1 - Analysts will matter more than data scientists
Well, they always have. From the London Whale all the way back to Long Term Capital (wanna buy a tulip?), with a Great Recession diversion in between, whenever policy and data collide, data loses. More's the pity.
2 - R will replace legacy SAS solutions and go mainstream
Perhaps. Revolution sells a RBAR version of R, and appears (to me, at least) attempting to encroach on SAS's meme: "your all in one data platform" (my instant quote, but SAS said much the same in the Goodnight Old Days). By the mid-80s SAS was selling itself as a database, not just a stat pack.
This paper examines the database features of the Version 6 SAS System  and compares them to the services offered by several popular DBMSs. The conclusion is that the SAS System can provide a cost-effective alternative to a commercial DBMS for the storage of data.
I don't follow SAS marketing these days to know whether they're still pushing that notion.
3 - Big Data will bring its "A game" in sports marketing
Can't say much about that. Nate Silver started out in bayesball, so it could be. Not that I care even a little bit.
4 - Hadoop moves from curiosity to critical
Codd, I hope not. All this NoSql stupidity has been revealed by MarkLogic and healthcare.gov. It is true that stat types have always viewed data as flatfile images. Have I mentioned earning kopecks in the basement of the UMass graduate center typing 059 input for BMDP? Stat types remain enamoured of flat. PL/R has shown them the way, and both Oracle and SAP are providing similar functionality. Still, they persist in viewing the computer as a Friden adding machine.
5 - Gartner's prediction that the line-of-business will drive analytics spend will happen
Again, this is not new. "Mad Men" have been using stats since Hector was a pup. The only difference is that R (and before it, 1-2-3 and Excel) with i7 chips and 32gig PCs make it possible to do such analyses without a server, much less a mainframe. Beware: never let the children play with the sharp cutlery.
6 - Visual analytics continues to grow but users need more
Well, "users" remain in love with pie charts, so I'm not so sanguine. Between lattice and ggplot2, R has more than enough built-in graphics to give Tufte a massive MI. Don't do that to him; he's a nice guy.
7 - Analysts lives get more complex, but also easier
Again, beware children and sharp cutlery. The London Whale made a mess just with Excel. Hmm. Progress requires change, but change isn't necessarily progress.
8 - Predictive analytics will no longer be a specialist subject
And, in due time, the same will happen with neurosurgery. Utopia will proliferate.
9 - Customer analytics is the next big marketing role
Next? What does anyone think Google and Amazon and ... is doing with all that personal data the sheep provide? Curing cancer?
10 - A new analytics stack will emerge
Revolution R, I suppose he means.
11 - Location meets big data analytics
Again, old wine, new bottles. While a graduate student, I did some stat work with one of my undergraduate professors. He called his product "Shift and Share Analysis", wherein we took the smallest grain SMSA data to predict the best location for local bank branches. Buffalo Bob (yeah, that's what we called him when he wasn't looking) was by no means the first to try this. Big data doesn't help here, much.
12 - NoSQL meets analytics
Analysts have always hated RDBMS, so this is nothing to predict.
13, 14 he doesn't list.