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Test Shows Big Data Text Analysis Inconsistent, Inaccurate
DillyTonto writes The "state of the art" in big-data (text) analysis turns out to use a method of categorizing words and documents that, when tested, offered different results for the same data 20% of the time and was flat wrong another 10%, according to researchers at Northwestern. The Researchers offered a more accurate method, but only as an example of how to use community detection algorithms to improve on the leading method (LDA). Meanwhile, a certain percentage of answers from all those big data installations will continue to be flat wrong until they're re-run, which will make them wrong in a different way.
In other words, when it comes to big data, you're doing it wrong - and if you change how you're doing it, you're still going to be doing it wrong.
Big data fails to live up to hype - news at 11.
"Transparent" is a shit show that trades on every stereotype going. A man in drag is NOT a transsexual.
People thought you could bypass doing the work and actually understand what is going on but get useful results.
Turns out you can't.
Or put another way, If big data is so great "Why didn't Watson see IBM's crash coming ?"
The difference between "92% accurate" and "accurate enough for my task" are profound.
If you were using these kind of analytics to bill your customers, 92% would be hideously inaccurate. You'd face lawsuits on a daily basis, and you wouldn't survive a month in business. So the easy answer is, "this would be the wrong tool for billing."
But if you're advertising, you know the rates at which people bite on your message. Perhaps only 0.1% of random people are going to respond, but of people who are interested, 5.0% might bite. If you have the choice between sending the message to 10000 random people, or to 217 targeted people (only 92% of whom may be your target audience), both groups will deliver the same 10 hits. Let's say the cost per message is $10.00 per thousand views. The first wave of advertising cost you $100. The second costs you $2.17. Big Data, with all of its inaccuracies, still improves your results by a wide margin.
Way too often people like this point out that perfection is impossible. They presume that "because it's not perfect, it's useless." The answer is not always to focus on becoming more accurate, but to choose the right tool for the job, and to learn how to recognize when it's good enough to be usable. At that point you learn how to cope with the inaccuracy and derive the maximum benefits possible given what you have.
John
The hype over big data comes from companies like Facebook or Amazon. It's a consequence of bad decisions made in the early days.
It's easy to see how this happens. Some dude says: to hell with data models, data governance or a formal approach to data warehousing; those are too "enterprisey", we are a nimble startup with the need to pivot and build MVPs quickly, let's just serialize our java/python/php objects for now. A billion dollars and 20 petabytes later the company has to rely on machine learning to sift through their digital garbage so they could find out how many users they have. And if they need stuff that runs on thousands of commodity servers, like hadoop or cassandra, it's not because it's better, it's because IBM doesn't make a mainframe big enough to help them.
In most organization these solutions should not even be considered. That's like considering bariatric surgery to lose 10 lbs because it helped the morbidly obese lady next door lose 250 lbs.
But it's cooler to say you work on a Spark project than on evolving an Inman-inspired enterprise data warehouse using Netezza to crunch numbers. So let's all brush up on our graph theory and deliver unreliable answers to painstakingly formulated questions until the next fad kicks in.
lucm, indeed.
All models are wrong, some are useful.
If you can't be good, be good at it!
The first hint you get is when you notice this paper was published in a physics journal, not a great sign. Then you actually start reading, and you see they declare LDA as "state of the art". And when you actually read what they propose it is a bunch of standard text techniques which actually work quite well with LDA.
So what they actually showed is that taking vanilla algorithms out of the box without even the most basic data processing under-performs compared to superior data processing attached to a simpler algorithm. Which anyone which did any sort of text processing or any other kind of data managling already new.