Data mining

There were three different parts to my data-self. A small amount was composed of data that I'd volunteered myself: my address, name, contact details, and so on. The second, much larger part was data that I'd generated as I'd used a company's services or products.

But the most interesting was data in a third category: data that had been created from other data that had been collected about me - from models and segmentations, based on probabilities and likelihoods.

About 1,500 of those pages were this kind of educated guesswork, all of it from companies I had never heard of before.

It's easy to find data on this scale a little alarming, but most of it I found more silly than sinister:

• The age of my boiler had been predicted
• My likelihood to be interested in gardening was 23.3%
• My interest in prize draws and competitions was 11%
• My "animal/nature awareness level" was low
• My consumer technology audience segmentation was described as (among other things) "young and struggling".
• My household was found to have no "regular interest in book reading" (I have written a book)

At one moment I was a go-getter, an idea-seeker. Then I was a love aspirer, a disengaged worker, part of a group called budgeted stability or, simply, downhearted.
Something I did triggered a "Netmums - women trying to conceive" event.

If this was a reflection of myself, I didn't recognise it.
https://www.bbc.com/news/technology-48434175