Santa Clara, CA — There’s no shortage of news these days about the quantified self movement, tracking, wearables and the recording of personal data. We’ve certainly covered a lot of it here in this blog. Seems like every week there is a new app or device on the market ready to track any measurement you can think of—from calories and sleep to medical condition-specific metrics like glucose levels and seizure frequency.

While the possibilities associated with being able to track all of this data are certainly exciting, there is still much to be desired in terms of how it is being presented back to the user and made meaningful. Most commonly the data is confined to the fixed representations designed by the app makers, and at best, served up in an aggregate view. While this might be okay for making broad comparisons on where you stand in the crowd, what is it really telling you about you?

Dawn Nafus, an anthropologist at Intel Labs who has spent time researching the quantified self phenomenon, recently discussed the limitations of typical aggregate data interpretation:

What “wellness” is to me is different than what it is to you. One size does not fit all, and so figuring out what new ‘healthy’ thing actually works for you, and what is realistic for you, is a very real problem. How, in these circumstances, might a self-tracker interpret all the data she has collected? If the issue is what is right for you, then data interpretation can only meaningfully be done in context, not in aggregate.

She goes on to say that the optimal scenario for gathering the most meaning from the data, and putting it into the proper context is when “the interpreter and data generator are one and the same person.”

To help make that scenario more of a reality, Dawn and a team of colleagues at Intel (in collaboration with Savage Internet and Empirical) developed a tool called Data Sense which was recently released as an open research experiment. Data Sense is designed to empower the community of self trackers to do more with their data and uncover more personal and actionable insights. It uses visual interactions to make the data easier to explore, and allows the user to look at data from a variety of sources to spot patterns and overlaps that makes sense to them.

Video Player



Tools like Data Sense may hold the key to unlocking the true behavior-changing potential of the tracking devices and apps. As they simply and elegantly state on their website: “Our hypothesis is that when better tools are built, more people might come to understand the value of their own data, and be in a better position to advocate for themselves and their community in a big data world.”

About the Author:

Jeffrey Giermek