I use the Overcast app for podcasts on my iPhone and iPad. It’s really good – straightforward with some useful features like keeping synced between devices and being able to control the speed of playback.
Today it asked me if I wanted to “go anonymous”.
So simple. So much simpler and less queasy an experience to be able to opt out completely of having my data tracked than the post-Cambridge Analytica, pre-GDPR emails and terms and conditions alerts from apps and online services elsewhere. While they are all getting you to click more user agreements you might have a 20% better chance of understanding or even seeing than the old ones, all in the hope of evading a fine or further market cap slips – this approach is so refreshing.
“In or out?,” It says. “We don’t really have to know your date of birth and closest friends and family in order to provide you with an acceptable podcast app.”
Last month I saw a warning from the near future for brands. I was at something called a Cryptoparty, one of hundreds happening every month around the world where activists teach ordinary people how to lock down their personal data online and avoid the perceived twin evils of Big Brother and big brands.
A nice man called Chris quickly taught me how to encrypt my email, web browsing and instant messaging. In 2008 the founder of Facebook predicted that the amount of information people shared online would double every year. Zuckerberg’s Law as it was inevitably named, was part of the spirit of openness and increasing transparency that had been sweeping through the web and our personal lives since the first glimmerings of social media as mass media took hold in the early Noughties.
That wave may now be breaking with some violence on the rocks of the Snowden revelations of mass surveillance by the US and its allies, along with the clumsy efforts of governments and corporations to take advantage of the big data bonanza to peer into the lives of citizens and consumers.
Marketers have been lazy and clumsy in their use of customer data to date. Even floating the idea for this article met with indifference and denial from my peers – consumers couldn’t give a fig about privacy, is the gist of some individuals’ feelings on the matter.
Things move fast on the web, however, and soon enough Martin Sorrell was telling Ad Week Europe that the Snowden scandal was going to hit brands harder than they thought and that “people are underestimating its significance among consumers.”
At the Cryptoparty, I learned that there are a mass of apps and services you can use securely, but as soon as I try them a big downside becomes clear. They are slow, clunky and lack the features of free services like those from Google, Microsoft and Apple, for instance.
I point this out to Chris. “People think my machine is broken when try it,” he admits cheerfully, “But it’s just very secure.”[…]
Last word to Sir Martin: “We want to be more respectful of privacy and also want to monetise our audiences our way. Being more focused on privacy is not bad for business, it can be good.”
We may not reach a stage where everyone cares about online privacy enough to download a Tor browser or a VPN like Cloak to their smartphone, but the number of people who do is likely to grow, even become a significant minority.
This is a lovely image was created by the Visual Insights team at Twitter from billions of geo-tagged tweets posted since 2009. Look closely and you can pick out the roads between cities – even a little bright spot that is Brighton (directly south of London).
A term of art has emerged to describe the digital trail that people leave in their wake: “data exhaust”. it refers to data that is shed as a byproduct of people’s actions and movements in the world. For the Internet, it describes users’ online interactions: where they click, how long they look at apage, where the mouse-cursor hovers, what they type, and more. Many companies design their systems so that they can harvest data exhaust and recycle it, to improve an existing service or to develop new ones. Google is the undisputed leader.
As datafication continues, our data exhaust trails get larger: cameras and other sensors, carried by people and installed in .
Cisco’s Chief Futurist says shops’ CCTV will become the equivalent of web analytics to examine how shoppers are making their choices and allowing shops to optimise their layouts and even their offers in realtime…
As video pixel counts increase, retailers will use video surveillance to hone in on shoppers with new levels of precision, determining demographic traits like, age, sex, and more. In-store activities can also be monitored with video, including display effectiveness, customer traffic patterns, and aisle dwell time. All of this data can be assessed in real time to adjust store operations dynamically. For example, the number of open registers could be increased based on an the number of shoppers in the store; heat maps will show which aisles attract the most traffic; and object detection can figure out which items shoppers are interacting with most.
This trend is at once exciting from a business and data strategy point of view and concerning from a personal point of view. How can we manage our web shadows when we aren’t even sure what data we are leaving behind us?
“Big data” as a term reminds me of “social media” a few years ago. It is in danger – through mis-use and over-use – of losing its currency before many people fully understand its significance. And it is very, very significant indeed.
One of the problems with the term “big data” is that it is doing too many jobs. Cukier and Mayer-Schonberger offer us a provisional term for the revolution in data that we are living through:
There’s no good term to describe what’s taking place now, but one that helps frame the changes is datafication, a concept that we introduce in Chapter Five. It refers to taking information about all things under the sun—including ones we never used to think of as information at all, such as a person’s location, the vibrations of an engine, or the stress on a bridge—and transforming it into a data format to make it quantified.
Awkward as it is, “datafication” works for me as a description (possibly simply because it isn’t “big data”).
And the definition of big data? Try these:
There is no rigorous definition of big data. Initially the idea was that the volume of information had grown so large that the quantity being examined no longer fit into the memory that computers use for processing, so engineers needed to revamp the tools they used for analyzing it all. That is the origin of new processing technologies like Google’s MapReduce and its open-source equivalent, Hadoop, which came out of Yahoo. These let one manage far larger quantities of data than before, and the data—importantly—need not be placed in tidy rows or classic database tables. Other data-crunching technologies that dispense with the rigid hierarchies and homogeneity of yore are also on the horizon.
big data refers to things one can do at a large scale that cannot be done at a smaller one, to extract new insights or create new forms of value, in ways that change markets, organizations, the relationship between citizens and governments, and more.
Before you get too cynical, before your cortex starts rejecting any conversation, content or plan that includes “big data”, I urge you to read this book. It’s a great primer on the issues and opportunities that the era of big data presents us with.
It also quickly introduces some key concepts that are incredibly powerful – about the messiness of data, the switch from causes to correlation and other ideas. It has my brain fizzing in the same way that The Origin of Wealth and Linked did a few years ago about networks and complexity.
Researchers working for the Pakistani government developed an early epidemic detection system for their region that looked for telltale signs of a serious outbreak in data gathered by government employees searching for dengue larvae and confirmed cases reported from hospitals. If the system’s algorithms spotted an impending outbreak, government employees would then go to the region to clear mosquito breeding grounds and kill larvae. “Getting early epidemic predictions this year helped us to identify outbreaks early,” says Umar Saif, a computer scientist at the Lahore University of Management Sciences, and a recipient of MIT Technology Review’s Innovators Under 35 award in 2011.
When we think about “mobility” and its potential in business and society, we shouldn’t limit ourselves to the desktop and app paradigm. (more…)
“A database of intentions” is how John Battelle described Google. It is a thrilling concept, at times unsettling, that you can see into the searching soul of the connected populace by seeing the words they use t find things.
Google Trends is one of those miraculous tools of the web that has quickly become commonplace. With a prophylactic time-lapse to keep its powerful advantage of insight, Google lets us see what people were search for by year and by region.
The other day I came across the Google Ngrams Viewer for the first time. This gives a slightly longer trends view in language, taking all the books since 1800 as its data set (actually up to 2008, I think).
Networks are an abiding obsession for me. So I love this…
This data visualisation video illustrates how Amazon applies the power of networks to selling more books (and everything else) – by tapping into the networks of purchases of books to offer more. (more…)
“Do you do any work on how annoying you are?” – Peter Day to an ad re-targeter…
In Business, the podcast by the BBC’s Peter Day, is something I have enjoyed for years. Every now and again he does a programme which is so exactly pertinent to things I’m working on that I listen to the whole thing with a broad grin, while a sort of Hallelujah chorus jangles about in the back of my mind while I am listening…
For now, the thought I want to share is one sparked by a comment that Peter Day made to a online advertising re-targeter: “Do you do any work on how annoying you are?”
He was talking about the irritation he might feel when an ad for something he searched for days ago followed him around for days afterward.
The response wasn’t convincing. Of course you don’t want to annoy your potential customers, said the re-targeter, and they provide tools to help you not blitz people.
I wonder how many users of the system calibrate in that way?
Re-targeting is just the latest in a long line of advertising technologies and innovations – latterly mostly in digital – which promise – and often deliver – “uplift”, greater click-throughs, sales, awareness etc. than previous methods.
There are two broad responses to an ad following you around the web. The first is “Wow, that’s cool!” The second is a raised eyebrow, a suspicious sneer, a question: “Why is that happening? How do they know who I am? What elese do they know?”
Response one typically comes from, er, people in digital advertising. The second, in my experience, comes from anyone else.
The steady flow of privacy nightmare stories and Facebookphobia in the media and generally in people’s consciousness is raising a – probably healthy – scepticism about online media. The more digitally literate the average user becomes the more they question what is happening to their personal data and how it is being used.
Wishful thinking on the part of online media companies and digital agencies means that not enough work is going into thinking about this growing, fundamental user need.
Apart from the inconvenience of facing up to the possibility that people might not want to play exactly the role alloted for them in the great media/marketing ecosystem, the digital advertising industry is let down by a kind of fatal optimism. They only want to look at the good news in the data and not the bad.
The thing is, that the bad news might be as useful, even more useful than the bad.
To illustrate, take a look at “success” in a typical online display campaign. A clickthrough rate of 0.2%.
Doesn’t matter what happened to the other 99.8%, i.e. most people. They simply weren’t interessted enough to look.
When it comes to re-targeted ads, social ads, etc., the clickthrough rate improves over that of typical ads. I wonder if annoyance and negative feelings to brands using these techniques does too?
Clues about what people don’t like in ads – signals of dissatisfaction, if you like – abound, but it always the positive outcome on which paid media professionals are focused. Maybe there would be more use in looking at all the data, including the damage you may be doing your own cause?