(Big) Data and sensemaking

This week’s subject is personal informatics, quantified self, lived informatics, living by numbers – all the things that really intrigue me. Our learning triangle was given a paper by Ian Li called “A Stage-Based Model of Personal Informatics Systems”, which approaches personal information systems with a stage-based model dividing the process to 5 stages – preparation, collection, integration, reflection, and action. They looked at different existing personal informatic systems that people use and analysed what are their shortcomings according to the participants. The study was done by surveying 68 people and then doing follow-up interviews for 11 of them.   

Author emphasises that there is no comprehensive list of problems that users experience when using self tracking systems. Some of the issues that they recorded were related to finding an appropriate tool for collecting, going back and forth between applications, lack of multi-faceted tools, insufficient data and tools for visualising it. As people are more and more interested in collecting personal data and the paper gives good insights for developers of how to improve personal informatic systems and make them more useful and holistic.

I like the breakdown of self monitoring into stages, because in order to make real sense of the data we have to be aware what are we doing in every step. Robert L. Mercer said: “There is no data like more data!”. As this might be true when building a robust machine learning models, I would argue that when trying to unravel your life using numbers it’s not. Just collecting data without proper analysis – the data becomes noise. In the end we want insights not numbers.

This might be because of my interest, but I didn’t get any new information from the paper. I have done self tracking for about 6 years now. I tried different applications but what they all have limitations and I always end up collecting raw sensor data and then using my own programming skills to extract some knowledge. So I agree with the author that there is a need for better systems to extract meaning from data.

Finding a sample of personal tracking isn’t that hard for me as I have developed tracking solutions and tracked myself for years now. The thing that always irritates me is the insights that some applications offer you (e.g deep sleep, light sleep). What is the meaning of it? What influences it? How can I learn from it and improve my life? What do we need to build to have the heureka moment? These questions trouble me and keep me awake. So why do we use these self monitoring apps and gadgets if we don’t get anything useful out of them? Is it because it is cool thing to do? I hope to find some answers to these questions at the end of my PhD, by experimenting on myself and unraveling other people’s relationships with their data.

I’ll leave you with my 4 years of passive mobile positioning data from Estonia LINK.