Firms have always had an interest maintaining the loyalty of their customers. This has also involved knowing more about the customers. In a discussion of subscription models, the Economist, refers to what may happen because of restriction on data that may emerge because of the Cambridge Analytica imbroglio. Subscription models are becoming more popular, in part because technology has made it easier to rent rather than own assets. Instead of buying software, for example, users can get access to it as a cloud-based service.
At one time, transaction-generated data (TGD) was the by product. E commerce or retailing services provided over the web was the main product. But if analysis of the TGD is used to give the company leverage in other sectors, resulting in acquisitions or entry .. .
The IDRC Asia Office was kind enough to permit publication of a part of a concept paper I wrote for them. Here is an excerpt: At present, the principal methods for understanding users or demand are quantitative research (representative-sample surveys) and qualitative research. The former is used primarily for understanding “what” questions and the latter for “why” questions. Quantitative research is very costly. Its limitations include problems of recall and different forms of bias.
Issue of discrimination coming up in big data policy review. The value of big data is in understanding the consumer. But with understanding comes the ability to discriminate. Not all discrimination is bad. But some may be.
A recent case gave hope to those who wanted the n=all collection of telephone transaction-generated data to cease. But only court that can overrule Smith v Maryland is the Supreme Court. Now a FISA court has explicitly declined to follow Judge Leon. So n=all continues. A telephone company asked the Foreign Intelligence Surveillance Court in January to stop requiring it to give records of its customers’ calls to the National Security Agency, in light of a ruling by a Federal District Court judge that the N.
The recent kerfuffle about Google flu trends showed all kinds of critics of big data come out of the woodwork. This is normal for anything new. I am sure there was an outpouring to hostility to the motor car when the first accident occurred. What I found useful was the cooling of the hype associated with big data. I have no doubt it is big and will, in fact, lead to a data economy.
Since 2006, when the majority of the world’s population became city-dwellers (can’t use the original term “citizens” because it has now lost its connection to cities), there has been a great deal of interest in understanding these engines of economic growth. Here are some findings from Spain, in the process of being replicated in Asia. The results reveal some fascinating patterns in city structure. For a start, every city undergoes a kind of respiration in which people converge into the center and then withdraw on a daily basis, almost like breathing. And this happens in all cities.
John Podesta is no stranger to privacy issues. I can remember some interactions with him in the context of the Electronic Privacy Information Center (EPIC) during the Clinton Presidency. He has now been tasked with producing a big data-privacy report in 90 days. We are undergoing a revolution in the way that information about our purchases, our conversations, our social networks, our movements, and even our physical identities are collected, stored, analyzed and used. The immense volume, diversity and potential value of data will have profound implications for privacy, the economy, and public policy.
Is their ability to generate massive transaction-generated data streams that will yield insights into human behavior. Packed with sensors and software that can, say, detect that the house is empty and turn down the heating, Nest’s connected thermostats generate plenty of data, which the firm captures. Tony Fadell, Nest’s boss, has often talked about how Nest is well-positioned to profit from “the internet of things”—a world in which all kinds of devices use a combination of software, sensors and wireless connectivity to talk to their owners and one another. Other big technology firms are also joining the battle to dominate the connected home. This month Samsung announced a new smart-home computing platform that will let people control washing machines, televisions and other devices it makes from a single app.
We think about transaction-generated data (TGD) a lot. The essence is that data generated as a by-product of some activity (and which is therefore highly accurate) can tell us more about behavior (even future behavior) than all the questionnaires in the world. Behavior associated with music, closely tied to emotion,seems like an even better candidate than reading. During the next federal election cycle, for instance, Pandora users tuning into country music acts, stand-up comedians or Christian bands might hear or see ads for Republican candidates for Congress. Others listening to hip-hop tunes, or to classical acts like the Berlin Philharmonic, might hear ads for Democrats.
My work on privacy in the 1990s greatly benefited from my teaching. My classes were like laboratories where we tested out scenarios and concepts. I (and my students) also engaged with science fiction. I still talk about the extraordinarily powerful, low-tech surveillance techniques described by Margaret Atwood in The Handmaid’s Tale. That was brought to me by a student.
I was reminded of that old chestnut about a flagman having to walk in front of early automobiles when I heard some participants talk at the workshop on big data, social good and privacy. Imagine imposing inform and consent rules on transaction-generated data (big data) belonging to large corporate entities such as mobile operators. They need the data on user mobility patterns to manage their networks; they need financial transaction data to manage their finances. All these things can be covered under broad inform and consent procedures that will be presented to customers as they sign up. What will not be possible would be to permit use by third parties for traffic management, energy management, urban planning etc, since these uses could not be conceptualized at the time of signing up customers.
When asked about emerging trends of relevance to those picking research topics at the recent CPRsouth conference, I pointed to the growing importance of the badly named “big data” or its more analytically satisfying subset of transaction generated data (TGD) or information (Thomas McManus coined TGI back in 1991; TGD is more accurate). It’s going to be big data in everything. Even the shift to MOOCs is driven by the need for TGD, according to the NYT. There are potential advantages to this shift. When students are logged on, educators can monitor their work in ways that are otherwise impossible.