Weekly recap: 2023/03/26 (2023/03/26) - random thoughts and articles from the past week

When generated images take on a life of their own (2023/03/23) - A reminder of generative AI's chaotic potential

Measuring the wrong thing (2023/03/21) - Using automation to double down on an ineffective metric.

Weekly recap: 2023/03/19 (2023/03/19) - random thoughts and articles from the past week

Same name, new face for AI (2023/03/15) - The artist currently and formerly known as "AI" gets another turn on the stage.

ML for Executives, Part 2: What Your Company's Data Scientists Do (2023/03/13) - A stakeholder's view of how data scientists build and deploy ML models.

Weekly recap: 2023/03/12 (2023/03/12) - random thoughts and articles from the past week

Weekly recap: 2023/03/05 (2023/03/05) - random thoughts and articles from the past week

Weekly recap: 2023/02/26 (2023/02/26) - random thoughts and articles from the past week

Congratulations, you are now a data company (2023/02/24) - What custom software can tell us about bringing AI into a company.

When your metrics are fooling you (2023/02/22) - Operating on bad metrics is worse than having no metrics at all.

Weekly recap: 2023/02/19 (2023/02/19) - random thoughts and articles from the past week

Is your company on autopilot? (2023/02/17) - Asking "why?" more often.

When good metrics are bad (2023/02/16) - Some metrics are good, some are bad. But even the good ones can be bad if you're not careful.

Some thoughts on generative AI (2023/02/13) - My take on tools such as Dall-E, Stable Diffusion, and ChatGPT

Weekly recap: 2023/02/12 (2023/02/12) - random thoughts and articles from the past week

Revisiting the idea of noncompete clauses (2023/01/09) - A quick on the FTC's recent proposal, and a link to something I wrote almost a decade ago

YouTube's in-site commerce play (2022/11/14) - The video IS the advert

ML for Executives, Part 1: High-Dimensional Pattern Matching (2022/10/24) - Using spreadsheets to explain core ML/AI concepts to executives.

No Vanity Metrics (2022/10/17) - Measure what matters

New Radar article: Ad Networks and Content Marketing (2022/08/16) - Disney+ has announced an ad-supported tier. Here's my idea on how they might run it.

Block & Mortar (web3 newsletter) update (2022/07/25) - An update on my web3 newsletter

New Radar article on marketplaces: 'Flex Your Brain, Not Your Market Muscle' (2022/05/30) - The second article in a short series on N-sided marketplaces

New project: the Block & Mortar web3 newsletter (2022/05/23) - I've launched a newsletter covering web3: cryptocurrencies, blockchain, and metaverse

New Radar series on N-sided marketplaces (2022/04/25) - N-sided marketplaces are very common in the business world. What are they, and how do they work?

Not All Datasets Are Created Equal (2022/04/11) - Some datasets are problems in waiting.

The Top Sources of Risk Facing the AI Sector (2022/04/04) - Potential problems that can affect the entire field.

Risk Bingo Cards (2022/03/21) - Adopt a popular expression to improve your risk awareness and assessment.

No Silver Bullets (2022/03/14) - Sometimes, you have to tackle a challenge head-on.

360-Degree ML/AI (2022/03/07) - Keeping the bigger picture in mind.

The Opposite of 'On Hiatus' (2022/02/07) - You'll see fewer posts here for a while, as I pursue some new projects

New DSS Podcast episode: Communal Computing and AI (2022/01/17) - The second part of my interview with product manager Chris Butler, this time on communal computing and AI.

New DSS Podcast episode: Spatial Data and R&D Projects (2021/12/20) - My panel discussion with Linda Liu (Hyrecar) and Giacomo Vianello (Cape Analytics)

My take on the Zillow Offers shutdown (2021/12/06) - Zillow recently closed down its home-buying program. Do I see this as a failure of ML/AI? In a word: No.

New Radar Article: "Remote Teams in ML/AI" (2021/11/15) - I've published an article on O'Reilly Radar: The key ingredient to a successful remote team? Leadership buy-in.

New DSS Podcast episode: Uncertainty in AI Product Management (2021/11/08) - I interviewed product manager Chris Butler about the role uncertainty plays in AI product management.

Reducing Risk in Building ML/AI Models (2021/11/01) - Best practices to balance the risk and reward of building predictive models.

Human/AI Interaction: Exoskeletons, Sidekicks, and Blinking Lights (2021/10/18) - Spotting opportunities to build AI systems that complement, not outright replace, people on the job.

Periods, Question Marks, and now Ellipses: The Punctuation Marks of Data Analysis. (2021/10/04) - BI is periods. AI is question marks. Simulation is ellipses.

Business Stakeholders: Three Questions to Improve Your Communications With Data Scientists (2021/09/27) - When talking with your company's data scientists, does the conversation quickly bog down? Try these questions to keep things moving.

When "Constants" ... Aren't (2021/09/20) - Want to improve your risk assessment? Identify, then question, the constants in your world.

Data Scientists: Four Questions to Improve Your Stakeholder Communications (2021/09/13) - Nervous about meeting with your company's execs, legal counsel, or product team? Just answer these four questions.

New Radar article: "Rebranding Data" (2021/08/30) - I've published an article on O'Reilly Radar: how many times will we rename the data field?

Preparing your Chief Data Officer (CDO) for Success (2021/08/23) - Three steps can reduce churn in your company's data leadership role(s).

AI-based Automation: Ways to Mix Human and Machine (2021/08/09) - Humans versus machines? To reduce your risk, the best answer is "yes."

Our Maturing Expectations of AI (2021/08/02) - As we learn more about AI, what will change about how we develop and deploy it?

Handling Risk and the Three Ms (2021/07/26) - Learning how to handle risk, from the fields that do it well.

Where Is Your Risk Department? (2021/07/19) - Every company needs someone to be their extra eyes.

What is Risk? (2021/07/12) - An introduction to risk.

Towards Quantification: Finding Hard and Soft Numbers In Your Business (2021/06/28) - In search of ML/AI success? Know your hard and your soft numbers.

Are We In An AI Bubble? (2021/06/14) - Does the AI hype meet the technical term of a "bubble?"

Lessons Learned from an AI Submarine (2021/06/07) - Putting AI to good use in a dangerous environment.

The Lemonade Lesson (2021/05/31) - Lemonade's recent media spotlight is a cautionary tale for any company using ML/AI.

Undervalued Practices in ML/AI: Conclusion (2021/04/19) - Discipline pays off.

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.

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.

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.)

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.

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

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.

Planting a Seed: Setting a New Direction for Tech Noncompetes (2014/10/07) - Tech noncompetes can raise thorny issues. We offer some ideas to smooth things out.

Viva Spreadsheets (2014/06/09) -

Good Use of Your Customer Data (2014/04/07) - use your customer data to really engage your customers

ORD Camp 2014: A Fun Mind-Bender (2014/01/29) - a look back at ORD Camp 2014

The Power of "I Don't Know" (2013/11/20) -

Every Project Needs a Producer (2013/10/15) - Similarities between music producers and consultants

Are you a fan? (2013/10/07)

new paper: "Business Models for the Data Economy" (2013/10/03) - Business Models for the Data Economy

new service: R training (2013/09/30) - announcing a new service: R training

Strata 2013: "Building Your Analytics Shop, Step By Step" (2013/07/01)

Introducing "NoSoftware" (2013/06/11)

announcing: ControlledBurn (2013/05/29)

two new posts on Radar: chatting with Mike Loukides (2013/05/07)

Unveiling a new project: Making Analytics Work (2013/04/25)

announcing charcuterie (2013/01/20)

looking forward, 2013 (2013/01/08)

forqlift 0.9.0: support for external data types (2012/12/15)

novi 2.1.2 - fixes for RHEL6 and CentOS6 (2012/07/31)

not so quiet ... (2011/12/26)

new book on the way: Parallel R (2011/07/05)

news next week (2011/06/30)

forqlift 0.8.0 (alpha!): direct HDFS interaction (2011/05/31)

new forqlift on the way (2011/05/27)

novi on RHEL5 and RHEL6 (2011/05/26)

forqlift 0.7.1: subtle UI improvements (2011/03/30)

forqlift 0.7.0: faster archive translations (2011/01/30)

forqlift 0.6 has arrived! (2011/01/25)

forqlift 0.6 landing soon (2011/01/22)

a couple of new R packages: factualR and Segue (2011/01/15)

New year, new toy: forqlift (2011/01/01)

novi 2.1.1 - Fedora 13 fixes (2010/09/27)

novi v2.1.0 release (2010/01/12)

systems architecture update (2009/06/19)

Fixed! (RPM v4.6, Fedora 10) (2008/12/29)

Fedora 10 build errors (2008/12/05)

systems infrastructure update: more on naming conventions (2008/10/04)

systems infrastructure update: "defining an architecture" (2008/09/16)

systems infrastructure update: "the what and the why" (2008/08/24)

New section: systems infrastructure (2008/08/19)

novi 1.1.9 release -- fixes build problems under GCC 4.3 and Fedora 9 (2008/06/29)

novi 1.1.8 release (2008/02/08)

novi release 1.1.7 -- Expat version fix (2007/09/03)

Fedora 7 upgrade error (2007/07/24)

Companion book site (2007/06/19)

Hot off the presses! (2007/03/20)

News, Updates, Surprises (2006/11/25)

New article on APR, new page (2006/11/05)

novi 1.1.6 release: fix build problems (2006/05/21)

novi 1.1.5 release: include source RPMs (2006/05/17)

novi 1.1.4 release: RPM epochs (2006/05/09)

RPM epoch problems (2006/05/07)

novi for Fedora Core 5 (2006/05/04)

novi 1.1.2 release: small build fixes (2005/10/02)

novi 1.1.0 release: parse repodata files (2005/07/18)

announcing novi 1.0.1 (2005/06/22)

Build problems under GCC 4 (2005/06/19)

Bouncing e-mails (2005/06/12)

Almost There... (2005/05/24)

Coming Soon... (2005/05/01)