Wēnaþ þā dysiġan þæt ǣlċ mann sīe blind swā hīe sind, and þæt nān mann ne mæġe ġesēon þæt hīe gesēon ne magon.
Thursday, February 21, 2019
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:
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
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)
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
Friday, February 8, 2019
Thursday, February 7, 2019
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