Platt Perspective on Business and Technology

Big data 10: redefining the group demographic 3

Posted in business and convergent technologies by Timothy Platt on May 24, 2013

This is my tenth installment in a series on an emerging capability that has become surrounded by hype, even as it has emerged as a powerfully disruptive societal force: big data (see Ubiquitous Computing and Communications – everywhere all the time, postings 177 and following for Parts 1-7 and its continuation page, posting 207 and 209 for Part 8-9.)

I have been writing about the collection, organization, accumulation and sharing of data, personally identifiable consumer data included, in this series. And in Part 9 I turned to consider the role and activities of governmental Big Brothers in this. Then towards the end of that posting I brought up the issues of corporate Little Brothers, and corporate big data accumulation and use. And I noted that at least collectively, these Little Brothers are coming to be both more pervasive and more impactful than any of their perhaps more visible Big Brother counterparts. And while Big Brother does this for survival, Little Brother does this for profit.

I stated that I would look into the issues of Little Brother in this series installment, “focusing on concerns that have continuously seemed to have arisen regarding Facebook and its development and use of big data.” And I continue that narrative here with a set of crucial observations:

• Facebook offers incredible resources for individuals and groups – for all of us as we develop and connect into communities, and as we connect and organize to achieve goals. This is all very positive.
• Facebook also accumulates vast amounts of user submitted data, and with increasing capability for scanning and searching and tagging more and more types of data, more and more of what is added to the Facebook databases can be searched and individually identified and bundled into progressively more comprehensive individual profiles.
• The best working example as of this writing, for how Facebook is leveraging new technologies to expand its searchable and functionally usable database resources, comes with its very active program for scanning and individually identification-tagging people depicted in photos uploaded to their site.
• That and essentially everything else that they do with the personal data uploaded to their site became a problem when Facebook as a business decided to monetize and commoditize this bonanza of personally identifiable data, and when they started to change their privacy policies to better meet their business model needs – in too many cases doing so with opt-out options only, for their member users.
• So people submitted information into their user profiles, for example, under one set of usage and reuse terms and conditions. Facebook loosened the restrictions they agreed to honor, allowing them to use and to sell more of this information and in new ways and to a wider marketplace. And unless a user/member went into their profile and navigated their way through the menus there to the right screens and clicked the right opt-out choices, Facebook saw itself as authorized to proceed in selling access to their data.
• So I cited Facebook in Part 9 as a rapidly emerging poster child as to how to do this badly and wrong. Facebook, as of this writing, has on the order of a billion member profiles in their system and there are people everywhere, globally who know how that social networking site and business has repeatedly shot itself in the foot for this, to put their business practice decisions most charitably. But there are others and some are much more worrisome than Facebook could ever be who also play Little Brother in this. Many work essentially behind the scenes and in ways a public facing company such as Facebook never could. Many impact widely across hundreds of millions of lives.

My goal for this posting is to at least briefly touch upon a few of those Little Brothers that for the scope of their impact, are not actually so little at all. And I begin with credit reporting agencies.

Experian, Equifax, TransUnion and similar credit reporting agencies accumulate vast amounts of information on essentially everyone, as to our spending behavior and any debts that we might carry and of any sort, our history for paying off those debts, our income and savings and investments, and our financial situations and behavior in general.
• And they run all of this raw data through statistical models to predict our future financial behavior and our capacity to pay off future debt obligations, assigning overall credit scores as summary overall ratings to the profiles that they assemble on us.
• Every time we seek to make a major purchase such as buy a car, every time we seek to sign a lease or buy a home, or when we seek to acquire a new credit card, the businesses we would carry through on these transactions with all go to at least one of the major credit agencies such as the three named above, to check our creditworthiness.
• It is important to note that while this can serve as a gatekeeper in deciding whether, for example, we can get a loan at all to buy a new car, this also sets the interest rates we would be required to pay on that car loan if we can get it. This, quite arguably is justified. A buyer with a lower credit score and shakier repayment history can credibly be considered as representing a greater risk to a lender than one with a significantly higher score, simply assuming that credit scores are set on sufficient amounts of sufficiently accurate data and that the models used to categorize individuals under review are well designed and empirically validated.
• But credit reports are also used for other purposes as well. As an arguably justifiable example, individuals can get copies of their credit reports to monitor what is in them, and both to know where they stand from their own financial behavior and history, and to spot if they have become a victim of identity theft. That is a reactive and after-the-fact way of finding out, but for most of us this can be a crucial tool for protecting ourselves from further harm.
• On a more negative side, a lot of hiring companies have started using credit report findings to eliminate job candidates from consideration – in spite of the fact that empirical evidence shows this not to be a valid metric of value for the purpose of finding a good let alone a best job candidate. Credit report scores tend to go down for people out of work and the longer they are, the more their finances and their credit scores can suffer. So as far as hiring is concerned, the credit score primarily just shows that a given candidate has been out of work, and perhaps through no fault of their own and that they really need this job. And that use of credit scores has become an increasingly important revenue stream for these credit reporting agencies, and that is one of the reasons why I write of them here as Little Brother examples.

Facebook is very public and very directly connected with vast numbers of members of the community. And they have found ways to both gather and organize and to monetize and sell information about essentially everyone in their system, and as both anonymized demographic data and as personally identifiable and even profile-organized information about us. The big credit reporting agencies are also well known to the public – at least for their major lines of business. But their resources are packaged and sold for other less well known purposes too, and some of them are problematical and even directly contrary to our needs and both as individuals and as members of communities. Then there are the much less known companies that accumulate, organize and sell marketing intelligence, and both anonymized and individualized and in many cases for essentially any end-use purpose. Business to business, and more generally business to organization Little Brother accumulators and sellers of big data, are becoming a major driving force in reshaping both marketplaces and the businesses that serve them – and every group or organization that is trying to publically push an agenda.

When I wrote Big Data 7 I invoked the extremes of utopias and dystopias, and in the course of this series I have at least pointed towards both. But most of what I have been writing about fits more into the vast gray area in between. In Part 9 and this installment I have touched on both extremes while noting the Big and Little Brothers of our world, and their developing impact upon us all. I am going to continue this discussion in my next series installment, beginning with a basic question that comes out of this series and its installments up to here as a whole:

• The first ten installments of this Big Data series have delved at least briefly and selectively into where we are now. Where are we going, and what can we as individuals and as businesses, and there perhaps particularly as small businesses, do to more effectively succeed in the midst of all of this?

Meanwhile, you can find this and related postings at Ubiquitous Computing and Communications – everywhere all the time and its continuation page.


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