“I often say I’ve seen more change in the past five years as chief marketing and communications officer of Unilever than I did in the 25 years I was in business before that, and it’s not a statement I make just for dramatic effect.” So said Keith Weed, the chief marketing and communications officer at Unilever, in an article published in Marketing Week in May 2018.
Anyone with any experience in marketing must feel that we are living through a period of rapid change that qualifies as a revolution. It is a revolution in information and communication that is transforming the world of brands (as well as the world of pretty much everything else).
Here is a question. Does anyone really feel they are on top of this: that they have cracked the code? Continue reading →
The ‘agile business’ is very much of the moment. Wherever you look you find consultants promoting it and business leaders adopting it (or at least exhorting their troops to become it). The need for agility is usually linked to the ‘rapid pace of technological change’ and that other concept du jour ‘disruption’ (the need to either avoid it or become it).
Here is a slightly disruptive thought. What if our obsession with agility is a present day manifestation of the fact that in the past, not enough organisations spent enough time thinking about the future?
Here is another one. Businesses don’t become successful by being disruptive, they become disruptive by being successful.
I wouldn’t disagree with the claim that we are living through a technological revolution, but we have had this thing called the internet for more than 20 years now. For sure, it has caused huge changes – but they have panned-out over that period of 20 years. Most of the fundamental forces that have shaped those changes have been apparent from the earliest times – if people chose to study them. The problem has been that many organisations have spent their time ignoring what has been going on and therefore find themselves now living in a state of perpetual crisis management – a condition which they have sought to dignify with the term agility.
The real problem is that the future is not what it used to be. Dealing with a changing future does not depend on agility, it depends on thinking. In writing this I am reminded of a piece I wrote almost exactly 12 years ago (The future is not what it used to be). This was a series of ten, semi-serious predictions designed to get people thinking about how the digital revolution might change things. Almost none of these have panned out exactly as predicted (or within the time-frame predicted), but I look at them quite proudly because they did a pretty good job at nailing the fundamental direction of required thinking. If, as a brand, you had spent some time thinking in this way 12 years ago, you wouldn’t currently find yourself looking down the barrel of disruption while desperately trying to do the agility dance.
Businesses that think about the future don’t need to be agile. Agility is something we have invented to put a positive spin on panic. It is time we started to Think.
Following the publication of my Stop and Think (think) piece I have been having an email conversation with Stan Magniant. Stan is Digital & Social Communications Director, Western Europe, for The Coca-Cola Company – i.e. a player. I used to work with him at Publicis when we were both bright-eyed early adopters of the whole social digital thing.
One of the issues we got into was the question of what constitutes an audience and specifically what size an audience is. As Stan put it “ to marketers, an audience is synonymous with scale: as big a group of people as I can expose to my brand, synchronously or asynchronously. I’m not clear, in your argument, whether you invite brands to explore new creative ways to gather large audiences (through paid or earned tactics? Likely both), or whether it’s all about aggregated niche audiences (a more “long tail” approach).”
This was a good question and in trying to answer it I stumbled into the analogy of joke telling. The point I was trying to make is that an audience is not defined by size, it is defined by behaviour and/or context. The reason that, to marketers, it has become synonymous with scale is a question of conventional mass-marketing economics.
If you are telling a joke, the person or people you are telling it to is an audience. This is something that implicit in the nature (behaviour) of joke telling. What is also implicit is that a joke, even if told to only one other person, is based on an element of universal relevance. A joke that only one person finds funny isn’t really a joke, even if you are telling it to the person who is meant to find it funny.
So, the audience for a joke can be one person, or millions of people. However, when it comes to deciding the optimum size of the audience, this is down to the money. If you are a stand-up comedian who has invested time and effort in developing a set, you need to get as many as people as possible into your audience. Brands are like stand-up comedians. Their material (campaigns) is time consuming and expensive to produce – which is why a brands definition of an audience has become synonymous with scale.
The joke analogy also helps explain why the concept of aggregated niche audiences doesn’t work. As a stand-up comedian you wouldn’t tell a series of five jokes, each of which would appeal to only 20 percent of the audience. The only way this would work is if you first dis-aggregated (segmented) the audience into those five groups and put each into a separate room – so that when you told the joke 100 percent of the people in each group would find it funny. An audience of aggregated niches may look like an audience in terms of size, but it doesn’t behave like an audience in terms of how you make it laugh.
As a brand you can have an audience of one, but not if you then try to create a joke that only that one person will find funny – which is essence is what most ‘mass personalisation’ strategies try to do (one reason I view these with scepticism). Mass (joke telling) is important, personalisation is important – but mass personalisation could be one of those things Seth Godin has called a ‘meatball sundae (ice cream)’.
But to be the thing we call a brand, you need to make people laugh. Which is why preserving the concept of an audience remains critical. Most social media campaigns (and many digital strategies) are the equivalent of a stand-up comedian telling their jokes to people one person at a time – i.e. a waste of time.
Anyway – a brand manager, an advertising executive and a consumer walked into a bar …
Summary: Institutionalised forms of content regulation rest on the realistic assumption that all published content can been monitored and made to conform. If you can’t establish this expectation, this form of regulation becomes instantly redundant. That is why applying the old, publication-based regulatory model to Facebook et al is a distraction that only serves to make politicians feel good about themselves while actually increasing the dangers of online hate and fake news.
In the UK the phrase of ‘leaves on the line’ is firmly established within the national conversation as an example of an unacceptable corporate excuse. It is an excuse rail operators use around this time of year to explain delays to trains. The reason it is deemed unacceptable is that leaves fall off trees every year and have done so for quite some time and thus there is a realistic expectation that this is a problem rail operators should have cracked by now.
Which brings me to another problem where an expectation is building of a corporate fix: fake news and all forms of inappropriate online content/behaviour. This has clearly become something of big issue in recent times to the extent that governments are under considerable pressure to Do Something. And the Thing they are mostly looking to Do is to turn around and tell Facebook, Google, Twitter et al that they need to Do Something. In essence what governments are looking for them to do is assume a publishers responsibility for the content that appears on their platforms. The most recent example of this is the private members bill just introduced by the UK Member of Parliament, Lucy Powell (of which more in a moment).
You can see why this is a popular approach. In the first instance, it allows government to deflect responsibility away from itself or, at the very least, create an imagined space where established regulatory approaches can continue to have relevance. It is an approach which finds favour with the traditional media, which has to operate under conventional publication responsibilities and resents the fact that these new players are eating their advertising lunch while avoiding such constraints. To an extent, it even plays to the agenda of Facebook and Google themselves, because they know that in order to attract the advertising shilling, they need to present themselves as a form of media channel, if not a conventional form of publication. Facebook and Google also know that, despite all the regulatory huffing and puffing, governments will not be able to effectively deploy most of the things they are currently threatening to do – because the space they are trying to create is a fantasy space.
The trouble is – this approach will never work. Worse than that, it is dangerous. Continue reading →
I have just received an email with the confirmed line-up for this year’s Festival of Marketing. My first reaction to this was that I can’t believe it is nearly a year since the last one. It also reminded me that the headline event last year was a battle of the professors between Byron Sharpe and Mark Ritson. Unfortunately I missed it, but for those not in the know the basis for said battle is Sharpe’s advocacy of mass communication versus Ritson’s focus on targeting and segmentation. I was also reminded of this conflict because Byron Sharpe was featured on Adam Fraser’s EchoJunction podcast a couple of weeks ago. I must confess I haven’t read Sharpe’s famous book ‘How Brands Grow’ so was hopeful that the podcast might give me a shortcut. I also availed myself of the opportunity to listen to the Prof. Ritson’s appearance on the same podcast some time previously.
On the basis of the podcasts, I would say I came out more of a Ritsonist. Of course, as Ritson himself has pointed out, it is not a question of either/or. A brand has to be able to address its entire audience and its ability to do this essentially defines it status as a brand. But an audience is not homogeneous, either in terms of attitudes or behaviours over time – which creates the requirement for targeting.
In fact, according to my theory of the future of marketing in a digital world, brands face two challenges: first is redefining the concept of an audience (and indeed a segment of such) and becoming more adept at convening these audiences rather than renting access to them; and the second is understanding how to create value from relationships with consumers as individuals (the world of distribution and the world of connection).
I guess the reason I came out on Ritson’s side was because I very much liked his scathing view of social media and assessment that most marketers have simply been jumping on a series of digital bandwagons, but also because there was something in Sharpe’s absolutist approach that I was uncomfortable with. First was his contempt for the idea of the niche and his dismissal of a niche brand as an unsuccessful brand. We are entering a time when the competitive advantages associated with being big are reducing and the ability to be small is increasing. Many big brands are facing the long-term challenge of death by a thousand niches. Second, while I am all in favour of developing a more rigourous, data driven approach to marketing I couldn’t help but get the feeling the Professor Sharpe was restricting his field of analysis according to the ability to gather or analyse the available data and disregarding evidence outside of this, not because the data was telling him to do this, but because the data was not available (or not available to measure in the way he wished).
It reminded me of a story about data, measurement techniques and assumptions that was doing the rounds back when I was studying for my degree. I studied geography, with a specialism in geomorphology (rivers, erosion and stuff). At the time, geomorphology had a problem in that we could look around and see evidence that erosion had happened, but couldn’t see it, and measure it, actually happening. (This is a bit like the issue of knowing that half of the marketing budget works, but not knowing which half). The assumption was therefore that erosion was a very slow process – water dripping on a stone – and the reason we couldn’t detect it was that we hadn’t had sufficiently sophisticated techniques or equipment to measure it.
Geomorphology had another problem in that it was, at heart, an observational science: knock-kneed bearded blokes in hobnail boots and khaki shorts wandering around with notebooks looking at things and thinking about stuff. This was deeply unfashionable back in the 60s and 70s at a time when computers were becoming established in academia. You couldn’t be a proper scientist if you didn’t run what we then thought of as large amounts of quantitative data through computer programmes.
So an attempt was made to address these two problems by wiring-up a hill slope (geomorphologists were, and probably still are, obsessed with slopes) with all the latest detection equipment, feeding all the data into a computer, pressing the button and finally nailing the causes of erosion. This slope was going to be so closely monitored that an ant couldn’t fart without us knowing about it. Who knows, perhaps farting ants would be revealed as the culprits?
Anyway, the equipment was put in place, turned on, and revealed precisely nothing. A total flatline. No erosion was taking place. And so it continued for weeks on end until after a prolonged period of heavy rain a landslide washed all the equipment away. It was as though the slope was saying “so you wanted to measure me? Well measure this sucker”.
Of course the real issue was one of false assumptions (erosion as a slow process), a restriction of the field of investigation to those areas from which data could be extracted, a desire to use new bright shiny techno things, plus a distaste for conventional, less data-driven, analysis. Geomorphologists have subsequently realised that erosion is often not a slow process, but an an infrequent, catastrophic process. The slight irony is that an old fashioned knock-kneed bloke with a certain level of experience, wandering around with a notebook, looking at stuff, noting slope angles, digging some holes to determine soil depth and composition and sticking a finger in the ground to get a sense of soil moisture levels could actually develop a much more effective functional understanding of what was going on, what had previously happened and what was likely to happen in the future that someone possessed of all the latest measurement techniques and data.
This is not to say that we should eschew evidence-based marketing, but we need to take about what assumptions we make, what evidence we seek and, crucially, not discard evidence simply because it is difficult to measure or crunch through an analysis programme.
And also, in relation to his dismissal of the niche, I have a suspicion that Sharpe’s book may come to be regarded more as a piece of historical analysis than as a guide to the future. Perhaps it should be renamed “How Bands Grew”.
However, there is a geomorphological post-script to this story which does favour Prof. Sharpe. In relation to catastrophic geomorphological events, we now know that the east coast of Australia was once devastated by a massive tsunami with waves in excess of 100 metres high. Ths was caused by the collapse of one of the islands in the Hawaiian archipelago – a phenomena know as a long run-out landslide. And the bad news is that this is going to happen again in the not too distant future, geomorphologically speaking. So Prof. Ritson in Melbourne could be in trouble, but Prof. Sharpe around the corner in Adelaide should be OK especially if he sets up house up in the Flinders Ranges.
Thanks to Jeremy Epstein (go-to for all things blockchain) for drawing my attention to this Wired interview with Emmanuel Macron. Here is a man who understands the world of the algorithm. There are three reasons you can tell this. First: he doesn’t talk about trying to lock-up access to data – he talks about making data open (with conditions attached – primarily transparency). Second: from a regulatory perspective he focuses on the importance of transparency and shows he understands the dangers of a world where responsibility is delegated to algorithms. Third: he talks about the need for social consent, and how lack thereof is both a danger to society but also to the legitimacy (and thus ability to operate) of the commercial operators in the space (I was 7 years ahead of you here Emmanuel).
As an example, he is opening access to public data on the condition that any algorithms that feed on this data are also made open. This is an issue that I belive could be absolutely critical. As I have said before, algorithms are the genes of a datafied society. In much the same way that some commercial organisations tried (and fortunately failed) to privatise pieces of our genetic code, there is a danger that our social algorithmic code could similarly be removed from the public realm. This isn’t to say that all algorithms should become public property but they should be open to public inspection. It is usage of algorithms that require regulatory focus, not usage of data.
This is a man who understands the role of government in unlocking the opportunities of AI, but also recognises the problems government has a duty to manage. It is such a shame that there are so few others (especially in the UK where the government response is child-like, facile and utterly dissmisive of the idea that government has any role to play other than to let ‘the market’ run its course whilst making token gestures of ‘getting tough‘).
Mark Zuckerberg’s appearance before Congress is a good example of the extent to which politicians and regulators have no idea, to quote The Donald, of “what on earth is going on”. It is not just them, this lack of understanding extends into the communities of thought and opinion framed by academia and journalism. This is a problem, because it means we have not yet identified the questions we need to be asking or the problems we need to be solving. If we think we are going to achieve anything by hauling Mark Zuckerberg over the coals, or telling Facebook to “act on data privacy or face regulation”, we have another think coming.
This is my attempt to provide that think.
The Google search and anonymity problem
Let’s start with Google Search. Imagine you sit down at a computer in a public library (i.e. a computer that has no data history associated with you) and type a question into Google. In this situation you are reasonably anonymous, especially if we imagine that the computer has a browser that isn’t tracking track search history. Despite this anonymity, Google can serve you up an answer that is incredibly specific to you and what it is you are looking for. Google knows almost nothing about you, yet is able to infer a very great deal about you – at least in relation to the very specific task associated with finding an answer to your question. It can do this because it (or its algorithms) ‘knows’ a very great deal about everyone or everything in relation to this specific search term.
So what? Most people sort of know this is how Google works and understand that Google uses data derived from how we all use Google, to make our individual experiences of Google better. But hidden within this seemingly benign and beneficial use of data is the same algorithmic process that could drive cyber warfare or mass surveillance. It therefore has incredibly important implications for how we think about privacy and regulation, not least because we have to find a way to outlaw the things we don’t like, while still allowing the things that we do (like search). You could call this the Google search problem or possibly the Google anonymity problem, because it demonstrates that in the world of the algorthm, anonymity has very little meaning and provides very little defence.
The Stasi problem
When you frame laws or regulations you need to start with defining what sort of problem you are trying to solve or avoid. To date the starting point for regulations on data and privacy (including the GDPR – the regulation to come) is what I call the STASI problem. The STASI was the East German Security Service and it was responsible for a mass surveillance operation that encouraged people to spy on each other and was thus able to amass detailed data files on a huge number of its citizens. The thinking behind this, and indeed the thinking applied to the usage of personal data everywhere in the age before big data, is that the only way to ‘know’ stuff about a person is to collect as much information about them as possible. The more information you have, the more complete the story and the better your understanding. At the heart of this approach is the concept that there exists in some form a data file on an individual which can be interrogated, read or owned.
The ability of a state or an organisation to compile such data files was seen as a bad thing and our approach to data regulation and privacy has therefore been based on trying to stop this from happening. This is why we have focused on things like anonymity, in the belief that a personal data file without a name attached to it becomes largely useless in terms of its impact on the individual to whom the data relates. Or we have established rights that allow us to see these data files, so that we can check that they don’t contain wrong information or give us the ability to edit, correct or withdraw information. Alternatively, regulation has sought to establish rights for us to determine how our the data in the file data is used or for us to have some sort of ownership or control over that data, wherever they may be held.
But think again about the Google search example. Our anonymity had no material bearing on what Google was able to do. It was able to infer a very great deal about us – in relation to a specific task – without actually knowing anything about us. It did this because it knew a lot about everything, which it had gained from gathering a very small amount of data from a huge number of people (i.e. everyone who had previously entered that same search term). It was analysing data laterally, not vertically. This is what I call Google anonymity, and it is a key part of Google’s privacy defence when it comes to things such as gmail. If you have a gmail account, Google ‘reads’ all your emails. If you have Google keyboard on your mobile, Google ‘knows’ everything that you enter into your mobile (including the passwords to your bank account) – but Google will say that it doesn’t really know this because algorithmic reading and knowledge is a different sort of thing. We can all swim in a sea of Google anonymity right up until the moment a data fisherman (such as a Google search query) gets us on the hook.
The reason this defence (sort of) stacks-up is that Google can only really know your bank account password is if it analyses your data vertically. The personal data file is a vertical form of data analysis. It requires that you mine downwards and digest all the data to then derive any range of conclusions about the person to whom that data corresponds. It has its limitations, as the Stasi found out, in that if you collect too much data you suffer from data overload. The bigger each file becomes the more cumbersome it is to read or digest the information that lies within it. It is a small data approach. Anyone who talks about data overload or data noise is a small data person.
Now while it might have been possible to get the Stasi to supply all the information it has on you, the idea that you place the same requirement on Google is ridiculous. If I think about all the Google services I use and the vast amount of data this generates, there is no way this data could be assembled into a single file and even if it could, it would have no meaning, because the way it would be structured has no relevance to the way in which Google uses this data. Google already has vastly more data on me than the biggest data file the STASI ever had on a single individual. But this doesn’t mean that Google actually knows anything about me as an individual. I still have a form of anonymity, but this anoymity is largely useless because it has no bearing on the outcomes that derive from the usage of my data.
The KitKat Problem
Algorithms don’t suffer from data overload, not just because of the speed at which they can process information but because they are designed to create shortcuts through the process of correlations and pattern recognition. One of the most revealing nuggets of information within Carole Cadwalladr’s expose of the Facebook / Cambridge Analytica ‘scandal’ was the fact that a data agency of the like of Cambridge Analytica working for a state intelligence service had discovered a correlation between people who self-confess to hating Israel and a tendency to like Nike trainers and KitKats. This exercise, in fact, became known as Operation KitKat. To put it another way, with an algorithm it is possible to infer something very consequential about someone (that they hate Israel) not by a detailed analysis of their data file, but by looking at consumption of chocolate bars. This is an issue I first flagged back in 2012.
I think this is possibly the most important revelation of the whole saga, because, as with the Google search example it cuts right to the heart of the issue and exposes the extent to which our current definition of the problem is so misplaced. We shouldn’t be worrying about the STASI problem, we should be worried about the KitKat problem. Operation KitKat demonstrates two of the fundamental characteristics of algorithmic analysis (or algorithmic surveillance). First, not only can you derive something quite significant about a person based on data that has nothing whatsoever to do with what it is you are looking for. Second, algorithms can tell you what to do (discover haters of Israel by looking at chocolate and trainers) without the need to understand why this works. An algorithm cannot tell you why there is a link between haters of Israel and KitKats. There may not even be reason that makes any sort of sense. Algorithms cannot explain themselves, they leave no audit trail – they are the classic black box.
The reason this is so important is that it drives a cart and horse through any form of regulation that tries to establish a link between any one piece of data and the use to which that data is then put. How could one create a piece of legislation that requires manufacturers or retailers of KitKats to anticipate (and otherwise encourage or prevent) data about their product being used to identify haters of Israel? It also scuppers the idea that any form protection can be provided through the act of data ownership. You cannot make the consumption of a chocolate bar or the wearing of trainers a private act, the data on which is ‘owned’ by the people concerned.
KitKats and trainers bring us neatly to the Internet of Things. Up until now we have been able to assume that most data is created by, or about, people. This is about to change as the amount of data produced by people is dwarfed by the amount of data produced by things. How do we establish rules about data produced by things especially when it is data about other things? If your fridge is talking to your lighting about your heating thermostat, who owns that conversation? There is a form of Facebook emerging for objects and it is going to be much bigger than the Facebook for people.
Within this world the concept of personal data as a discrete category will melt away and instead we will see the emergence of vaste new swathes of data, most of which is entirely unregulatable or even unownable.
The digital caste problem
A recent blog post by Doc Searls has made the point that what Facebook has been doing is simply the tip of an iceberg, in that all online publishers or any owners of digital platforms are doing the same thing to create targeted digital advertising opportunities. However, targeted digital advertising is itself the tip of a much bigger iceberg. One of Edward Snowden’s Wikileaks exposures concerned something known as Operation Prism. This was (probably still is) a programme run by the NSA in the US that involves the abilty to hoover-up huge swathes of data from all of the world’s biggest internet companies. Snowden also revealed that the UK’s GCHQ is copying huge chunks of the internet by accessing the data cables that carry internet traffic. This expropriation of data is essentially the same as Cambridge Analytica’s usage of the ‘breach’ of Facebook data, except on a vastly greater scale. Cambridge Analytica used their slice of Facebook to create a targeting algorithm to analyse political behaviour or intentions, whereas GCHQ or the NSA can use their slice of the internet to create algorithms that analyse the behaviour or intentions of all of us about pretty much anything. Apparently GCHQ only holds the data it copies for a maximum of 30 days, but once you have built your algorithms and are engaged in a process of real-time sifting, the data that you used to build the agorithm in the first place, or the data that you then sift through it, is of no real value anymore. Retention of data is only an issue if you are still thinking about personal data files and the STASI problem.
This is all quite concerning on a number of levels, but when it comes to thinking about data regulation it highlights the fact that, provided we wish to maintain the idea that we live in a democracy where governments can’t operate above the law, any form of regulation you might decide to apply to Facebook and any current or future Cambridge Analyticas also has to apply to GCHQ and the NSA. The NSA deserves to be put in front of Congress just as much as Mark Zuckerberg.
Furthermore, it highlights the extent to which this is so much bigger than digital advertising. We are moving towards a society structured along lines defined by a form of digital caste system. We will all be assigned membership of a digital caste. This won’t be fixed but will be related to specific tasks in the same way that Google search’s understanding of us is related to the specific task of answering a particular search query. These tasks could be as varied as providing us with search results, to deciding whether to loan us money, or whether we are a potential terrorist. For some things we may be desirable digital Brahmins, for others we may be digital untouchables and it will be algorithms that will determine our status. And the data the algorithms use to do this could come from KitKats and fridges – not through any detailed analysis of our personal data files. In this world the reality of our lives becomes little more than personal opinion: we are what the algorithm says we are, and the algorithm can’t or won’t tell us why it thinks that. In a strange way, creating a big personal data file and making this available is the only way to provide protection in this world so that we can ‘prove’ our identity (cue reference to a Blockchain solution which I could devise if I knew more about Blockchains), rather than have an algorithmic identity (or caste) assigned to us. Or to put it another way, the problem we are seeking to avoid could actually be a solution to the real problem we need to solve.
The digital caste problem is the one we really need to be focused on.
So – the challenge is how do we prevent or manage the emergence of a digital caste system. And how do we do this in a way which still allows Google search to operate, doesn’t require that we make consumption of chocolate bars a private act or regulate conversations between household objects (and all the things on the Internet of Things) and can apply both to the operations of Facebook and the NSA. I don’t see any evidence thus far the the great and the good have any clue that this is what they need to be thinking about. If there is any clue as to the direction of travel it is that the focus needs to be on the algorithms, not the data they feed on.
We live in a world of a rising tide of data, and trying to control the tides is a futile exercise, as Canute the Great demonstrated in the 11th century. The only difference between then and now is that Canute understood this, and his exercise in placing his seat by the ocean was designed to demostrate the limits of kingly power. The GDPR is currently dragging its regulatory throne to the waters edge anticipating an entirely different outcome.
For the last year Carole Cadwalladr at the Observer has been doing a very important job exposing the activities of Cambridge Analytica and its role in creating targeted political advertising using data sourced, primarily, from Facebook. It is only now, with the latest revelations published in this week’s Observer, that her work is starting to gain political traction.
This is an exercise in shining a light where a light needs to be shone. The problem however is in illuminating something that is actually illegal. Currently the focus is on the way in which CA obtained the data it then used to create its targeting algorithm and whether this happened with either the consent or knowledge of Facebook or the individuals concerned. But this misses the real issue. The problem with algorithms is not the data that they feed on. The problem is that an algorithm, by its very nature, drives a horse and cart through all of the regulatory frameworks we have in place, mostly because these regulations have data as their starting point. This is one of the reasons why the new set of European data regulations – the GDPR – which come in to force in a couple of months, are unlikely to be much use in preventing US billionaires from influencing elections.
If we look at what CA appear to have been doing, laying aside the data acquisition issue, it is hard to see what is actually illegal. Facebook has been criticised for not being sufficiently rigourous in how it policed the usage of its data but, I would speculate, the reason for this is that CA was not doing anything particularly different from what Facebook itself does with its own algorithms within the privacy of its own algorithmic workshops. The only difference is that Facebook does this to target brand messages (because that is where the money is), whereas CA does it to target political messages. Since the output of the CA activity was still Facebook ads (and thus Facebook revenue), from Facebook’s perspective their work appeared to be little more that a form of outsourcing and thus didn’t initially set-off any major alarm bells.
This is the problem if you make ownership or control of the data the issue – it makes it very difficult to discriminate between what we have come to accept as ‘normal’ brand activities and cyber warfare. Data is data: the issue is not who owns it, it is what you do with it.
We are creating a datafied society, whether we like it or not. Data is becoming ubiquitous and seeking to control data will soon be recognised as a futile exercise. Algorithms are the genes of a datafied society: the billons of pieces of code that at one level all have their specific, isolated, purpose but which, collectively, can come to shape the operation of the entire organism (that organism being society itself). Currently, the only people with the resources or incentive to write society’s algorithmic code are large corporations or very wealthy individuals and they are doing this, to a large extent, outside of the view, scope or interest of either governments or the public. This is the problem. The regulatory starting point therefore needs to be the algorithms, not the data, and creating both transparency and control over their ownership and purpose.
Carol’s work is so important because it brings this activity into view. We should not be distracted or deterred by the desire to detect illegality because this ultimately plays to the agenda of the billionaires. What is happening is very dangerous and that danger cannot be contained by the current legal cage, but it can be constrained by transparency.
One of my big ‘things’ about the social digital revolution is the fact that it is changing the nature of trust, shifting it from institutions into (often community-based) transparent processes. This has enormous implications, given the importance of trust in creating both brands and societies. If you look at platforms such as Wikipedia, Ebay, Uber, Trip Advisor, Amazon, Airbnb and even Google (search) itself their operation and success all have roots in forms of community-based processes.
Therefore, when I first came across Blockchain a couple of years ago I realised it had the potential to change the world – simply because Blockchain is all about process-based trust. In fact it has been labelled a trust engine. I therefore knew it was going to be important, before I knew how it was going to be important.
In the intervening time I haven’t been able to delve sufficiently deeply into the technology (mostly because I don’t really understand it) to get a handle on how Blockchain is going to change the World. Neither, I suspect, had many others. However, this is now changing, and smart people are starting to work-out the Blockchain applications.
Which brings me to Jeremy Epstein. My faith in the significance of Blockchain was re-inforced when I heard that Jeremy had moved from Sprinklr, one of my favourite marketing technology platfroms, to set up Never Stop Marketing – a consultancy focused on helping busnesses understand Blockchain. Jeremy is someone who has always been at the leading edge of digital change and who ‘gets it’ in terms of understanding its core principles. As a rule, if jeremy is on to it, it is worth getting on to. He has just released an e-book “The CMO Primer for the Blockchain World“. I have read this once and need to read it at least two more times. It contains some reassuring endorsements of the Blockchain concept from some leading marketers, but the most important bit is the section in the middle, where Jeremy starts to map-out potential practical applications of Blockchains in marketing, backed up by many linked examples.
I would thoroughly recommend this e-book as mandatory summer reading. I am going to use it as my staring point for, finally, getting my head around Blockchain.
This is a post I have been meaning to write for at least 18 months. When first conceived it was in part a prediction. Recent events have conspired to make that prediction a reality, which has encouraged me to get it out there. It is post about the three Ds of modern political communication: Deception, Deflection and Disruption.
It all started with Deception. Many people have accused UK Prime Minister Tony Blair of being a liar. In truth, he was far too clever to deserve this label. Calling Tony Blair a liar is a bit like calling a successful poker player a liar. What Tony Blair and a successful poker player have in common is that the practice of deceit is fundamental to their success. Indeed the whole New Labour project was built upon deception. There was, of course, the grand deception designed to create support or justification for the Iraq war but at a more prosaic level there was the deception that New Labour was a party that was going to deliver on any of its promises, when in fact all they were doing was kicking the can down the road – just another variant of TINA (There Is No Alternative) politics. Labour confused being a party of opposition with being a party in opposition, a problem which exists to this day – but that is another story.
The Conservative-lead government of David Cameron learned a lot from New Labour. Continue reading →