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Exploring the science and magic of Identity and Access Management
Saturday, May 25, 2024

Big Data Analytics – Subtle Patterns and Relationships

Author: Mark Dixon
Thursday, March 14, 2013
8:55 am


A recent Wall Street Journal article, “When ‘Likes’ Can Shed Light,” stated:

Patterns of “Likes” posted by people on Facebook can unintentionally expose their political and religious views, drug use, divorce and sexual orientation …

My first response was, “Duh, of course!”  But I think the implications are much deeper.  A wide range of disparate conditions can be linked together to imply seemingly distant results.  For example:

“Likes” for Austin, Texas; “Big Momma” movies; and the statement “Relationships Should Be Between Two People Not the Whole Universe” were among a set of 10 choices that, combined, predicted drug use. 

“Likes” for swimming, chocolate-chip cookie-dough ice cream and “Sliding On Floors with Your Socks On” were part of a pattern predicting that a person didn’t use drugs.

What in the world do all those things have to do with each other?
The article suggests that this type of analysis …
… arises from an emerging discipline in which experts sift through extremely large digital data sets, such as collections of web searches or Twitter messages, for subtle patterns and relationships.
“Subtle Patterns and Relationships” is the key phrase.  In our highly connected world, we all leave digital breadcrumbs scattered about that are subject to this type of analysis.  Sophisticated data analytics will progressively be able to pinpoint behavior patterns and even predict behavior, based on relationships between seemingly disparate and unrelated bits of data.
Will this be used to do a better job of targeting advertising?  If so, that might be beneficial to vendors and consumers alike.
But could it be used for nefarious purposes – even harassment, stalking, exploitation or discrimination?  You bet.  We had best be careful out there.
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The Value of Data and Meaningful Analytics

Author: Mark Dixon
Tuesday, January 19, 2010
11:45 am

Semantics: “The study of meaning”

markmontgomery This morning I read a thought-provoking article by my associate Mark Montgomery entitled “Systemic failures, by design.” The article proposes that in many high-profile cases, catastrophes could have been averted or moderated if appropriate semantic-based analysis and action had been taken, based on data that existed prior to the event:

Over the course of the past dozen years the U.S. has experienced a series of dangerous and costly systemic failures throughout our security and regulatory framework. The unfettered bubble in technology, missed opportunities to prevent 9/11—leading to two ongoing wars, the tragic response to Katrina, the largest financial crisis in history, the Fort Hood massacre, and the ‘underwear bomber’ incident on Christmas Day all share one commonality.

In each of these cases, data had been collected by U.S. government agencies that contained a high probability of either entirely preventing or substantially mitigating each event, if only the information had been recognized and acted upon within the window of time allowed by circumstances. In case after case, repeated warnings by recognized experts, sourced internally and externally, were ignored or suppressed.

In the past few months, I blogged a couple of times about the use of data analytics with Digital Identity:

In his address at Digital ID World, Jeff Jonas’ discussion about using data analytics to discover space-time-travel characteristics of individuals was both challenging and disturbing. He proposed that advanced analytic techniques could be effectively used to pinpoint the identities of people of interest based on patterns of use of mobile phones and other data sources readily available today.

While there is certainly danger of loss of freedom to ordinary citizens due to government surveillance, it is apparent that a much better job of identifying and acting upon potential threats and the identities of people involved is quite possible if existing data, lawfully acquired, is more effectively analyzed in meaningful (aka semantic) ways.

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