Cloud Computing and the Quantified Self

The concept of the quantified self sometimes seems to be just as over-analyzed as the self itself. Many bloggers write about the consequences of using apps that aim to improve your health, self-awareness, and mindfulness. Opinions on the potential benefits of self-knowledge and the problems of badly interpreted data are widely divided, but it doesn’t change the fact that our smartphones are loaded with small but accurate sensors that collect information about everything we do and everywhere we go – and that people love to view and share this information.

Supported by other trends like big data, gamification, and mobility, quantified self applications are growing in popularity, and followers form communities to compare and discuss their collected information. Fitness apps like Digifit and Fitbit monitor physical activity; Moodpanda and MoodScope help to track your moods and the events that influence them; dozens of digital coaches help you lose weight or improve your sleep quality.

Partly, current development in quantified self applications is driven by sensors becoming smaller, cheaper, and more advanced. The real revolution, however, can be found in processing the huge amounts of collected data using cloud computing. Utilizing the cloud means that all users have access to a network of servers that share amazing processing power and virtually unlimited storage space on demand, without having to rely on the specifications of their own device. Out-dated computers or simple smartphones and tablets can be used as thin clients as long as they’re connected to the internet; all software they need to run apps is found in the cloud through nothing more than a browser window.

There are three distinct stages in self-quantification: data collection, data interpretation, and data visualisation. Sensors in phones and specifically designed devices monitor everything from physiological data such as heart rate, sleeping patterns, and amount of calories burnt, to psychological information like mood. Data collection is easy, but making sense of it is a lot harder. Cloud computing especially plays a part in this, by providing unprecedented processing power and access to the huge amounts of data from other users for comparison, previously only available to large corporations.

Data without patterns in relation to other relevant data is meaningless. That’s why developers pay a lot of attention to the visualisation of collected data, and it’s also why comparing it to large amounts of shared data in the cloud can make these patterns more accurate and more meaningful. Here, there is also an opportunity for the use of APIs, especially for users to build their own apps that discover patterns in large data sets and visualize these in a way that clarifies them in an appealing way. Soon, users will have full control over the manipulation of their data using complex algorithms.

Some quantified self apps already utilize certain aspects of cloud computing, especially for location-tracking. My Tracks, for example, uses data from GPS and Google Maps to automatically update information in real time, instead of requiring input from the user. But apps like this still rely on their own servers and the users’ own software, instead of using cloud management tools that take advantage of the full potential of the cloud.

One thing the cloud doesn’t solve is the main problem found in many quantified self apps: an overload of information. But cloud computing provides users and amateur developers with the tools to prevent bad analyses of data that are easily manipulated into showing the most desirable results using dodgy statistics. Quantitative information alone is not enough, but with the right interpretations and visualisations, the quantified self trend could really improve your life.