Newsroom

Big Data: Dating Apps

Big Data: Dating Apps

Over the summer, our Spanish colleague Héctor Pérez Arteaga wrote about a much-used application of Big Data: Dating Apps. You might have been on holiday with your family and loved-one(s) and missed the article, so we wanted to come back to it here. 

We are not new to dating apps and finding love online. ‘Hot or Not’, the site that lets you rate other people’s attractiveness, was launched in the US in 2000. It inspired many of the dating apps that are currently still around. According to the film The Social Network, it was also the inspiration behind Facebook. Mark Zuckerberg created Facemash; using pictures of Harvard students to let visitors vote which of the two pictures presented showed the most attractive person.

It changed the dating scene into another of society’s instant gratifications. What used to be a game of chance, is now subject to algorithm. But it works and 1 in 4 relationships nowadays starts online – a number that is likely to be higher amongst Millennials.

What is behind these apps is Big Data, distributed computing, cloud systems (like, Amazon, Azure, Google Cloud) and reduced costs of soft- and hardware.

Let’s look at Tinder, the king of dating apps. Tinder operates in 196 countries. Users swipe 1.6 billion times a day on average. This results in 26 million matches and enables 1.5 million dates a week. It really is a numbers game!

Tinder processes users’ behaviour in the app and matches this information with data from other sources, like the aforementioned Facebook, or Instagram. It updates its recommendations in real-time whilst you interact. It is a hive of interactions with a volume of data that is so vast that it requires powerful data processing.

We do not use centralised processing anymore but instead get many machines to work together; divide the work and organise it into fail-resistant structures – making it powerful and scalable. In the event of failure of one of the machines, the work is simply redistributed.

What is integral to the success of this, is the quality of the data. For example, Tinder recognised that an increasing number of users would randomly like everyone they saw on the app in a bit to increase their chances to getting likes back. This made the data less valuable. Tinder solved this by limiting the number of times a user could swipe to the right in a specific period of time – forcing people to be more selective.

At everis, we (sadly) have not worked on any dating apps but we do use Big Data technologies in many other projects for our all our sectors, including banking, insurance, industry and health. It might not be the answer to a lonely Saturday evening, but Big Data can save lives. everis Spain just completed a successful pilot at several ICUs at a hospital in Seville, monitoring critically ill patients and predicting critical situations 8 minutes in advance. Big Data also helped many of our clients to prepare for the change to GDPR back in May this year.

Big Data is powerful and its applications infinite – for good and for bad, so be aware of the information you share. May the force of love be with you!