Category: Algorithms

Thongs on the internet: the next big internet thing

FireShot Screen Capture #259 - 'Buy Internet Thongs & G-Strings I Personalised I - CafePress UK' - www_cafepress_co_uk_+internet+underwear-panties_cat=100115Around this time last year I was asked by Savas Onemli, the editor of Digital Age in Turkey, what I thought the big thing in 2013 was going to be.  I said Big Data.  Phew!  So recently I was asking myself the question, what might be the big thing of 2014.  I think Big Data is still going to be pretty big, but I think the new thing we will be talking about (which is closely linked to Big Data) is The Internet of Things.

I have just got back from Brussels where I was taking part in a panel discussion on Big Data at the annual get together of the European Association of Communications Agencies (EACA).  One of the issues I raised was that the amount of data out there was about to explode on account of the fact that things, not just people, were now becoming connected to it.  In the Big Data context I called this ‘the kettles that spy on your life’ – for this, essentially, is what happens when we connect things to the internet: they become spies, either on your  life specifically or on life in general.

I was first introduced to this idea about 18 months ago when I saw a presentation by Andy Hobsbawn at the Social Media Influence conference.  Andy was an enthusiastic supporter of the idea and one of the possibilities he suggested was what might happen when jeans, or other items of clothing, acquire an internet identity.  As soon as he said this the thought that flashed into my mind, however, was not ‘how intriguing’, it was ‘oh, my God’.  The whole thing seemed to be a terrifying prospect, not just the ability of these things to become spies but also the multiplicity of issues that might emerge when we start to give things independent identities and personalities that interact with our own.  Does this mean we will have to start giving things rights for example?  We are having enough difficulties dealing with issues like our own rights to privacy and data protection in relation to Big Data as it is (one of the issues also discussed at the EACA event), let alone dealing with our items of clothing – underwear, data and privacy: welcome to the Internet of Thongs.

Joking aside, the fact that things will join us on the internet as producers and quite possibly consumers of data clearly re-inforces what I already believe, which is that we are not going to solve this privacy and data protection thing by looking at initial sources of the data, because otherwise we will end up giving rights to our underwear and asking its consent.  The answer has to lie in controlling how data is used, not by controlling the way in which it is sourced.

However, the reason I now know this will be the Big Thing of 2014 is that I have just heard a piece on it on the BBC’s World at One programme.  It was approached in the same way the BBC and Radio 4 always approach these things which is to damn it with frivolity.  “Goodness me, a rubbish bin connected to the internet, whatever will these (silly) people think of next” was the general tone of the piece.  “Goodness me, a computer that everyone can own, whatever will these (silly) people think of next”.  Whenever the BBC (in fact traditional mainstream media in general) doles out this sort of treatment – you can be sure they are talking about the next big thing.

 

 

Big data: turning hay into needles

Here is a quick riff on an analogy.  Small data analysis is all about looking for needles in haystacks.  Big data analysis is all about turning hay into needles (or rather turning hay into something that achieves what it is we used needles to do).

Being more specific.  Small data analysis (i.e. the only form of data analysis we have had to date) was a reductive process – like everything else in the world where the data and information channels were likewise restrictive, largely as a result of their cost of deployment.  Traditional marketing, for example, is the art of the reduction – squeezing whole brand stories into 30 second segments in order to utilise the expensive distribution channel of TV.  Academic analysis likewise – squeezing knowledge through the limited distribution vessel that is either an academic or a peer-reviewed publication.

As a result the process of data analysis was all about discarding data that was not seen to be either relevant or accurate enough, or reducing the amount of data analysed via sampling and statistical analysis.  The conventional wisdom was that if you put poor quality data into a (small) data analysis box – you got poor quality results out at the other end.  Sourcing small amounts of highly accurate and relevant data was the name of the game.  All of scientific investigation has been based on this approach.

Not so now with big data.  We are just starting to realise that a funny thing happens to data when you can get enough of it and can push it through analytical black boxes designed to handle quantity (algorithms).  At a certain point, the volume of the data transcends the accuracy of the individual component parts in terms of producing a reliable result.  It is a bit like a compass bearing (to shift analogies for a moment).  A single bearing will produce a fix on something along one dimension.  Take another bearing and you can get a fix in two dimensions, take a third and you can get a fix in all three dimensions.  However, any small inaccuracy in your measurement can produce a big inaccuracy in your ability to get a precise fix.  However, suppose you have 10,000 bearings.  Or rather can produce a grid of 10,000 bearings, or a succession of overlapping grids, each comprised of millions of bearings.  In this situation it is the density of the grid, the volume of the data and, interestingly, often the variance (or inaccuracies) within the data that is the prime determinant of your ability to get an accurate fix.

To return to haystacks, it is the hay itself which becomes important – and rather than looking for needles within it it is a bit like looking into a haystack and finding an already stitched together suit of clothes.This is why big data is such an important thing – and also why a big data approach is fundamentally different to what we can now call small data analysis.  It is also why there is now no such thing as inconsequential information (i.e. hay) – every bit of it now has a use provided you can capture it and run it through an appropriate tailoring algorithm.

Article on Big Data in Sunday Telegraph’s Business Technology supplement

FireShot Screen Capture #242 - 'Richard Stacy_ The algorithm is the most powerful tool of social control since the sword - Business Technology' - biztechreport_co_uk_2013_07_richard-stacy-the-algorithm-is-most-powerfHere is a small article on Big Data I wrote as the opening shot in the Business Technology supplement published yesterday in the Sunday Telegraph.

Big Data is certainly a big buzzword, but there are those out there who say Big Data is nothing really new.  As a rule I find these people have careers based on what we can now call small data (or perhaps that should be Small Data).  Big Data certainly is something new, and there are two reasons why it is aptly named.

First, Big Data is really big.  It is not just a bit larger than the data we had before, nor is it just lots more of small data.  Big Data is defined by the fact that it is so large, it cannot be handled by the tools or techniques conventionally associated with data analysis (one of the reasons its rubs small data people up the wrong way) and this also means we can use it to do things which were not possible when all we had was small data. Continue reading →

Big Data: gold mine or fool’s gold?

(This was published in the print edition of Digital Age in Turkey earlier this month.  It also appeared as few days later as a Digital Age blog post – if you want to read it in Turkish!)

There is a lot of buzz about the concept of Big Data.  But it is really the potential gold mine that some are suggesting?

Back in July I was at the Marketing Week Live show in London participating in an event organised by IBM.  We were looking at data and consumer relationships within fashion retailing, using high-end women’s shoes as the example.  The big issue fashion retailers face is that everyone walking into a store is a stranger.  The sales assistants know nothing about them, other than what they can deduce from their appearance and any conversation they can then strike-up.  We therefore asked ourselves the question: how might it be possible to use data from the digital environment so that potential customers were no longer strangers?  How might we be able to create a digital relationship so that when a potential consumer walks through the door the sales assistant would be able call-up this relationship history and pull this on-line contact into an off-line sales conversation?  One of the IBM analysts put it thus, “we need to be able to identify the exact moment a potential consumer starts to think about buying a new pair of shoes, identified from conversations they have with their friends in social networks and be able to then join those conversations”.

Welcome to the world of Big Data.  In the world of Big Data it is theoretically possible to know as much about your consumers as they know about themselves: to be able to anticipate their every thought and desire and be there with an appropriate product or response.  It is a world of ultimate targeting and profiling Continue reading →