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2021

Undervalued Practices in ML/AI, Part 4: Project Execution (2021/04/12) - There's a lot more to this than just building models.

Undervalued Practices in ML/AI, Part 3: Planning Projects (2021/04/05) - A little planning will go a long way.

Undervalued Practices in ML/AI, Part 2: Hiring and Team Structure (2021/03/29) - Go off the beaten path to make the most of your data-related hiring.

Undervalued Practices in ML/AI, Part 1: Getting Started (2021/03/22) - Is your company getting started with ML/AI? These uncommon tips will save you time and trouble.

Undervalued Practices in ML/AI: Series Introduction (2021/03/15) - Following the herd can be costly. Improve your ML/AI shop by following these undervalued practices.

The Lifecycle of an ML/AI Model (2021/03/08) - It's not just train-test-deploy

Technology Alone Will Not Save You (2021/03/01) - What nighttime warfighting can teach us about using AI in companies.

The Importance of Simulating Data (2021/02/22) - Why it's important to be able to simulate your own data.

Are You Using ML/AI for Automation? or for Innovation? (2021/02/15) - Determine whether an ML/AI project is for automation or for innovation, so you can prioritize it accordingly.

Question Marks and Periods in the World of Data (2021/02/08) - Punctuation matters when working with data: BI is periods. AI is question marks.

2020

Competitive Advantage in ML/AI (2020/12/21) - Understand which aspects of your ML/AI shop can (and cannot) give you an edge over the competition.

Treating Your ML/AI Projects Like A Stock Portfolio (2020/11/10) - If your company has several ML/AI efforts on the roadmap, it can be difficult to decide how to prioritize them. You can look to the stock market for guidance.

New Radar article: "Our Favorite Questions" (2020/10/22) - I've published an article on O'Reilly Radar, on what makes a good question and what are my favorite questions to ask.

Misuse of Models: Recent Facial Recognition Failures (2020/09/22) - It's bad enough when the model is wrong; even worse, when it's wrong and you didn't have to build it in the first place.

Setting Expectations for ML/AI Projects (2020/09/17) - Explaining the realities of how an ML/AI project may go awry.

TCM: Total Cost of (ML/AI) Model (2020/09/08) - Shedding light on the hidden costs of employing ML/AI models, which can upend the price/value ratio.

New Radar article: "An Agent of Change" (2020/08/25) - I've published a new article on O'Reilly Radar, on how the Covid-19 pandemic influences how we think, spend, and manage our businesses.

Misuse of Models: IB predicting test scores (2020/07/27) - When a group predicts students' performance in lieu of holding a exam, it leads to some lessons in the deployment and use of ML/AI models.

Data Lessons from the World of Algorithmic Trading (part 9): "Analyze Your Performance" (2020/07/14) - What can the world of algorithmic (electronic) trading teach us about good ML/AI practices? (Part 9 in a series)

Data Lessons from the World of Algorithmic Trading (part 8): "Develop Controls Around Data" (2020/07/06) - What can the world of algorithmic (electronic) trading teach us about good ML/AI practices? (Part 8 in a series)

Data Lessons from the World of Algorithmic Trading (part 7): "Monitor Your Risk" (2020/06/15) - What can the world of algorithmic (electronic) trading teach us about good ML/AI practices? (Part 7 in a series)

Providing Padding Around ML/AI Models (2020/06/03) - ML/AI models still require a lot of human involvement.

Data Lessons from the World of Algorithmic Trading (part 6): "Monitor Your Models" (2020/05/28) - What can the world of algorithmic (electronic) trading teach us about good ML/AI practices? (Part 6 in a series)

Data Lessons from the World of Algorithmic Trading (part 5): "Monitor Your Data Feeds" (2020/05/25) - What can the world of algorithmic (electronic) trading teach us about good ML/AI practices? (Part 5 in a series)

Data Lessons from Algorithmic Trading (part 4): "Develop a Solid Data Infrastructure" (2020/05/22) - What can the world of algorithmic (electronic) trading teach us about good ML/AI practices? (Part 4 in a series)

Data Lessons from Algorithmic Trading (part 3): "Think In Terms of Experiments" (2020/05/20) - What can the world of algorithmic (electronic) trading teach us about good ML/AI practices? (Part 3 in a series)

Data Lessons from Algorithmic Trading (part 2): "Know Your Objective" (2020/05/18) - What can the world of algorithmic (electronic) trading teach us about good ML/AI practices? (Part 2 in a series.)

Data Lessons from Algorithmic Trading (part 1): Introduction (2020/05/07) - What can the world of algorithmic (electronic) trading teach us about good ML/AI practices? (Part 1 in a series)

A Risk Assessment for Your ML/AI Job Security (2020/04/28) - Three questions to help you gauge your risk of being laid off.

Identifying and Handling Risks in AI Businesses (2020/04/21) - Business models involving ML/AI are sitting on unrecognized risk.

The ML/AI Reality Check (2020/04/14) - COVID-19 may be what gets companies to take their ML/AI efforts seriously.

Why don't we talk more about risk in AI? (2020/04/07) - The AI world is sitting on all kinds of risk ... but no one wants to talk about it.

Academic Qualifications for Data Science Professionals (2020/03/10) - Should your company's data scientists hold a PhD? Probably not.

What should my company do with its data? (2020/02/27) - Executives want to know how to employ ML/AI in their company. They need more than just quick tips.

2019

How Do I Get More Data? (2019/12/23) - Since "more data is better," what do I do if I don't have enough?

How Much Data Is Enough? (2019/12/16) - How much data do you need to build good predictive models?

How to Prepare for That Data Scientist Job Interview (2019/12/11) - Looking for a data science job? It involves far more than the technical know-how.

Data Ethics for Leaders: A Risk Approach (Part 5) (2019/06/10) - Looking at data ethics through the lens of risk. (Part 5 in a series.)

Data Ethics for Leaders: A Risk Approach (Part 4) (2019/06/03) - Looking at data ethics through the lens of risk. (Part 4 in a series.)

Data Ethics for Leaders: A Risk Approach (Part 3) (2019/05/27) - Looking at data ethics through the lens of risk. (Part 3 in a series.)

Data Ethics for Leaders: A Risk Approach (Part 2) (2019/05/20) - Looking at data ethics through the lens of risk. (Part 2 in a series.)

Data Ethics for Leaders: A Risk Approach (Part 1) (2019/05/13) - Looking at data ethics through the lens of risk. (Part 1 in a series.)

2018

Business Intelligence: A First Step to Data Science (2018/02/19) - How can business intelligence (BI) launch your data efforts, and pave the way for your first data science hire?

Common Mistakes in Data Science Hiring : Part 2 (2018/01/30) - Having trouble hiring data scientists? or, once you hire them, do they not stick around? You may be tripping over your own feet. Part 2 of 2.

Common Mistakes in Data Science Hiring : Part 1 (2018/01/23) - Having trouble hiring data scientists? or, once you hire them, do they not stick around? You may be tripping over your own feet. Part 1 of 2.

2016

Data Science Hiring as a Sales Process (2016/11/28) - Having trouble hiring data scientists? Borrow some ideas from your sales team.

The Importance of Data Infrastructure (2016/11/21) - A successful data science shop requires more than just data scientists.

What is a data strategy, and why do I need one? (2016/02/17) - The what, why, and how of a data strategy -- a road map for your company's data efforts

2015

Hiring on Your Analytics Team (2015/10/14) - Stack the deck in your favor when hiring people into your data team.

"On Leadership" -- New O'Reilly Radar Post (2015/09/28) - Moving from a technical to a leadership role

Roles on Your Analytics Team (2015/02/10) - Data science is all the rage, but some companies focus on hiring just data scientists. Be careful.

How Do You Know If Your Company Needs Hadoop? (2015/01/02) - Let's walk through the decision of whether your company would benefit from building a Hadoop cluster.