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What's After Big Data?
gthuang88 writes: As the marketing hype around "big data" subsides, a recent wave of startups is solving a new class of data-related problems and showing where the field is headed. Niche analytics companies like RStudio, Vast, and FarmLink are trying to provide insights for specific industries such as finance, real estate, and agriculture. Data-wrangling software from startups like Tamr and Trifacta is targeting enterprises looking to find and prep corporate data. And heavily funded startups such as Actifio and DataGravity are trying to make data-storage systems smarter. Together, these efforts highlight where emerging data technologies might actually be used in the business world.
looking to find and prep corporate data
I read as "looking to find and grep corporate data"...
After big data comes bigger data. Why, did you think otherwise?
Don't waste your vote! Vote for whoever you want, unless you live in a swing state it won't matter anyways
It's a perfectly cromulent buzzword
After big data they will hire people to think and actually produce useful/actionable insights.
After that they will hire thinking machines.
After that .. with the last vestiges of humanity in zoo's for the amusement of machines .. it's anyone's guess.
Big Garbage(can). Most of the data is worthless
TFA's question answered is in headline
Thank you Dave Raggett
Cloud Data. Now if you'll excuse me, I'm going to create a phony website to attract suckers investors.
Deep Learning is the next marketing buzzword, perhaps with good reason this time.
"When information is power, privacy is freedom" - Jah-Wren Ryel
Rare. I prefer to have Big Macs as rarely as possible.
The problem with 'Big Data' is everyone is trying to use it as a substitute for actual hypothesizing and experimentation.
I am not suggesting it isn't useful, it is, and it can be a huge help in identifying non-intuitive relationships that may exist. Its not being marketed that way though! Everyone is trying to sell it as the solution to all their unresolved problems and knowledge gaps.
At the end of the day all it can ever show is correlation, never causation. All the fancy AIs we add on top are really just correlation engines as well. One day real-soon-now WATSON or something like it will diagnose your cancer. It won't 'discover' the cure though, it will just apply the 'KNOWN' treatment that statistically correlates with the best outcome, hopefully excluding some which correlate with especially un pleasant side effects.
Same is true with the financial markets. Big Data alone will never discover a unified theory that explains market behavior. It will probably make a handful of people stupid amounts of money based again or event correlation and speed. As long as those are the drivers though we will remain forever at risk of sudden meltdowns.
Repeal the 17th Amendment TODAY! Also Please Read http://www.gnu.org/philosophy/right-to-read.html
... distributed across a multiple heterogeneous platforms.
It's like trainspotting, but for advertising memes.
Gartner is the king/pusher of course. But I think they were actually insightful about this 5 or so years ago. They predicted about 3 year of all hype, no product "cloud", another 3 years of practical, useful cloud infrastructure with nothing really taking advantage of it, and only after that would we see startups (and VC investment opportunities) making use of the cloud to make actual products. I think we're almost there.
Even for hobby programming, the cloud is becoming quite appealing. For example, take a look at this remarkable Mabdelbrot zoom to 10^275. This required 6 core-years to render (6 months wall clock). If you have the patience, the machines sitting idle (perhaps discarded bitcoin rigs) and no fear of power bills, then sure, turn on 3 old high-CPU towers for 6 months. But if you're good at massively parallel coding (and Mabdelbrot rendering is great to learn that!) you can usually get AWS Spot machines for under a penny per core-hour. That means you can get that 6 core-years of CPU for about the price of a midrange geek PC, and you can get thousands of cores in parallel, and be done rendering in a day.
For a hobby project it might be hard to justify spending $hundreds this way, but for a start-up it makes perfect sense. So there's something to the "cloud" IMO if you're trying to do supercomputer parallelism on a shoestring budget, something that's really only become possible in the past couple of years. I'm not sure how cheap 10000 core-hours for $100 is, really, but 10000 cores in parallel for an hour for $100 is something wonderful.
Socialism: a lie told by totalitarians and believed by fools.
RStudio is an IDE for the R language. When an author conflates that with whatever those other things are, you can be fairly sure whatever he's saying is likely to be up there on the bullshit scale.
[FUCK BETA]