Wednesday, July 31, 2019

Biggest mistakes data scientists make

The biggest mistake that data scientists make is the classic case of correlation vs. causation. Just because two trends are linked, it doesn't mean that one causes another. An example of this that I'm aware of is a tech company that developed an application to sort resumes. The application recognized from the data that most engineers that ended up being hired were male. Of course, this is a correlation, because most applicants are male. But the software "taught" itself that it was causation—i.e. being male caused them to be good candidates. So the program started automatically rejecting female applicants. When the company found out, they had to shut down the program.

© 2019 Praveen Puri


Praveen Puri is the Strategic Simplicity® expert who has delivered over $400 million in value. He helps clients "weaponize" simplicity and bridge the gap between strategy and execution. Visit PuriConsulting.com

Hot Crypto-startup in IoT to Watch

I think an interesting player to keep an eye on is Augmate.    They were an original Google Glass partner, and they develop wearable IoT control devices (such as glasses), as well as the platform that other companies can use to develop their own applications.

They use their own coin and block chain technology to allow their IoT wearables to work with different supply partners, without an intermediary.

© 2019 Praveen Puri

Praveen Puri is the Strategic Simplicity® expert who has delivered over $400 million in value. He helps clients "weaponize" simplicity and bridge the gap between strategy and execution. Visit PuriConsulting.com

Tuesday, July 16, 2019

My guest column on IT Strategy Best Practices

My guest column on IT Strategy Best Practices was published on the blog of Alan Weiss, one of the top management consultants in the world!

https://alanweiss.com/guest-column-it-strategy-best-practices/

Friday, July 12, 2019

IT Strategy / Business Alignment

IT Strategy / Business Alignment: This is why most IT projects either fail, go over time/budget, or under-deliver. 1. At the beginning, there is not enough "Why" and "What" discussions before moving to the "How". 2. As the project advances, there's not enough iterative feedback and pushback from both the technology and the business.   Both sides end up focusing on what they want, but not on what they need. This distance between want and need is what consulting expert Alan Weiss calls "the value distance".