Ozminkowski RJ. Creating Technology-Based Offerings. On https://cxotechmagazine.com/creating-technology-based-offerings/
Ozminkowski RJ. Scaling Technology-based Offerings. On https://cxotechmagazine.com/scaling-technology-based-offerings/
Ozminkowski RJ. Creating Value for the Market With Technology-Based Offerings. On https://cxotechmagazine.com/creating-value-for-the-market-with-technology-based-offerings/
Ozminkowski RJ. Prioritizing Data Science Models for Production: Statistical Performance Metrics Aren’t Enough to Pick the Right Models to Bring to Market. On https://www.kdnuggets.com/2022/04/prioritizing-data-science-models-production.html
Ozminkowski RJ. Using Data Science to Develop a Winning Business Strategy, Part 1. On https://opendatascience.com/using-data-science-to-develop-a-winning-business-strategy-part-1/
Ozminkowski RJ. Using Data Science to Develop a Winning Business Strategy, Part 2. On https://opendatascience.com/using-data-science-to-develop-a-winning-business-strategy-part-2/
Ozminkowski RJ. Using Data Science to Develop a Winning Business Strategy, Part 3. On https://opendatascience.com/using-data-science-to-develop-a-winning-business-strategy-part-3/
Ozminkowski RJ. Are we all Bayesians? Our Brains Think so. On https://towardsdatascience.com/are-we-all-bayesian-our-brains-think-so-555cedaffed9
Ozminkowski RJ. What do 101 Dalmations and Machine Learning have in Common? There are at least 101 Examples of Machine Learning Data Scientists can use to Create Valuable Insights. Here they are. On https://towardsdatascience.com/what-do-101-dalmations-and-machine-learning-have-in-common-9e389b899df3
Ozminkowski RJ. Garbage-in Garbage-out: Saving the World is Just one Reason to Address the Common Problem. On https://towardsdatascience.com/garbage-in-garbage-out-721b5b299bc1
Ozminkowski RJ. What Causes What, and How Would we Know? Facing a Barrage of Misinformation about COVID-19, Climate Change, Racism, and Other Controversies, Data Scientists can use Solid Causal Reasoning to help us Figure out What to Believe. On https://towardsdatascience.com/what-causes-what-and-how-would-we-know-b736a3d0eefb
Sources & References
For a great description of what it means to have a responsible analytics organization, see Hall P, Gill N, and Cox B, Responsible Machine Learning: Adaptable Strategies for Mitigating Risks and Driving Adoption, Boston, MA: O'Reilly Media, Inc., 2021
The Data Science Lifecycle image at the top of the Services page has roots in two other publications. It was inspired first by what is known as the Cross-Industry Standard Process for Data Mining (CRISP-DM). A summary of that can be found in Kelleher JD and Tierney B, Data Science, Cambridge, MA: MIT Press, pp. 58-67, 2018.
Second, that image incorporates some of the revisions to CRISP-DM that are discussed in Stirrup, Jen. What's Wrong with CRISP-DM, and is There an Alternative? See https://jenstirrup.com/2017/07/01/whats-wrong-with-crisp-dm-and-is-there-an-alternative/. My enhanced version on the Services tab adds the SDOH (Social Determinants of Health) and Equity perspective and also notes the person (not the data) as the center of the data science universe and the main focus of analytic work, as is appropriate in healthcare applications..
The image of the crew team on the Projects tab was downloaded from Unsplash.com, which provides free, uncopyrighted images.
The image about Blue Ocean Strategy is from a Powerpoint presentation I developed. The ideas expressed there are part of my summary of ideas about blue ocean strategy that were put forth in a great book published in Kim WC and Mauborgne R, called Blue Ocean Strategy, from Harvard Business Review Press, 2015.
Other images on the Projects tab were downloaded from the internet. No copyright infringement is intended, nor will any revenues be made from the use of these images. The images used are purely supplemental to my personal commentary, ideas and previous works. Viewers are encouraged to seek out the services of those who made those images, if desired, or something similar.