In another area we have already seen what temptation can do with the availability of data is easy to obtain..IRS anyone? Ok so now we have some more algorithms added here for additional translations and interpretations. Would SAP be messing around here if there was not huge hunks of money to be made? Nope. Good video at the link below with folks like NASA, Ford and others trying to figure out what to do with their “big data” and how they don’t even know exactly what they are doing, big chance of loss of context here too.
Big Data/Analytics If Used Out of Context and Without True Values Stand To Be A Huge Discriminatory Practice Against Consumers–More Honest Data Scientists Needed to Formulate Accuracy/Value To Keep Algo Duping For Profit Out of the Game
We hear so much about de-identified data and when you have data profiles for sale, what data base does the buyer have to query it with? You should realize that the data is making more than one trip from A to B. As B takes the data and queries it with other data bases and sells it to C. Now C may take this new data base and with what they have purchased and already have in house, they just might be pretty successful at re-identifying the data with a little data scientist or data base tech type work. It happens a lot. You just don’t know how many trips around the horn it makes once it leaves Verizon. That's how this happens, see the link below, how did my data get out there…duh?
Data Floating Around the Web and You Don’t Know How It Got There? Time to License and Excise Tax Data Sellers–Identify “Flawed Data” Epidemic At The Root of the Problem
“But she didn’t know which company had collected and shared the data in the first place, so she didn’t know how to have her entry removed from the original marketing list. “
This is why we need to license and excise tax data sellers as we don’t know how many times our data or the profiles made about us are resold. New tools for matching, anyone want to toss in a FacePrint? Of course when data is flawed and full of errors, consumers are free labor for banks and companies as we are stuck doing it on our own dime as we get denied something along the line if we don’t.
Licensing and Excise Taxing Data Sellers, Facial Recognition Yet One More Tool Used To Secure & Match Data - The Epidemic, Billions in Profits for Banks and US Corporations Using Killer Algorithms to Further Erode Consumer Privacy
Ok so Verizon has figured out how to make some jillions here and now SAP wants a cut of that to make the jillions even more data rich and “saleable”. You know I hear all this talk about “brands” anymore and sure that has some significance to banks and companies, but not much for me anymore as so many products and services tend to be copycats of one another, so seen one, seen them all. Sure there are exceptions but not a lot. Sure as the article states, Verizon is committed to your privacy and they are, until it’s sold and is gone out the door and then the buyer of the data is in control. Again, what kind of value will show up here? Good question as we don’t quite know yet and without context all of this doesn’t matter anyway when folks take analytics out of context as you get junk then. All this continues as we have some very weak tech areas with government in understanding the roots of how all this happens, They think they get it but they don’t and follow the Algo Fairies for the most part.
Lack of Modeling and Algorithm Sleuths and Stalkers In Government, Part of Why We See Such “Silly” HHS and Congressional News About Financial Donations And/Or Solicitations…
I have said many times that Walgreens makes close to a billion every year just selling data but the concept of intangible profits and how they work is difficult for most to get their heads around and hopefully the ABC example above might help. Time to license and tax the data sellers. When you see all the economic reports on why manufacturing is down, this is a big part of it as it’s easy money for banks and companies.
Some of the new technology would be nice to add to the manufacturing processes and help the US become less dependent on other countries and to stop the epidemic of flipping algorithms for easy money. Here’s a good one to look at for reselling. We all know insurers love data and analytics so what are they doing here with the Visa and Master Card data? Pretty silly answer from Blue Cross saying we are monitoring to see if any of our members are buying larger clothes. They are business people and take it and query with even more data and do they sell it or use it in house? Only the shadow may know that one but they are using all kinds of stuff with working with claims today.
Insurance Companies Are Buying Up Consumer Spending Data-Time is Here to License and Tax the Data Sellers-As Insurers Sell Tons of Data, Gets Flawed Data When Data Buyers Uses Out of Context Too
From an investor side too I wonder how many of the companies and some of the mobile apps would fair with solvency if their “data selling” revenue were not there? Do they in fact create a product and/or service with enough value to stand on their own without selling data? I think the answers might be a little scary if someone really dug down into this as we have seen recently with Zynga how dependency works…got it?
So I guess our government is majorly bliss or Algo Duped beyond comprehension with models and algorithms that this stuff is going to keep bouncing around hurting consumers are the flaws and errors continue to rise, Attack of the Killer Algorithms. All the laws I have seen are useless as there’s no path to identify data sellers (which a license would do) nor what they are selling and to who, and guess what, they could be less than honest with all their privacy claims, it happens, called a breach. Below is a great video that talks about models from a former Wall Street Quant, listen to her as she’s been there done that and worked on stuff for Larry Summers but we won’t hold that against her. Companies are sitting on $1.7 trillion in cash today as reported by Bill Moyer.
Modeling for Inequality With Segmentation, Insurance Industry Uses Backwards Segmentation As Some Models Stand to Threaten Overall Democracy
If you want to really dig in, one more great video about Quants below from Emanuel Derman who was a quant for years with Goldman and now teaches at Columbia. He wrote a book and is certainly one of the “types” of intelligence that it would really behoove the government to employ, otherwise we are just sunk as corporate models and those from the banks just walk all over the government and us. These are the smart folks that create the models and the math that runs the financial world. So in the meantime we have corporate USA growing larger and mining data and the small companies and individuals stand little chance sadly as the data is sold and models are created to even query and sell more. BD
In an earlier report, The Wall Street Journal offered up one potential use scenario: "Retailers worried about 'show rooming'" -- or customers handling products that they'll later buy via the Web -- "can find out what Web sites people visit on their phones when they're in their stores." The Journal said SAP will share profits from the service with the mobile operators that provide data. The paper also reported that SAP hasn't said which mobile operators, specifically, it's working with.
The Journal notes that the growing market for data could inspire companies to collect still more data -- and thus "broaden the range of data about individuals' habits and movements that law enforcement could subpoena." It quotes a privacy specialist at the American Civil Liberties Union: "It's the collection that's the scary part, not the business use."
SAP is also clearly aware of the basic privacy issue. In its release, it makes a point of referring to the data it would gather from wireless operators as "anonymized." It's also clearly aware of the growing practice of selling consumer data -- and of the opportunity such selling presents for a middleman with a product like its Consumer Insight 365. In the release, it quotes a study by Gartner:
The financial demands of storing and managing big data will lead 30 percent of businesses to directly or indirectly monetize their information assets by trading, bartering, or outright selling them by 2016. Many enterprises are starting to appreciate the real market value that their harvested information assets have within their own industries or beyond. However, the lack of expertise in handling big data and developing information products will create an opportunity for the growth of specialist intermediaries, acting as information brokers or resellers.