Why is Data So Unreliable?

This post is about the information that is used by a supercomputer we all carry around with us (no, dummy, not your mobile phone. Not quite yet). It is about the idea that we all suffer from innumerable distortions and agendas. Even those who claim to have no agenda…well, that is the agenda. To pretend they are above it or haven’t got one is disingenuous and indicates a worrying lack of self-awareness.

This supercomputer is an organic machine, part of a larger organism with a wide range of sensors for input, it requires very careful maintenance, is easily damaged and easily corrupted, benefits from being made to regularly solve complex problems, has seemingly limitless storage capacity and is carried around on your shoulders.

In our rush to worship at the altar of AI, data, quantum computers, and machine learning we step neatly over the weirdness and utter unpredictability that this supercomputer (for ease of typing it shall henceforth be known as the brain) brings.

optical illusion
Two straight lines, bent by the brain.

Sure, you can reduce all the external inputs into a measurable thing. The number of photons that hit the retina, the loss caused by an aged lens, the sensitivity of our fingers, sense of balance, the speed at which we solve problems and so on. Despite possessing a brain in all its judgemental and unpredictable glory we seem desperate to quantify and measure everything possible. For with the surety that comes from turning every conceivable bit of input into a number, then surely it must be within our ken to calculate the output? And if we don’t get it right then we can re-examine the computational processing algorithms and refine them until we come to as close an output that the programmer(s) expect. You see, there will be some parameters set for an acceptable/realistic/likely outcome somewhere in the brief, and usually, the aim is that the output matches the expectation.

The brain starts to gallop ahead, and I suppose this is what intrigues the scientists trying to create AI when the capacity for the random social variables and the filters they create comes in. The ability of the brain to make the weirdest associations from two apparently random bits of data always astonishes me.

For example: 30 years ago I shared a flat with Oliver Reed’s nephew, who apparently strove to exceed or at least replicate the lifestyle of his uncle. I was persuaded to take a tab of acid (LSD), which I tore in half because it scared me but, hey, peer pressure. This had a startling effect on me and I had to sit alone as it felt as if snooker balls of thoughts were cannoning into one another and going off at funny angles. To this day, if I see a snooker game I am reminded of that moment. I imagine it will be a very very long time before there will be a computer that can make those sort of weird cognitive leaps.

Data can be as much social and experiential as pure numbers fed into STATA, R or SPSS and manipulated in various approved ways. The data coming from the brain’s sensors is reliably distorted through one or several social lenses. Are you rich, poor, foreign, insecure, angry, a victim of something, in a wheelchair, aspirational, impaired, with a neurological condition (I have MS – have had for 26y)? Perhaps you are clinging to the notion that you are utterly impartial and free from an agenda and thus right? That is a powerful filter, often producing feelings of self-righteous indignation that can’t always be adequately expressed in 280 characters.

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It doesn’t stop this fellow from trying though.

When you are designing research, analysing research, presenting research, having research presented to you then try to remember that your brain and the recipient’s brain have different filters. They may seem externally similar, but at some point, that same information will hit a unique filter and the gap between intent and understanding soon becomes apparent.

The human desire to avoid cognitive dissonance is strong and mismanaged leads people to do terrible things to try and ‘fix’ it. Thankfully, we have a great ability to bend data to fit our pre-conceived notions of what feels right or to fill voids with made-up data. We ALL do it. I believe that the most we can do is to open up the analysis to others who manifestly do not share a similar agenda. As independent as possible and are trained to look for inconsistency, in the accrual of data or the motives of those who have handled it before you see/hear it.

So-called facts in newspapers are a good place to start asking why and how. In a commercial environment, people bandy around poor data and try to cover it with the force of personality of seniority. BBC Radio 4 has an excellent series (available for download) called Thought Cages that deals with the vagaries of the human brain in an amusing and engaging way.

We all lie and deceive all the time. It is in our nature. Sometimes you need a different variety of deceiver to look into your world and help you identify the deceits. I can help you, so contact me via LinkedIn.

 

What Are You Really Worth?

I do mean really, can you really put a value on your being, your presence on this planet?  For starters, define value. I imagine that it is quite a different figure to that which you are paid, the increase you are going to carefully negotiate for, cometh the pay/performance review. What have you added to your company’s bottom line? Really, how can you express your actual hours of toil to yourself, your family, the shareholders and so on?

Instead, try this: what would a stranger pay for you?  This throws up all sorts of quite deep questions. Perhaps I mean the value of your life, a binary live/die scenario. What value does your life hold to a stranger? Why should they invest their money in your preservation? What is the bottom line for a stranger if they do not have an emotional investment in your continued existence? Perhaps that stranger is just a middle-man and your worth to them can only be expressed in what another third-party will pay for you, regardless of what the final owner of you does with/to you. The more I write the more it sounds like a people trafficking scenario being described in an article on a site devoted to understanding data.

You are worth nothing. Your personal data is worth everything. You are not the customer, you are the product.

Nevertheless, my friend Nick brought the following to my attention:

Future value of data

Image credit: PwC (a publication of some sort) 2019

It turns out that ‘experts’ have predicted the estimated (now there is a get out of jail free word when used in stats/studies) value, not of life per se, but more of a person’s worth. A worth that only some people/organisations will value.

Another shock: Headline grabbing bar charts with bold colours and zero bloody context around them. I get told off occasionally for worrying about trivia like this. My reply is that it isn’t trivia, it is e v e r y t h i n g.  This is clearly a graphic designed to show thought leadership of some description and therefore imbue the reader with a warm feeling that they are in the hands of ‘experts’ who ‘get’ this kind of stuff and that said experts are the ones to choose to help shape your organisational vision for the next millennia. You’ll be at the bleeding edge of thought and stand to leapfrog all your competitors in a trick where you simultaneously disappear in a puff of smoke and hit the ground running towards a new and lucrative market enjoying an unassailable lead. If, of course, you employ the genii at said group of thought leaders proffering such a compelling image of the future.

Wouldn’t it be interesting to know how these figures were arrived at? Why is a US citizen worth three times that of their European cousins? What was measured, what was controlled for, what was the working hypothesis (apart from baffle the punters with smoke, mirrors and a pretty chart?), when was the analysis conducted, what was excluded and why, how was it analysed, can we have the raw data ourselves please, what data, how is value computed, what markets will pay that, will some pay more or less? And so on…

Here is a little test you can run yourself. Call up a software/hardware firm or management consultancy and see if in a ten-minute chat they can refrain from using the words: Big Data, Blockchain (a new one getting traction), AI, Algorithm, paradigm (falling out of favour these days, I guess the era of New Paradigms has come and gone) or cloud. My guess is at least 4 out of six will crop up. Just saying.

This begs the question, how can we harness this apparent worth and charge for it? Perhaps there could be some charitable models developed around this?

Data Done Properly

In contrast to all the mangled, misinterpreted, unpublished and skewed data there are beacons of hope. This time it emanates from none other than Local Government, not known for leadership in these areas. So pleasing to see.

I saw this LinkedIn post by Steven Johnson and immediately commented. It is such a delight to see data being used properly, from gathering to analysis.

 

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That is all. Have a wonderful Tuesday.

 

PS: No conflict of interest as I have never even met Steven!

Red Flags & Sacred Cows

Here follows a cautionary tale. I name the culprit, not because I have an axe to grind or it is particularly unique, but it suits the example being made.

To repeat other posts on here: when someone starts quoting facts and figures at you and citing studies, it is entirely reasonable – and very sensible – to ask some probing questions. The figures are usually being used to sell you something. Be that an idea, credibility, services that the provider of the figures can also come and fix, at a price, naturally, or just in support of their existing position on a topic.

This entire topic is made much more challenging when very emotive topics are being commented on. Race, Gender, Diversity and Inclusion are today’s Sacred Cows. These topics always seem to make many people uncomfortable, whilst trying to appear as if they are just fine with it. They often deal with this by ensuring that they say nothing, thereby keeping their head below the parapet. An unintended consequence is that lack of enquiry means that statements with regard to the Sacred Cow go unchallenged.

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Twenty years ago there were few, if any, consultancies that were offering to help companies address issues that can arise as a result of various forms of discrimination. Many seem to think that because they are positioning themselves as experts in the field it puts them beyond reasonable criticism and examination. Please can someone help me understand why that elevates them beyond reasonable scrutiny and criticism?

A big problem with Sacred Cow topics is that any criticism of anything to do with them – in this case, the use/misuse of data – is tantamount to trying to undermine their very raison d’etre. It isn’t at all, it is all about the data. Data doesn’t care about any of these issues. To conflate the two seems as if it is a tactic to draw one’s eye away from the data and try and shame you into ceasing with the questions.

Where you should have a problem is when data is used to misrepresent issues. Whether intentionally or unintentionally, the mishandling of data can make problems appear very different from what they actually are. A simple example is in the analysis of raw data. If certain variables are not measured during collection and then controlled for during the analysis, or sometimes data collected in a specific area produces results that are then remarked upon and treated as a general finding with to qualifications added to them.

Back to the Red Flags though. The fact that it is a sensitive topic should prevent you from asking about the provenance of the data. If someone clasps their hand to their mouth and asks how could you possibly question a respected pillar of the industry, sometimes an author etc, then remind them about speaking truth to power.

Recently, I saw a post on LinkedIn from one of the founders of Pearn Kandola LLP Which read:

“A third (32%) of people who have witnessed racism at work take no action, and a shocking two-fifths (39%) of those said that this was because they feared the consequences of doing so*. If our workplaces are to become genuine places of safety, it’s vital that the government acts quickly to curb the use of NDAs to hide instances of harassment, whether it be racist, sexist or otherwise. RacismAtWork UnconsciousBias

*According to our own research at Pearn Kandola LLP

All well and good on the face of it. Nothing wrong with citing your own research, providing you can back it up. I was interested to learn more, so I asked if the research was published, what the sample size was, where and when it was collected etc? There has been no reply. Judging by many of the comments this has been accepted without criticism or interrogation by many, a worrying indication of a lack of critical thinking. Another area of concern when data is being reported and should also raise a little red flag in your mind is the use of words like shocking. I can only imagine this is to try and increase click through. It detracts from data and sounds more like a Daily Express ‘weather armageddon’ type headline.

Sacred Cow

If the data is robust they ought to be delighted to publish it and open it up to examination. After all, if it is robust enough to underpin public claims that are made then there is no reason why it ought not to be open to examination by a third party.

To question data means that you are thinking. Whatever the topic, there should be no Sacred Cows, especially not the data.

AI, ML & DL – A Bluffer’s Guide

AI, ML and DL are our attempts to get machines to think and learn in the way that we can. Get that right and you’ll take the power of the human multiplied a million-fold, to have a breathtakingly capable machine. Probably our new robot overlords but we’ll cover that later. Whilst I do not have any issue with these developments, and do believe it is both attainable and useful, we are not there yet. To date we have these incredibly fast calculators that are essentially linear and binary. These are our modern computers. There are boffins in labs developing non-linear and non-binary counting machines but they are not here yet. This means that we are left with the brute force approach to problem solving. Run the right algorithm (at least to start it is provided by a   human) and you can get the giant calculator to supply an answer, often the correct one but f not then it can learn from its mistakes, rewrite the algorithm and try again. (By the way: that is ML/DL in a nutshell) Machine learning and AI.jpg Here is a definition of ML: Machine learning is the study of algorithms and mathematical models that computer systems use to progressively improve their performance on a specific task. That’s it. It is a computer learning to improve and tweak it’s algorithm, based on trial and error. Just like we learn things. No difference. Here is a definition for AI: Artificial intelligence, sometimes called machine intelligence, is intelligence demonstrated by machines, in contrast to the natural intelligence displayed by humans and other animals. However, AI is where things can really come unstuck. The aim is to get machines to think as we do. In a non-linear way. Human beings deal exceptionally well with ambiguity and we have an ability to match things up like apparently different words and images. Have you ever been transported back in time, in an instant, by a song clip or a smell? That is  human, no one taught you to do that. A computer could conceivably do that but only if it had previously been instructed to do so. It can do it so very fast you would be forgiven for thinking it was natural. It is not though, it is programmed to do it. Sure, it might have learnt to improve its own algorithm (Machine Learning again) to do that based on observations of human behaviour. It is still just mimicking what it sees as the appropriate behaviour, there has never been that spontaneous connection that you experienced that transported you to another time and place, even fleetingly. A recent high-profile example of AI and ML going a little bit awry and showing bias is in this article here. “Amazon Reportedly Killed an AI Recruitment System Because It Couldn’t Stop the Tool from Discriminating Against Women“ Well worth listening to the video and understanding the unconscious bias exhibited by the builders of the algorithms. There are efforts to remove the human biases that the machines learn from and perpetuate. But what is Deep Learning, I hear you cry? It  can simply be differentiated from Machine Learning as when the need for a human being to categorise all the different data inputs is eliminated. Now the machine (still only  the really fast calculator). Think self-driving cars, drones and many more much duller things. Presently, we humans need to be involved in the categorisation. There is even a Data Labelling factory in China to use humans to ‘teach’ machines what it is  that they are seeing. Equitable, Just, Neutral and Fair are components of moral behaviour that reside in the interpretation of the present societal norms, and not everyone agrees with them. Different cultures can have quite different views on a correct moral choice. Remember this when someone is trying to argue about the infallibility of computers. They can only be programmed with lagging data and they will always reflect us and our biases. For better or worse. bias see-saw.jpg

Algorithms – A Bluffers Guide

A breakdown and simplification of some current tech speak

The word ‘algorithm’ is uttered with a degree of reverence these days. It is the black magic behind AI and Machine Learning and is a favourite thing to go rogue in a modern plot line. The actors merely blame the bad algorithm when a computer goes crazy in a dystopian sci-fi catastrophe.   The decision making requirements that we are faced with in the modern commercial world far exceed our capacity in many instances because our brains evolved for a very different sort of world. A world of small groups where we rarely met anyone very  different from ourselves. We had significantly shorter lives and our main priorities were sex and survival. These days there is hugely increased complexity and nuance yet the evolved desire for rapid choice-making hasn’t left us.  Faced with these pressures we turn to computers for help. Computers helping humans is so pervasive and permeates almost all aspects of life. Such a rapid change has occurred in the last lifetime as the evolution of computing capacity increases exponentially. Your mobile telephone has vastly greater computing power than all three computers on the first Space Shuttle. Think about it for a moment. Your phone possesses all the computing power required to fire you into space. This incredible capability means that people have been fascinated with the idea that a computer can be turned from a dumb machine into a thinking machine (thinking as we do) since the dawn of the first machine. However, computational power is one thing. How to make it work as an independent thinking machine is another thing all together. One of the key things you need to do this you need an algorithm.

Algorithms: the rules needed for machine thinking. 

Algorithm Just to clear this up. Machines DO NOT think. Computers can process a huge volume of information, really really quickly because they are unbelievably fast calculators. The hardware is just a superfast counting machine with a screen. Algorithms are not hard to conceive, if you think of them like this; an algorithm is what you need to cook supper for your family. Few families eat the same thing for every meal of every day so there are constraints and variables. Imagine there are four of you. One is a vegetarian, one is on a low-fat diet and the other too aren’t that fussy but do have preferences. You want to provide them with a nutritious and tasty meal that ensures everyone enjoys the experience, including you.   Let’s imagine that you are 45 and have cooked for the same people many times before (almost daily) and as a consequence you have learnt a lot about what works and what doesn’t. However, this week is different and you haven’t had time to shop and the other three did the shopping for you. You open the cupboard doors and have a peer in the fridge and freezer to get an idea of what is available for you to cook with. Within about 30 seconds of taking stock of the cupboard contents, the fridge contents, the available utensils to cook with, any time constraints, the dietary preferences and so on you decide on a meal. You cook it, serve it and everyone eats. They get up from the table appropriately nourished leaving the process to be repeated the next day. What allowed you to do this was an algorithm in you head. Call it the ‘cooking for family’ algorithm.  Algorith wordcloud   Pause for a moment though and think about how simple it can sound and actually how the thinking and actions required was so incredibly, amazingly, mind-blowingly complex and nuanced.

 A quick note as to where this can go wrong

Simply put, computers are not people. Computers are superb for making decisions that do not require any emotion, ethics, bias and the like. Eventually a computer beat a Chess Grandmaster and uit did it by sheer computational brute force. However, to take the supper example: the cook knows the audience at a level a computer can’t match. All the calculations from an algortihm and it can’t know from someone’s face if they are the special kind of tired that a Wednesday can make them, so putting any kind of pie down for dessert would mean the world to them. And the others would see that a pie was not only what was needed but was a very thoughtful gesture thereby elevating the cook in the eyes of the other three and making an intangible but felt contribution to them too.  The aim is to have algorithms teach themselves by learning from mistakes in order to achieve the desired outcome of the programmer(s). They try,  but they are far from perfect and because we expect perfection from computers, in a way that is different from our expectations of one another, then mistakes are not easily forgiven. Algorithm 2

Data Ethics For Business

We exist in an increasingly data driven world. More and more, we are encouraged or directed to ‘listen to the data’ above all else. After all, the data doesn’t lie. Does it?

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Data Ethics in business is the name of the practice used to ensure that the data being used to make high-value commercial decisions is of the highest quality possible. However, there is a catch. Human beings are the catch. We have  gut-instinct, prejudices, experience, belief systems, conditioning, ego, expectation, deceit, vested interests etc. These behavioural biases all stand to cloud the data story, and usually do.

A high-value commercial decision does not necessarily have immediate financial consequences. Although, in commercial terms, a sub-optimal outcome is invariably linked with financial loss. In the first instance, the immediate effects of a high-value decision can be on organisational morale or have reputational consequences.

responsibility

When a high-value decision is to be made there are invariably advocates and detractors. Both camps like to believe that they are acting in the service of a cause greater than themselves. Occasionally, some of the actors cloud the story because their self-interest is what really matters to them, and they try hard to mask that with the veneer of the greater good. Hence the term ‘Data Story’, because behind the bare numbers and pretty graphics  there is an entire story.

The concept of conducting a pre-mortem examination of the entire data story to model what can go wrong is becoming more important for senior decision makers. It is getting increasingly difficult to use the traditional internally appointed devil’s advocate as, due to the inherent complexity of understanding a data story, this function needs to be performed by subject matter experts. Although the responsibility for decision-making always falls on the Senior Management, they want to do it with a full breakdown of the many facets of the data story.

BigData-wordcloud-2

 

In order to achieve this, individuals with a unique blend of talents, experience and inquisitiveness must be used. People with absolute objectivity and discretion, who don’t rely on inductive reasoning. Ones who are robust enough to operate independently, diplomatically and discreetly and have executive backing to interrogate all the data sources, ask the difficult questions and highlight any gaps, inconsistencies, irregularities. From this they can provide a report for the Executive Sponsor(s) with questions to ask and inquiries to make so a well-informed decision can be made.

After all, when there is  lots at stake, no one wants to be remembered as the person that screwed-up and tried to blame the data?

Why is data dangerous?

In the words of @RorySutherland: “The data made me do it” is the 21st Century equivalent of “I was only obeying orders”. The growing power and influence of Data Science touches everyone’s lives. Sutherland also remarks: “Markets are complex and there can be more than one right answer. People in business prefer the pretence of ‘definitive’ because if you can show you’ve done the ‘only right thing’ you have covered yourself in event of failure”. These are all attempts at Plausible Deniability, and they are weak.

For the record, plain old data is not dangerous, you are unlikely to be hit by an errant Spearmans Rho, or a rogue Control variable that detached itself from an analysis. Data is just a record of the measurable values of something that has happened in the past. Digital exhaust, if you will. Like speed in a car, it is the inappropriate use of it that causes issues.

zuck-data

Doing the right thing often sees people becoming  enslaved to Type 1 and Type 2 data, because they are the easy parts. You can hire experts, who can count well, use the software and understand how to tease out knowledge from the data points. What the majority can’t do, or may even do intentionally, is to manipulate the presentation, context and language used when presenting their findings. This is the Type 3 data I talk about, that isn’t traditional data as we know it.

Type 3 data is the really dangerous stuff. The reason for this is our complete fallibility as human beings. This is nothing to be ashamed of, it is how we are made and conditioned. It is in fact, entirely, boringly, and ordinarily normal. I was recently told by a lawyer – I say this because she is pretty well-educated – that all statistics are a lie. She then cited the famous Mark Twain (nicked from Disraeli) saying of, “There are lies, damn lies and statistics”, as if this were all the proof she required. Interestingly, when I challenged her on this and made a case for accurate uses of statistics she refused to even acknowledge this. She was wedded to her belief and I must be wrong. Case closed.

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I think immersion in courtroom rhetoric may have been getting the better of her. However, this goes to show the just how dangerous we humans can be. Imagine being a client with a lawyer whose dogmatism may cause them to overlook or be able to question relevant statistical evidence? All stemming from a strongly held view that all statistics are lies. Professor Bobby Duffy recently wrote an excellent book called Perils of Perception and on p.100 he shows just how problematic this view can be.

My point is: If a person who is well-educated, and practising in a profession like law, can hold such a position, then it is not beyond any of us to do so, quite unwittingly. Until one is more familiar with the behavioural biases that we are all susceptible to, the way Type 1 and Type 2 data can be mis-represented (Type 3 data) and how that uses our in-built foibles to generate a reaction.

This is where someone who understands both of these areas, and can blend that knowledge into an expertise which is useful, can help you. When important decisions on strategy, direction and spending  are conditional on interpreting data from others, you want to get it right first time. If not, you’ll be forced into, “The data made me do it”, and that rarely ends well.

burning money

 

 

 

 

Another Meaningless Graphic: Another Meaningless ‘Fact’

Have you ever seen one of these? A classic example of an attempt to bamboozle you with utterly meaningless data.

This is from a website that, amongst many other things, promises to “outpace disruption“. Does anyone know what that means? Anyhow, here is the result of outpacing disruption.

meaningless histogram
A meaningless bar chart

This was all there was. There was no information giving context. Still, positive numbers must mean it is wonderful investment. You can hardly fail to make a bundle

Are you ready to part with your money yet? No?  How about if you knew this dazzling fact: what if I were to tell you that this product increases checkout speed (e-commerce) by 24%. Impressed yet?

Or perhaps, after you read the first posts on The Problem With Data you were asking things like, a 24% increase over what? How many? What period? Which currency? What language? How measured? Credit/Debit card? PayPal? Amazon Pay? Stored customer details? First-time transactions? Repeat transactions? Fibre broadband or 5meg FTTC, TCP to the residence? And on and on.

 

 

Type 3 data in action. The Guardian is at it again.

The purpose of this blog is to get behind the data stories we encounter. Understandably, most commercial data is sensitive and remains unpublished. This means I have to rely on publicly available mangling of the data to illustrate the points.

The article of 11th October 2018 carries the snappy title, “Profits slide at big six energy firms as 1.4m customers switch” (The 3 types of data are explained here)

I will stick to the problems with data and not make this a critique af the article, for its weaknesses alone. That is just churlish. Read the following and think of yourself being presented with a document like this and having to critique its worth as something to base your decision-making on.

This article encompasses the Type 3 data example so very well! It appears that the journalist has started with an idea and then worked backwards to mangle what Type 1 data they have to fit the idea they want to transmit to the reader. To be clear: this post is not written an opinion piece about the Guardian, but a critique of an article purporting to use Type 1 data  to support the ‘Sliding Profits’ hypothesis.

Before we go any further the Golden Rule of data has been broken. You simply mustn’t decide the answer, and then try to manipulate, mangle and torture the data to fit your conclusion. You must be led by the data, not the other way round. It is fine to start with a hypothesis and then test the data to see if that is true. It is a major credibility red flag when the conclusion is actually the initially assumed answer.

Red Flag

If the article is apparently a business article it is rather worrying when the journalist obviously doesn’t know the difference between profit margins and profit¹. These are two distinctly different ideas yet they are used interchangeably in the piece. Red flag number two (if the first wasn’t enough). Paragraph five manages to combine the margin’s of two companies with the profits of another and then – completely randomly – plugs in (excuse the pun) an apparently random reference to a merger and the Competition Commission.

Terms like the ‘Big Six’ are used but nowhere does the author bother to say who the Big Six are. Whilst it is a moderately common term it cannot be assumed that everyone knows who they are. This is sloppy reportage and another Red Flag for the reader. Sloppy here, sloppy elsewhere. Who knows? This is back to the Type 3 issue of how it is presented to you. In this case, so far, very poorly.

The energy market regulator, Ofgem, is cited as the source for the first graphic. The Y (vertical) axis is numbered with no qualification, the date and document that this is taken from isn’t mentioned. Type 1 data being mangled by the Type 3 data. Overall – poor sourcing and not worth the bother. You can dismiss graphics like this as you can reasonably assume it is a form of visual semiotic designed to elicit a feeling and not communicate any reliable Type 1 data to you. (Note the profits and profit margins even being conflated in the graphic title!)

Poor graphic.JPG
Poor graphic designed to mislead – taken from the Guardian article.

 

The final critique is the one that speaks to the concept of Type 3 data. The language used in the article is such a blatant attempt to skew the article away from reportage about how the entrant of challengers into the market place are affecting the profits, and profit margins, of the established players. I think the subsidiary point is about the fact that consumers aren’t switching suppliers as much as is expected. I had to read the article several times to distil those as the most likely objectives of the piece.

Finally, if you re-read the article and just look at the tone and, more specifically, the adjectives used you’ll be surprised. What I can’t work out is the author’s agenda. To just report such a muddle of data is one thing, most popular press has an agenda of some kind.

NB: I really hope the Guardian doesn’t just keep gifting such poorly written articles. I think I may look at the coconut oil debate next!

Continue reading “Type 3 data in action. The Guardian is at it again.”