Have you ever stopped to think about all the data you generate throughout the day? It was the possibility of actually making use of all this data through numerous apps, records and data bases that gave rise to Big Data.
The smartphone and social network boom has also meant a huge rise in the amount of data generated. So much so that just by taking a few steps, we generate an enormously diverse data base. All of this data is analyzed by different professionals that have created the revolution currently underway.
We accompany 39 year old Manuela for a few hours. She is just like us. She lives downtown and does simple, everyday things. Almost every single movement she makes creates a record, a notch in some data base, some list. It’s a Tuesday. The alarm on her phone goes off at 6:45AM just like every morning. She turns it off and goes to the kitchen, phone in hand with her eyes barely open. She turns on the coffee maker and looks for a capsule – yes, the kind they deliver every two months for free if you belong to a club.
18 steps. Her smartphone reveals it all. She goes to the bathroom, washes her hands, 32 steps. She drinks the coffee, quickly puts on workout clothes and goes for a run. To wake up she puts some music on her phone. Spotify lets her go from song to song without changing the album. Many of her office colleagues will later find out that she listened to The Strokes, The XX and then a Kings of Leon song. Her Facebook is synchronized with the streaming platform so everyone will quickly know what kind of mood she’s in that day.
69 heartbeats per minute before starting and 80 after running 4 km. Once again, her cell phone lets her check her heart rate just by putting a finger on the screen. If that weren’t enough, she has a record of her run thanks to one of those apps that keeps track for you and lets you share it with a community. She goes home, showers and gets ready.
At 8:30 AM she gets in her car in the resident area. She normally takes the metro, but today she has an appointment with her doctor at 9:00 AM. She has a hard time parking in the blue zone. She enters her license plate and pays with a credit card because she doesn’t have change. In the waiting room, Manuela uses the time to check the news on different social networks, share a few things and comment on others. She checks her three different email accounts: her work email, her personal email and another email she created a century ago where she receives promotional, less important emails.
Finally, Manuela has all the medicine she needs on her electronic healthcare card – not a lot but she has chronic allergies and stomach pain. She goes to the pharmacy every two months for Omeoprazol and every month for Ebastina. She doesn’t even need to ask the pharmacist, just hand him or her the card. She gets back in her car and parks it near her house. She checks the traffic to see if it’s better to take the bus or the metro. One of the apps on her cell phone provides her the recommendation. She walks to the bus stop. She has already moved 6.5 km and it’s 10:00 in the morning. She gets to work at 10:30AM, an advertising agency downtown. Before starting to work, she has already created a huge data trail and the day has just begun.
Her movements, even her way of thinking and opinions have been recorded. They were collected in different data bases: mobile apps for health and fitness, social networks, companies that build customer loyalty by using her data, email, credit card payments, healthcare services data bases, patient lists, geolocation services, transportation services… Next to nothing.
For Julia Díaz, Director of Innovation for Health and Energy Predictive Analytics at IIC, this data trail “is unstoppable in social situations. We all use the same social networks to varying degrees. We are providing our data to any app we use, even if it’s not a social network. We have given our consent.”
In other words, she believes that “whether we use social networks or simply the apps that are part of our everyday life, we are providing so much information. It obviously needs to be analyzed to extract its value so that the information can be used in an intelligent manner. This will allow us to better optimize resources and anticipate needs that will be real,” she adds.
Journalist Mar Cabra, Editor of the Data and Research Unit at the International Consortium of Investigative Journalists, is always talking about the data trail we leave, or the trail crated by the data itself. “For example, a fire. When the fire occurs a report is filed, which is then sent to the autonomous community, then to the Ministry, and suddenly there a data base is created. When there is a data base it means the data has left a trail,” she explains.
We also leave a trail when we move, just like Manuela. Cabra is aware that she is constantly leaving her own data trail. “Every time I enter my license plate in the parking meter, for example. In fact, I think that the Madrid City Council has a better idea of where I go than I do,” she jokes.
“We are not at all aware, but our data trail is even greater with mobile technologies. When we move, the phone companies know exactly where we are at all times. And so do social networks if you have the geolocation turned on. Most people are not aware of this and I think it’s one of the biggest challenges we face – being more aware of the data trail we are leaving behind,” says the journalist. Of course, every day more and more large corporations and experts are capable of analyzing this trail and drawing their own conclusions, “even just by looking at meta-data from our email,” notes the journalist. That simple.
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