on the topic of LinkedIn and data mining

last thursday forbes published the following article: LinkedIn’s Mountain Of Data Is Now A $1,200 Sales Tool. linkedin’s usage of who you know is simply an extension of who you are (see part one of my earlier series on data brokers) and the forbes article does a great job of explaining how linkedin is monetizing your data. it’s a worthy read.

i’m not being an alarmist here. on the contrary, i get benefit from linkedin’s free account status so i accept the quid pro quo nature of our relationship: their service for my data. others, however, may not and will scale back or leave the site altogether. i’m sure linkedin employs a handful of quants and 2 or 3 actuaries who consider the risk/reward ratio for the intrusion, be it real or perceived.

i say perceived because your data is already being put to use out there. online services such as spokeo, intelius, us search, and several others have made a business of helping their subscribers find you. data brokers, as i’ve said, take it many steps further. linkedin is using what they have (which is quite a bit) and doing the same conceptual thing. the realness percolates to the top only when you recognize it happening to you (e.g.  Russ August & Kabat).

taking what appears to be dissimilar data sets and putting them together is like compressing carbon into a diamond. we do the same conceptual thing in my industry. companies who produce, manage, and deliver electricity rely on ‘forecasted load’ to ensure the electricity is always there. load forecasts are a combination of historical power usage, weather trends, and some other inputs to produce an estimate of what’s going to happen next day, next hour, and the next 5 minutes. although the implementation can be more complicated than it often should be, the concept is easy peasy lemon squeezy; a correlation of disparate data sets.

still, things can (and will) go awry in a data-centric economy. a real example:  imagine searching for yourself online – through one of the people finders – and see, listed under ‘Related to’, the name of a casual ex from 20 years ago. worse, she/he has somehow been given your surname; that’s a surprise! now, imagine a salesperson, subscribing to a data mining tool, calls upon your family-run business using this little assumed-to-be-true-data-nugget to get their foot in the door. not good, and in the words of ricky ricardo, “you’ve got some ‘splaining to do!”

data quality is important; it’s the difference between glengarry leads and ordinary ones. good sales people know this and adjust their approach accordingly. good data services should know this also but too often employ a backhoe approach when forceps should be used. let’s hope Jeff Weiner knows it as well.

 sidebar… if you do happen to take a look at my previous series,  on the topic of the GUID that is you, there are two other articles i invite you to read that are directly related to topics i’ve raised:

don’t say i didn’t warn you.

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