on the topic of the GUID that is you, part 1

unless you’ve been living under a boulder, someone knows more than a little something about you. ever wonder what you look like to them? you look something like this:

class model of how you look to a big-data collector

class model of how you look to a big-data collector

this is a low-fidelity logicial data model (ldm) and the depiction is in the form of a uml class model. see my previous posts here and here for more on what that all means.

sure, you know names and headlines such as… snowden, nsa, metadata, google, facebook, and the like. depending on where and how you live, you’ve purchased groceries, gasoline, retail goods, airline tickets, etc. you may also have financed the purchase of a car, house, boat, condo, etc. you went to school or funded another to do so. you’ve paid taxes, may have been sued, and have gotten married or divorced.

what’s that, you bought tickets online from royal caribbean, went on a cruise, your spouse blew your savings gambling so you got divorced? the four events are related then. i get it.

so does someone else.

this past friday, cnn published an opinion, The secret eyes watching you shop, by Edith Ramirez, chairwoman of the federal trade commission. she points to a freshly released report by the ftc titled, Data Brokers – A Call for Transparency and Accountability. i’m on my second pass and it’s quite informative.

recall that i am politically agnostic with this type of stuff; i’m reading it because it’s well drafted, non-technical, and pulls the curtains open further than i’ve seen in a long time. please don’t flame me over left vs. right, red vs. blue, R vs. D or the like.

in the nuttiest of nutshells, there exist data brokers, collectors, and aggregators. the data they collect is all about you and the amount is vast. some of the data is obtained from public record — free for the taking. some of the data is derived through clever data-mining associations. a great deal of the data is directly supplied by you. they collect it, they buy it, they share it. they combine it, correlate it, package it, and sell it. it’s a half-billion dollar industry.

the class model above was created from this report’s appendix b: illustrative list of data elements and segments. each of the yellow boxes in the model are named segments from the appendix. those boxes are called classes. in four of the classes i added the bullets from the report’s segments as class attributes. i didn’t go beyond the four as it would have exploded the picture beyond what i wanted to illustrate. the lines between the classes are called associations and they convey how you are related to all the classes – and how some of the classes are related to each other.

read the cnn piece and the ftc report, then come back here and follow this post for parts 2-4. i think you’ll find it interesting.

 

a couple points for clarity…

  1. it’s a semantic argument to most but not to me: data is only data, and remains so, until someone turns it into something actionable.
    example: in the u.s., your social security number is a repetition-allowed-permutation of 9 digits between the numbers 0 and 9. alone it means diddly in this state. write it on a brick wall, put it on the side of a bus; it’s benign. join it with the name it’s assigned to, however, and it becomes information. join it with more data, address and d.o.b. say, and it becomes actionable information.
  2. in every large-bore database you are a collection of data and you are uniquely individualized by means of an identifier. the nuance of that identifier depends on the I.T. used but speaking in the technology vernacular you are a GUID (pronounced ‘goo-id’). wikipedia is good for several things and this is one of them; if you would like to know more, go here.

 

this topic has broad impacts and it’s too long for a single post. this is part 1 of a multi-part series with the following under development:

  • part 2:  i’ll use myself as an illustration and look at my receipt from a recent grocery store visit
  • part 3:  what i look like to one of the big-data brokers, Acxiom
  • part 4:  discuss home energy management data and talk about data ownership
  • part 5:  how to curtail what’s known about you
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