news
2021
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.
2014
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
2013
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)
2012
forqlift 0.9.0: support for external data types (2012/12/15)
novi 2.1.2 - fixes for RHEL6 and CentOS6 (2012/07/31)
2011
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)
2010
novi 2.1.1 - Fedora 13 fixes (2010/09/27)
novi v2.1.0 release (2010/01/12)
2009
systems architecture update (2009/06/19)
2008
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)
2007
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)
2006
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)
2005
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)