Data & Machine Learning

How to be effective each week

“Are you proud of your work this week?” This is a useful litmus test. It shows

Hiring Data Science Too Early

Hire a Data Scientist too early… and you’ll both regret it. Hire these roles first: 1.

Can Data Science Work With Agile?

Data Science doesn’t fit neatly into agile/software processes, but many are still valuable. We use:– Daily

7 Reasons Users Struggle to Adopt AI/Analytics

User adoption for B2B AI/Analytics can be hard, because users don’t pick their products. Here are

I Perpetuated the “Deep Learning” Myth

11 weeks of iteration. And the data team was killing it. We’d just built the startup’s

Morgan Housel

I started reading Morgan Housel‘s newsletter in March 2019. He’s one of my favorite thinkers and writers.

My Mantra: Pilot before Production

Simple things cause AI features to fail. Here’s a real world example and how we solved

Choose AI Projects Carefully (2 Reasons Why)

Choose AI projects carefully. 2 important reasons why 👇 1. Problem Space Understanding Understanding a problem well

Building data products is a lot like golf

Building data products is a lot like golf. In golf… Anyone can swing a club. But

The Most Underrated Data Role

Data Analysts are the most underrated data role right now. They do these 10 amazing things

Prediction: Data Team Structures

My prediction: We’ll see fewer standalone Data Science teams. More data teams will become embedded in

Communicating AI Project Status

People generally think of projects sequentially (i.e. like gantt charts). However, data science is inherently circular

Start with Analytics, not AI

If you’re building an AI product. Don’t start with AI. Why? Users don’t automatically trust AI.

More Tableau, Less Whiteboarding

Want to save yourself time and disappointment? I was getting fooled by false positives, so I

The “First Time” Tax

There’s a “first time tax” for every new data set, problem area, and modeling technique. Great

“Underwater” Data Features

Data features can easily become “underwater” Pay attention to Feature Economics. Impact = Value Created –

Occam’s Razor

Occam’s Razor. The longer you build data products, the more you realize how true (and important)

Lying Averages & AI Accuracy

Averages lie…especially when evaluating AI accuracy. Is an 80% model accuracy good? Here’s how you know:

“Doing AI”

Gone are the days of simply ‘doing AI’. Leaders expect AI strategies leading to real value.

AI Product Management and Lessons for SMBs

Joshua Schultz and I talk about AI Product Management and lessons for SMBs on Racket.com: – How AI

Machine Learning Feature Economics

Machine Learning projects often die, because of poor feature economics. Why? ML is expensive to build,

How good is this AI prediction?

4 ways to know (2 obvious, 2 less obvious) 1. Accuracy -> Predicted vs. Actual Compare

Healthy Paranoia Can Save Your AI Project

Andy Grove famously claimed: “Only the Paranoid Survive” Healthy Paranoia can save your AI project. Here

Job #1 for Data Science Leaders

Job #1 for Data Science leaders right now is: framing and narrative. Executives don’t have a

5 Ways to “Design for AI Adoption”

AI Products require a “Design for Adoption” strategy. 5 ways we avoid adoption failure 👇 1.

We have a working ML model! 🎉

AI Product Team: “We have a working ML model! 🎉” Savvy execs know… There are 3

Credibility is lost when AI teams overbuild.

AI is an expensive, high-performance material — much like Carbon Fiber in the physical world. Carbon

The Question Execs Should Ask Their AI Teams

The best question executives can ask their AI teams right now: “Are users finding our AI

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