We’ll put aside the great battle between the people who think “Data is” and the people who think “Data are” for another day and focus on the concept of data.

I find it useful to think about data as one of those context-specific things – it’s like my coffee table, sometimes I put my coffee mug on it, sometimes I put my feet on it, sometimes I sit on it, sometimes I stand on it. The coffee table is always the same thing, it’s just how it is being used that defines how useful it is for a given task. Back to data, let’s start at the beginning, or the morning at least, it’s 7:00 am and the alarm is going off on your phone. Is that data? To you probably not. To Google/Apple it probably is. By recording what time you wake up at every day they can more efficiently direct targeted information your way (i.e. adverts). Later to wake up than normal this morning? Here’s an ad for a takeaway breakfast on your way into work.

What about your favourite TV streaming service? Every time you watch something it keeps a note of it so it can recommend something it thinks you might like. It even keeps track of every time you pause it, or rewind it, or skip a bit, which feeds back into new programs it develops – everyone skipping the recap – we’ll stop putting recaps in, everyone rewinding a quiet scene – we’ll make the volume louder next time. Lots and lots of data points just from watching TV!

But it’s not all just about ways to recommend and sell stuff, let’s think about a trip to the GP for a routine asthma review and some of the data that might generate. You would likely have to book a slot in advance, then once you arrive the receptionist would likely mark you as present in their system. This is useful data! From this we can see how many appointments are booked at GP practices across the country and how many appointments are wasted due to people not showing up. It’s only when we can objectively measure these things that we can see if there is a problem or not.

When you are in with the GP perhaps they may review your medications, measure your lung function and take some blood samples. All of this will help the GP assess how well your asthma is maintained, but these measurements are also all useful data, which could be used to calculate how many people have asthma in an area. Perhaps it can be used to calculate if more people are developing asthma than the national average – something that might not be obvious to the GP when they are looking at individuals as part of their care.

When we are thinking about respiratory illnesses (like asthma) perhaps we will look to air quality measurements, or people’s access to green space (e.g. parks – something we are looking at in our GroundsWell project – https://www.groundswelluk.org/) or how energy efficient their homes are. Maybe we can look at the dispensing data from the local pharmacies to see if patients are picking up their prescribed medications. All of these are useful data points when viewed in the relevant context.

We sometimes refer to data collected as part of everyday services (e.g. going to the GP or having an energy performance assessment on your house) as administrative data or routinely collected data. This is in contrast to data actively collected to answer a specific question (e.g. a survey asking why people are missing GP appointments). We will explore administrative/routine data in a later blog post.

The take home from this blog post is that data may be generated and collected from almost every interaction we have. The usefulness of the data depends on what it is to be used for and that is not always obvious (or known) when the data is collected.

Data is BTW 🙂

– Dr Olly Butters, Care and Health Informatics theme.