Join our Meetup on August 2nd 2018 for the chance build your own innovation using passive data
The winners of our passive data challenge will receive:
- 3M NIS investment and incubation from the eHV
- A free trip to Copenhagen to compete in LEO Innovation Lab’s Global Challenge
- 10k EUR prize, 3 months of mentoring & incubation in Copenhagen
What is Passive Data?
Did you know there’s an app that, with your permission, gathers 10,000 data points from your phone to determine whether you’ll get a loan? By analysing your social media activity, call duration, power usage, recharge frequency and even how many apps you have and the way your smartphone is organised, this app can predict how creditworthy you are better than most banks.
Imagine a world where your smartphone can…
… uncover hidden diseases that you may have but not yet know about (e.g., is eating fast food 10 times a week while not exercising predictive of pre-diabetes?)
… predict how responsible you might be in taking your medication (e.g., does correlating how many times you’ve left your home with 5% battery or are late for meetings predict whether you’ll take your medication responsibly?)
… warn you a week before a psoriatic flareup starts on your skin (e.g., based on your individual ‘triggers’, like stress levels, weather, upcoming trips, when you’ve visited a particular restaurant?)
Welcome to the world of passive data
While most people think about passive data as the basic data collected from the smartphone’s sensors, which include elements like location (from the GPS) and movement (from the accelerometer), data from sensors is just the first layer.
Activity data like duration of calls, battery recharge frequency, length or frequency of texts, number of times visiting a certain app or even the frequency of app updates can all contribute to a rich network of behavioural data. These data can be explored to better understand an individual, and how they move through their life.
Most of us don’t realize, but even the number of apps, the version of the operating system on our phone, how we organize our contacts on our contact list, the presence or absence of particular apps, as well as audiovisual data (e.g., analysis of voice patterns) are also included in the universe of passive data.
Putting these data together in unique combinations and applying the principles of behavioral science can yield powerful insights both in healthcare and beyond.
For example, believe it or not, there is evidence that if more than 40% of your contacts are organized by both first and last name, you are more creditworthy.
Of course, this level of radical visibility comes with ethical considerations. How can we ensure consent is baked into our passive data innovation? How can we bake in security to new the development of new apps and platforms that rely on passive data to protect our users? These are all core questions that you’ll need to reflect on during this program.
Over the next few weeks, we’ll be running a series of inspiring material to help guide you through this passive data challenge. We’ll be summarising the various types of data you can use from a smartphone to spark your thinking and asking you big questions to push you beyond your limits into exponential innovation.
So why do we care?
We all know that data is growing faster than ever before.
By the year 2020, it’s forecasted that over 44 zettabytes of digital data will exist (that’s 44 trillion gigabytes) and that there will be over 6.1 billion smartphones in the world, with over 50 billion smart connected devices, all developed to collect, analyze and share data.
…at the moment less than 0.5% of all data is actually analyzed and used.
Just imagine the possibilities.
This challenge for innovation is designed to change all that.
Imagine the improvement in healthcare, our knowledge of diseases, the way we run clinical trials to find the best medications for patients, treatment decisions and longer-term care if we can intelligently tap into the vast amounts of passive data generated.
How can we optimally collect and use passive digital data?
How can we use ambient tools and sensors increase our understanding of patients and chronic diseases by identifying the patients more accurately and less invasively?
The answer to these questions will require expertise in behavioral psychology, data analytics, data science, and artificial intelligence.
We hope you will join us in tackling this challenge.