I finally bought a smart wristband, a device that measures how much you sleep and how much you walk.
After one month, I like it more then ever. It’s not very accurate, but it gives me a rough idea of how much I walk and rest. It nudges me in doing the right thing: if I see that I did not walk much, I’m more likely to choose not to take the bus back and walk instead.
What matters is not the actual number of steps, but the capacity to be aware of myself. I can easily see if I am more or less active compared to my normal rate. I have trend data, and that’s all I care. I don’t care much for comparing against other people. But its great to be able to detect whether I’m having a lazy or active day.
And that’s probably true for big data in general. Despite the hype, it’s very very difficult to make sense of many large datasets. Cross analyzing data, identifying patterns and correlation is harder than we expected [1,2]. Basically, the problem is to make sense of big data.
But big data means also that as many things get measured, you will soon start having trend data for everything. You will uncover anomalies much earlier because everything will be measured.
In other words, while it remains difficult to cross analyze a huge amount of large datasets to uncover correlation, it will become much easier to simply uncover anomalies by comparing new data with old data. This suggests that big data will become much more important in the data: we’re today deploying sensors and struggling to make sense of the data, but in the future trend data from these sensors will become available, and simply detecting an anomaly will raise attention on potential problems.
Speaking of which, I am currently looking for data on the market for “quantified self”. How many people have smart wristbands? how many have fitness or health apps? Where can I find key data points?