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APPs 28 Jan 2015

Big Data in apps: 7 tools designed for end users

Big Data is often a great mystery. We view this technology as something far removed from the end user, something only for large companies that process great volumes of information to obtain results that only they can interpret. But Big Data is much closer to the general public than we think. Today we present some examples of tools for end users that make intensive use of Big Data.

Usually, when we think about Big Data what comes to mind is large computers processing huge amounts of varied information to obtain a result that is relevant to a company. But this is not always the case, there are also Big Data tools designed for end users, for our clients.

Today we present seven tools that are precisely designed for our clients, for end users. They are all well-known, but perhaps we have never thought about the technology behind them.

Swiftkey, a predictive keyboard

One of the most tedious tasks when we use a cellphone or a tablet is writing. And this is because a touchscreen is not exactly the best instrument in which to embed a keyboard. But there are companies like Swiftkey that have managed to make this experience much more user-friendly, and thanks to Big Data.

The incredible thing about Swiftkey is its predictive capacity. It’s capable of predicting not only the word we are writing (with one or two keystrokes), but also the next word we’re going to write. How does it do it? First, with a gigantic database that contains the most common expressions. But also by learning how each user usually writes.

The Swiftkey experience is incredible and now that it can also be used on iPhones and iPads there is no excuse for not using it. Once you use it, you’ll never want to go back to the complicated standard keyboard.

On iTunes | Swiftkey

On Google Play | Swiftkey

Pandora, the music you like

Pandora is a music service that unfortunately had to close down in Europe. Its model is very simple. You specify a song you like and it creates a customized radio with similar songs, and you can tell Pandora whether you like them or not.

The incredible thing about Pandora is its capacity for guessing our musical tastes. I know many people who passionately follow music bands they discovered through Pandora. And its capacity comes from Big Data analysis, from analyzing masses of information on its users’ tastes.

Google Now, the information you want, when you need it

Google has a service tightly integrated with its Android cellphones (although it’s also available for iPhones and iPads) that basically provides all the information you need without having to ask for it. For example, in the morning it tells you how long it’s going to take you go get to work based on the traffic. It provides reading recommendations based on what you look for and click on in the search engine. It tells you about the weather in your city, but also in your city of destination if you’re traveling (it looks on Gmail whether you’ve bought plane tickets).

Google Now is incredible. There are more and more functionalities, and that’s thanks to Big Data. It automatically uses everything it knows about you to make your life a bit easier. Once you use it, you won’t want to go back.

On iTunes | Google

On Google Play | Google

YouTube, customized recommendations

The great provider of videos YouTube also relies on Big Data, of course. And it does so by suggesting videos you might be interested in, based on the videos you watch and those you have marked as interesting.

This results in two very interesting things both for the users and for YouTube itself: users are constantly shown interesting things to watch, even if they visit the website with nothing specific in mind to watch; and YouTube enables its users to spend more time on its website, and revenue increases accordingly. A win-win situation, right?

Amazon, suggested purchases

The giant retailer Amazon also relies on Big Data. In this case to recommend products to its clients, based on what they’re looking for, watch and buy, as well as on information from other similar users. This is done both when we browse its website and with regular customized e-mails.

It’s clearly a winning strategy. It’s like the typical store catalog, but each Amazon user has their own, tailored to their tastes. It would be interesting to know what the conversion ratio of these customized recommendations is, but even if it’s low, given the volume of Amazon users, there’s no doubt that the Big Data system pays for itself.

Twitter, recommendations to follow

When you visit Twitter you always see a small box that recommends people to follow. And these recommendations are based on Big Data: similar people you follow, profiles similar to yours, etc.

These recommendations are always interesting, and that’s because of the capacity to adapt to our tastes. There can be a great deal of work behind a small box on a website. In the end, what’s important is that it’s valuable for the user. Big Data doesn’t have to look spectacular on the screen.

Earthquake and tsunami early detection system in Japan

In 2011 there was a devastating earthquake in Japan, accompanied by an equally devastating tsunami. The incredible thing is that the death toll wasn’t higher, given the intensity of the event. But there is one explanation: since 2007 Japan has an earthquake and tsunami early detection system.

It has a network of seismographs around the country, a centralized system that collects the information, analyzes it in real time and predicts, based on the P waves prior to the actual earthquake, when and where it will strike. This information is conveyed automatically to the Japanese people by television, radio and cellphone.

The incredible thing is that in the 2011 earthquake the population were warned of the earthquake some 30 seconds before it struck, while warning of the tsunami was giving hours in advance. Obviously a very expensive system that analyzes masses of data in real time, but that also saved many lives.

More information | YouTube

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