My Signal, Your Noise
What happens when we throw our data into the mix?
Personal data is exactly that, personal. When we view it we are looking at an individual, a person, someone who is wrapped in circumstance and context, it’s not just a simple data point.
Analyzing survey data based on demographic segments is one thing, but analyzing the incredibly granular data that comes from self-tracking and sensors is quite another.
Take the recent discovery that one Fitbit owner made. They spotted a sudden change in the data coming from their wife's device, thinking it was a sensor error they asked the community about it.
"My wife's Fitbit is showing her heartbeat being consistently high over the last few days. 2 days ago, a somewhat normal day, she logged 10 hours in the fat burning zone, which I would think to be impossible based on her activity level. Also her calories burned do seem accurate. I would imagine if she was in the fat burning zone she would burn a ton of calories, so it’s not lining up."
Another user asked in response to the data "could she be pregnant?" and it turns out she was. This was very, very early in the pregnancy, completely out of sync with the useful healthcare rhythms and cycles.
Here is the pic of the screen that started it all! Mom and baby are doing ok. Spoke to the doctor yesterday.
A photo posted by David & Ivonne (@babyfitbit) on Feb 10, 2016 at 8:36am PST
For now I’ll put aside the data privacy concerns and algorithmic opportunities this raises and instead concentrate on the shift this brings in understanding ‘my data’, in relation to the group’s data.
This was an amazing ‘signal’ in the data, but deeply individual and specific to the person. It displayed a recognized indicator for pregnancy, but one that is also an indicator for many other things. If the data had been part of a larger aggregated group it could have been seen as noise (especially if the additional pregnancy insight was unknown). If the data had been anonymous and maybe even had gender identifiers removed, then what? Would similar occurrences have skewed the group?
So how does my data fit with your data, and should it?
For years, IT departments resisted people using their own devices on corporate networks - but not anymore. Many of us now work in organizations that encourage us to "bring your own device". The same shift is happening with data. We are beginning to hear more and more about "bring your own data". In the same way as familiarity and expertise with a device improves productivity so it can with data and analytics. Of course the trick is balancing what must be governed and 'official' with what can be added to and augmented. Context, granularity and focus are key to knowing what the signal may mean at the level you are viewing the data at. For many of us navigating the depth and breadth of the data available will be the core skill for identifying those important signals.
This is the dizzying frontier the healthcare industry is facing, working from the extremely close up and granular view of the individual to the pulled back view of an entire populous. As electronic health records (EHR) become more sophisticated and connected, the more the view of the individual changes. The cadence changes and the EHR shifts from a historical document of record to a near real-time pulse. It becomes a useful diagnostics tool that we can augment with personal data supplied by the sensors an individual wears. That’s a lot of data, that’s a lot of messy, complex and sometimes unreliable data. But that is a rich and fertile ground for finding new signals and insights about groups and individuals. These layers; from official to informal, from individual to group, from clean to dirty, from exact to fuzzy, from past to present form the data-scape we are beginning to inhabit. Choosing the right frame for the data we have access to will be the key to drawing meaning from it.
Be sure to catch Qlik at HIMSS 2016
Read about how Qlik is enabling healthcare organizations to explore their data
Image by Lenilucho [CC BY-SA 3.0 (http://creativecommons.org/licenses/by-sa/3.0)], via Wikimedia Commons
