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Big Data Doesn’t Exist

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My customers always lie to me. They don’t lie about what they can afford. They don’t lie about how much (or how little) customer service they’ll need. They don’t lie about how quickly they can pay us.

They lie about how much data they have.

At first, I thought it was just a weird one-off. A client told us they needed to handle several billion calls each month, a “massive data stream.” That much analysis comes with a huge price tag. Once I made this clear, the truth came out: they hoped to ramp up to a million calls a day in the next several months. Even if they reached this optimistic goal, they’d only have less than one one-hundredth of the data they’d originally claimed.

It’s not just this client, either. I’ve found it’s a good rule of thumb to assume a company has one one-thousandth of the data they say they do.

“Big Data” Isn’t Big

Companies brag about the size of their datasets the way fishermen brag about the size of their fish. They claim access to endless terabytes of information. The advantages seem obvious: the more you know, the better.

Based on their marketing materials, it would seem that this data makes companies almost clairvoyant. They claim deep insights about everything from the performance of employees to the preferences of their customer base. More data means more understanding about how people make decisions, what people buy, what motivates them — right?

But marketing materials, like fishermen, exaggerate. Most companies only have a fraction of the data they claim. And typically, only a small fraction of that fraction is useful for generating any non-trivial insight.

Most “Big Data” Isn’t Actually Useful

Why do companies lie about the size of their data? Because they want to feel like one of the big dogs. They’ve heard about the enormous reserves of data collected by the likes of Amazon, Facebook and Google. And even though they don’t have the reach to collect that much data — or the money to buy it — they want to feel (and have outsiders think) they are in on the trend. As data analyst Cathy O’Neil noted in a recent blog post, many believe that “when you take a normal tech company and sprinkle on data, you get the next Google.”

But even big companies only use a tiny fraction of the data they collect.

Big data isn’t big, but good data is even smaller.

Twitter processes around 8 terabytes of data per day. That sounds intimidating to a small company trying to extract consumer insights from tweets. But how much of that data is the actual content of tweets? Twitter users create 500 million tweets per day, and the average tweet is 60 characters. If we do the simple math, that’s just 30 gigabytes of actual text content per day — about half a percent of 8 terabytes.

The pattern continues. Wikipedia is one of the largest repositories of text on the Internet, but all its text data could fit on a single USB. All the music in the world could fit on a $600 disk drive. I could go on, but the point is this: big data isn’t big, but good data is even smaller.

Making The Most Of Small Data

If most large datasets are useless, why talk about them at all? Because they aren’t useless for everyone. Deep-learning models can separate signal from noise, finding patterns that would typically take experts months to codify. But typical deep-learning models only work on massive amounts of labeled data. And labelling a large dataset takes hundreds of thousands of dollars and months of time. That’s a job for a corporate behemoth like Facebook or Google. Too many smaller companies don’t realize this and acquire massive data stores that they can’t afford to use.

These companies have a better option. They can get more value out of the data they already have.

True, most deep-learning algorithms need large datasets. But we can also design them to make inferences from small data, just like humans do. Using transfer learning, we can train an algorithm on a large dataset before sending it to work on a small one. This makes the learning process 100 to 1,000 times more effective.

Here are just a few examples of how startups put transfer learning to business use:

  • Dato’s GraphLab Create platform can be used to identify and classify huge numbers of images in fractions of a second. Users can apply existing features from previously trained deep-learning models — or train their own model on a dataset, like ImageNet.

  • Clarifai’s image recognition API tags images with descriptive text, making photo archives easily searchable. Its deep-learning algorithm also works on streaming video, which allows advertisers to drop in an ad that’s relevant to the content the user has just seen.

  • MetaMind’s AI platform can judge whether the content of an individual tweet about a brand is positive or negative, and also determine the main theme of a Twitter discussion surrounding it. For a company looking for insight into their customers’ opinions, that’s much more useful than simply scraping age, sex and location data from many more thousands of accounts.

You don’t even have to be a programmer to take advantage of these services. Blockspring lets users mash-up APIs in Excel spreadsheets without writing a line of code.

With all of these options available, it makes even less sense to purchase big data by the terabyte — much less to brag about it.

It’s clear the future of data isn’t big. It’s small.

With contribution by Artem Kaznatcheev and Ada Kulesza of Hippo Reads.
Featured Image: Maksim Kabakou/Shutterstock