Last month, Koray Köse wrote a great must-read post on why data scientists and orchestration officers are part of today’s silicon snake oil sales people and not qualified to solve your supply chain problems (my less-than-eloquent rephrasing of his words).

He was totally right in his rant, but I’m going to go one step further. There is no such thing as a “Data Scientist”. You can’t do “Science” on “Data” you don’t understand. You just can’t.

I don’t care how many mathematical models, statistical techniques, or “AI” toolsets you think you know (see my previous rants there), that doesn’t make you a “data” scientist — that makes you a mathematician, statistician, or new age script kiddie. (No better than the cut-and-paste script kiddies that hit the scene on mass before the dot-com crash, if you are old enough to remember it!)

I say this as someone who would best qualify if there was such a thing — PhD in CS with a thesis in multi-dimensional data structures and computational geometry, industry expertise in (Strategic Sourcing Decision) Optimization modelling (in high dimensions), spend analytics, etc. etc. etc. I was doing “big data” (more BS — we’ve always had more data than we could fit in memory on the machine resources we had available) before that was a term too.

Koray is also dead on with respect to PhDs. Even 26 years ago, you did a PhD to prove you could (or to stay in academia), not because it added anything of practicality beyond what you’d learn in a Masters.

However, I have to fact check him on the 50% to 70% supply chain tech project failure, since the latest Bain study puts the tech project failure rate in general at 88% and most of the partial to full failure is with the big players and Big X implementers (which is the majority of supply chain projects). The rate is higher (unless he’s talking full failure).

I am also going to remind him that this problem resonates through Procurement as well, so please don’t found just another ProcureTech company either. There are well over 700 now (see the mega map, and probably closer to 800 now) and we don’t need 100 “solutions” for the same problem that are almost the same. Get across-the-board experience, or at least spend years working with experts that have it, when developing your solutions if you are coming from a pure tech background.