Thanks for posting the summary of the GartnerChat. This is an extremely important topic that I have spoken on but apparently haven't blogged on yet (surprisingly). I'll just comment on one item here for now.
"While the jury is still out on whether you need highly trained scientists to empower your data no matter how big it is"...
I'll state here that the jury is back in, and the answer is this: "no, you don't need highly trained scientists" to get value from your data (which is what I assumed was meant by "empower your data". I've trained more than a thousand analysts and have heard back and continue to meet with dozens of them--they can do it with only my minimal training. I've worked with several organizations helping completely green in-house analysts move from having no idea how to gain value from their data to being productive analysts saving $$ and improving ROI.
This doesn't mean there isn't a need and role for highly trained scientists to tackle the hardest of problems. But this is just like any other field: there are orders of magnitude more relatively easy problems to solve than very hard problems to solve. With no course training and by only reading a manual, I was able to change alternators and water pumps in my car. That's ROI. But when the transmission failed, there's no way I was going to tackle that, so I needed a highly trained professional.
By the way, the bios you have for the top 5 data scientiests are fantastic: the diversity of backgrounds is interesting. I think my favorite though is DJ Patil, the "math dunce" See my smartdatacollective Target, Pregnancy and Predictive Analytics, Part I for a description on way predictive analytics is not math. :)