Shashwat Pradhan Shashwat Pradhan

Quantified Self: Understanding the data

In 2015 we saw the rise of expert based platforms for Quantified Self enthusiasts to help them understand their data and make actions based on it. These experts or coaches have been popular around the fitness side of self-tracking. With experts recommending you on exercising and dieting, data from smart phones & wearables is becoming more valuable.

The early era of Quantified Self lacked actionable insights for users. For example only 30% of Fitbit users were still wearing the band after 3 months. Was the step count data not useful enough? Most likely users weren’t able to set goals or achieve with these goals.

Quantified Self apps and services need to move the focus from the data to actionable insights. Even a simple graph, with your last week’s data can be a starter into actionable insights. Users have to be able to correlate this data with their own history or even with friends & family in certain parameters. This is what pushes self-improvement, that’s why the experts have been brought into these platforms. These expert or self-help services keep users more engaged by breaking down the data that has been collected and also by giving a straight forward plan of action. Think of it as a personal trainer at a gym. The best part of is that Artificial Intelligence is being used to achieve this.


With so much user data available, it is easier to train Machine Learning models to provide users with data analysis. Artificial Intelligence will help these services scale. We are seeing many companies using a mix of experts and AI algorithms. This is a great way of making the user data more meaningful.

We have seen how life loggers are able to make lifestyle changes and hack their disorders away with simple tracking techniques. With trackers getting more sophisticated and diverse, people can now correlate how different aspects in our life impact each other. I have been tracking my life for the past year with Instant, I discovered on days that I am using my smartphone more I end up walking less and spend more time at home. Seeing my daily time spent on a single graph makes me understand my time distribution & productivity better.

Apart from experts, correlating data on a weekly & comparative basis are useful ways to analyse all the data that our sensors track for us. I am looking forward to seeing how the data analysis experience improves along with the new tracking techniques. From what I have understood in the past year, I see that analysis is a more important step than tracking in the journey of self-improvement.

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