along a conveyor belt in a warehouse, sparking a silent conversation of efficiency and innovation within the shipping facility.

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.

Close-up view of a paved road with visible black tire skid marks, bordered by dirt and rocky terrain on one side—a scene where Michelin innovation meets the demands of challenging terrains.

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.

A group of professionals sit around a large conference table engaged in a meeting, with documents, notepads, and water bottles on the table.

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.

A group of ancient philosophers and monks interact with futuristic technology, circuit patterns, artificial intelligence, and a large yin-yang symbol in a vibrant, digital setting.

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.

A human hand and a robotic hand reach toward each other, fingertips nearly touching, with Intelligent Data and AI-themed graphics in the background.

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.

Person interacting with a laptop displaying digital analytics graphics, including charts, graphs, and data visualizations, in a blue-toned, futuristic setting—highlighting a strong data strategy and Data-For-Purpose approach.

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.

A group of unlit light bulbs hangs together on the left, while one illuminated bulb shines with purpose and leadership on the right against a dark background.

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.