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.

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.


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.


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.

Data – The Fog of Promises, and What To Do About It

Calculating the value of data is something I have been thinking about a lot. Data, any and all, seems to be relentlessly hoovered up whenever we use any form of connected device. Who had ever heard of data as a mainstream topic twenty years ago? Nowadays, we have seen Mark Zuckerberg answering to Congress in the States and countless articles based around what Google and Apple know about us. Some people are laissez-faire about it whilst others veer towards the downright paranoid.

zuck in congress

Organisations collect data, they hoard data and (hopefully) guard these vast amounts of data that they collect. Why? Because it is valuable. It is useful. Apparently. However, who in a company actually gets down to the nitty-gritty of this and can measure and express the Return on Data that this feverish collection and hoarding actually brings to the organisation?

In 2015 Doug Laney from Gartner wrote about data in financial terms. How it can affect the value of a takeover target, if they have a vast unexploited data store, for example. Were that to be monetised then what is it worth? Does this mean the buyer is getting a fantastic deal or when it seems to be overvalued on traditional metrics is the difference made up by the value of their data? Herein lies a real problem as the difficulty in valuing data stems from several reasons.

Firstly, that there is no firm formula to do so, because to some that data is just wasted storage and to others it is gold. Whereas a physical asset, such as a piece of land, is such a mainstream asset that it is far easier to value. With data, the  great big lump of bits and bytes only has value if the owner knows how to extract information and insight from it, and use that effectively to make them more competitive or to sell to someone else in a finished and usable form. People have had a stab at it by trying to vary old maths and make it new maths. I found the following on the Internet:


Though his looks like an elegant formula,  the Gain from Data metric is subject to so many other variables, primarily time,  it is almost impossible to calculate so simply, makinf the formula impossible to scale. It only serves to highlight just how the temporal aspect of data value is so important. Depending on what it is, it may be very time limited, making it useful only in a very brief window. Think of data like a paper currency that can burst into flames at any moment.burning-money-png-2

One second it has the face value and the next it is ashes.

In contrast, a piece of land is just there. No more land is being created, whereas data creation is never-ending: limited only by our ability to get it and store it.

Secondly, the technical aspects are crucial. What form is it held in, on what type of database, where is it held (there are massive regulatory differences around the world), have the data owners consented to its use, by whom, how old is it, how consistent is it and so on. If I can’t use in my company for my purposes then it is just Ones and Zeros on a hard drive somewhere, merely cluttering up the ether. Utterly without value.

The fact remains that extraordinary amounts of data are being recorded about us, all of the time. I recently holidayed in Norway and in ten days I didn’t use one bit of hard currency. All card, all the time. I navigated around using Google Maps. I checked TripAdvisor and used Uber, as well as uploading countless photos to Facebook for family abroad to see. In doing so I must have left an enormous digital smear across the Norwegian landscape. Me and the thousands of other tourists on holiday at the same time. Can you imagine the quantity of data generated by me and the billions of other people using connected services every single day?

To be able to achieve a RoD that makes all the efforts and costs at collection and storage worthwhile, several things need to happen and I can only really see that these can happen under the guidance and direction of a very senior – if not on the Board type senior – individual who guides a team with specific responsibilities. Call them a Chief Data Officer (CDO).

Ideally the value of data is considered so important that the CDO is on the Board. The CDO would need to have close ties with Marketing and Strategy functions to understand how they intend to use resources to achieve them, and whether existing data is useful or new data needs to be acquired. Additionally,  they need to know how to shape and deliver it to them in a worthwhile manner. Then there needs to be a real-time feedback loop – Sales? – in order to assess the efficacy of the deployed data as well, as a direct line between them and the technical functions of the company. The sort of things CIO deals with, especially storage and access. The CFO will have demands on their funds from the CDO. They need to be able to understand the RoD and how it is affecting the bottom line, the share price, their partners and so on.

businessman staring into fog

Most importantly of all is someone who can see through the Fog of Promise that all this data is purported to hold. The RoD that can be achieved if only they used it ‘properly’ is the sort of golden thread that is so often sold to them. Correlation does not equal causation. I’ll repeat that: correlation DOES NOT equal causation. Falling into the Feynman Trap is something that affects the best and the brightest (Famously, Jim Collins did this in Good to Great). Usually when they become mesmerised by their own belief in the infallibility of data.

The CDO not only ensures the data is valued correctly, they are responsible for preventing their company being led down a rabbit-hole of promise of the jam-tomorrow variety. The sunken cost fallacy remains as relevant today as it ever was and sometimes the emperor is indeed naked.



How Do I Know…

…if I am getting the entire Data Story?

…if it was analysed properly?

…if I can trust the conclusions and recommendations?

Every executive that is reliant on decision-making data presented to them by other people shares these doubts. If you don’t know how to ask the correct questions, parse the information in the replies correctly and follow-up with the right requests for more information you will forever be at the mercy of others. My experience is that people with responsibility do not enjoy that situation.

Without an impartial assessment of the Data Story they will not be able to satisfy themselves that the Data Story they are being told is the right one. Every big decision needs to be made with a greater element of faith than was intended.


There are two basic elements to achieving an accurate Data Story. The first is the human, and the second is the technical.

  1. Human

Everything may be tickety-boo, the best, most loyal people, are giving you a perfect Data Story. If you know this to be true then stop reading now. Life is great. On the other hand, if you ever wonder then keep reading.

(Type 1, Type 2, and Type 3 data - a recap here -  for clarity , I am writing about Type 2 and Type 3 data. Remember, Type 1 is the Mars Lander sort of stuff!)
  • “These results are from AI. It can do things we can’t.”

Whether the results are attributed to AI, which has spotted a very subtle pattern in a vast mass of data, or a straight survey designed, run and analysed by , means nothing in and of itself.

Even if an AI tool uses the best and the brightest to program the algorithms it ‘thinks and learns’ with, the fact remains that people – with all their attendant beliefs, prejudices, biases, agendas etc – set the rules, at least to start. If the machine has indeed learned by trial and error, it was still programmed by people. Therein lies the weakness.

human AI blend

This weakness comes from the initial decision makers, precisely because they aren’t you or your Board. The Board is likely to have a much wider range of experience and carry more responsibility than the Data Science/IT/Marketing departments.

How often have you spent time with these people? Are they even in the same office as you? How old are they? What are their social and political biases? And so on. Unless you know this then how can you begin to understand anything about the initial algorithms that started the AI going. When were they written, what was the market like then, by whom, in which country?

With all data collection and manipulation it is crucial to have a fuller story. It is the  background and understanding of those setting the questions, writing the algorithms, tweaking the machine learning, analysing the data, their managers, the instructions they have been given, the emphasis that this Data Story has received in the rest of the organisation before you see it. It is also insight into the marketplace provided by the sort of Thick Data that Tricia Wang and other ethnographers have popularised.

My message to you is that data is so much more than numbers. Just numbers can be misrepresent the story so greatly. We are social animals and as long as there are people involved in the production, analysis and presentation of data it doesn’t matter a jot how incredibly intelligent and fast the tools are. We are the weakness.

complicated employees

If you still struggle believing this concept then think about electronic espionage. It is rarely a failure in something mechanical that causes catastrophic breaches of security, it is the relative ease with which people can be compromised and share information. The people are the weak link.  In the very first days of hacking a chap called Kevin Mitnik in the US spoke of Social Engineering as the means to an end. We are all inherently flawed, these flaws are shaped and amplified by our social and work environments, so why couldn’t that affect the Data Story you get?

    2. Technical

  • “The data we have used is robust.”

I’ve heard that line trotted out many times. Gosh, where to start? It may be. Nonetheless, a lot can and does happen to the data before you see the pretty graph. Here are just a few things to consider before just agreeing with that assertion:

What was/were the hypothesis/hypotheses being tested?


When was it collected?

By whom (in-house or bought in from a third-party)?

Qualitative, quantitative, or a blend?

What was the method of collection (face to face interviews, Internet, watching and ticking boxes, survey, correlational, experimental, ethnographic, narrative,phenomenological, case study – you get the idea, there are more…)?

How was the study designed?

Who designed it?

How large was the sample(s)?

How was the data edited before analysis (by who, when, with what tools, any change logs etc, what questions were excluded and why)?

How was the data analysed (univariate, multivariate, logarithmic, what were the dummy variables and why, etc.)?

How is being presented to me, and why this way (scales, chart types, colouring, size, accompanying text etc)?

Research design

And so on. This is just a taste of the complexity behind the pretty pictures shown to you as part of the Data Story. From these manicured reports you are expected to make serious decisions that can have serious consequences.

You must ask yourself if you are happy knowing that the Data Story you get may be intentionally curated or unintentionally mangled. I started this site and the consultancy because I am an independent sceptic. In this age of data-driven decision-making you mustn’t forget. Incorrect data can’t take responsibility for mistakes, but you will be held to account. This is not scaremongering, it is simply fact.

If you need a discreet, reliable and sceptical  third-party to ask these questions then drop me an email.  I compile the answers or understand and highlight the gaps. You make the decisions, albeit far better informed and with the ability to show that you didn’t take the proffered Data Story at face-value, but asked an expert to help you understand it.



Let’s break down today’s bad data usage – Yes Guardian newspaper, I mean you!

Overnight there was a report published in The Guardian newspaper entitled “Met police’s use of force jumps 79% in one year”. I see the hysteria on Twitter – being whipped up and added to by the usual suspects who revel in the dog-whistle approach to political discourse – about the use of force by the Metropolitan Police being used disproportionately against black people.

“The Metropolitan police’s use of force has risen sharply in the last year, with black people far more likely to be subjected to such tactics than anyone else, the Guardian can reveal.”

Firstly: this is not an attempt to take sides. The police may be guilty of the accusation. Without correct and fair anaslysis of data it is impossible to tell. See a previous post about how to approach stories like this.

Secondly: the purpose of this article is to interrogate the findings of the Guardian’s reporting of this story. If this undermines the story then so be it. Do not conflate that with an endorsement of the police in London, for I do not know enough to comment about them. This is about the use of data.

The main thrust of the article is the “79% in a year” claim. It is what has been seized upon and retweeted with vigour. Nowhere does it appear that the people getting all worked up over this selective quote  have actually looked into the data.

“On 39% of occasions in which force was used by Met officers in the first five months of the financial year, it was used on black people, who constitute approximately 13% of London’s population.”

The first thing that struck me about this piece was the language and imagery used. Whilst the language is not the data, the way it is used certainly serves to alert you to the fact that they may be glossing over details in the pursuit of shock value. The Guardian is (was?) a credible broadsheet with a left-of-centre bias. Nevertheless, now they are giving away their content for free, they seem to be leaning towards the ‘clickbaity’ style of reportage, and that is a pity. Look at the graphic they have used. It is fairly emotive stuff. A white man pointing a weapon at you.

taser front view

In the first paragraphs the article uses words and phrases like, “jumps, risen sharply, most likely, on average, approximately, raised alarm, receiving end, stark figures, police culture” and the like.

These are written efforts to engage the enraged response (metaphorically speaking) part of our brains rather than the rational analysis. The System 1 reaction and not System 2 as Daniel Khanemann calls it in his book Thinking Fast and Slow.

Arguably, the alleged disproportionate use of force by police officers against black people is so serious an allegation that it warrants slowing down, taking a deep breath and analysing correctly?

Let’s break down the critical analysis a bit by asking some questions, and making some observations.

  • The only reference to the data used is “Guardian analysis of official figures“. This alone should sound the loudest alarms ringing in your head and set you into sceptical analysis mode. What figures, analysed by whom, what is their expertise, what were the controls used, compared to what, is there (perish the thought that a journalist is anything other than scrupulously impartial) an agenda on the part of the presenter of these figures?  [I think I may have found the data being used. See the bottom of the article for links. It certainly isn’t acknowledged in the article. This might lead a cynic to wonder if it may be being taken out of context and the journalists don’t want this easily checked for fear of undermining their credibility.]


  • Many people think of the word black being interchangeable (perhaps incorrectly) with people of colour. It turns out that the Guardian even mentions that ‘Asians’ and ‘Other’ are not part of this classification.


  • Are the figures generated by the ethnicity initially recorded by police codes for radio use or the self-defined ethnicity – 16+1 versus 9 – codes used by the subjects, even if they differ from the officer assesment. It doesn’t say.


  • In the last paragraph of the piece the most convoluted attempt at figures is used;  we witness the groups ‘Asian’ and ‘Other’ being rolled together to make a ‘52%’ claim sound more shocking. They need to decide how they portray things and stick to it. Previously the paper excluded ‘Asian’ and ‘Other’ from the ‘Black’ category and instead let them sit outside along with ‘White’ in order to use the five month and 79% figure on which the outrage is based.


  • There is no indication if these figures are split between reactive (responding to calls from the public), or proactive (the officers see something that they decide to investigate further). Proactive interventions are carefully considered by officers, they rarely steam in like you see in the movies. Things like back-up availability, whether they are single-crewed (and far more vulnerable), priorities like previous calls, outstanding paperwork (yes, really, there is a lot), their caseload and so on. Proactive policing is where a racist would shine as they would be able to target black people if that was their aim. From there they would need to engage and at least claim a veneer of credibility for their choice to use force. That wouldn’t last long as everyone would need to be in on it. These days that is very difficult.


  • Debra Coles from the charity Inquest is reported as saying: “This also provides yet more evidence about the overpolicing and criminalisation of people from black and minority communities. It begs important questions about structural racism and how this is embedded in policing practices.” – From other remarks in the article, when the Metropolitain Police were approached and asked for their view, it sounds like after losing some 20k officers the police are rarely proactive, mostly reactive. If only they had the time and resources to ‘overpolice’ anywhere.


  • What if, and I am trying to steer away from political and social commentary here for it is not my intention, the police respond to more incidents in places where there is a greater proportion of black people? I X amount of interactions involve use of foprce then is stands that the use of force against blacks is more likely. There is no doubt there is historical antipathy towards the police amongst much of the black community, especially in London. Previous generations of the Met (and other forces) have not been known for their even-handed approach towards the black community. Young men (for it is predominantly males)  in groups often feel that their masculinity is being challenged if an authority figure like a police officer lawfully requires them to do something. What if this leads to more physical  resistance which in turn leads to force having to be used? What if the white people, the Asians and the Others are more compliant when dealing with the police? What if, what if, what if? The fact is that these figures do not seem to be presented in a holistic manner. By that I mean controlling for variables such as age, gender, location, time, weather, changed police priorities, changed dynamics of interaction due to cuts in resources and so on.


  • The phrase ‘use of force’ is misused by the journalists and politicians. The police use a very specific definitionb, and it is not what the ordinary person thinks it may be. A voluntary handcuffing is a use of force. You know, the kind where the officer says something like, “for my own protection I am going to handcuff you.” and the subject complies. Perhaps a single-crewed female arresting a large male and having to drive him to custody herself. Merely drawing Captor (CS) spray needs to be recorded as a use of force. No one was sprayed, situation calmed down. Same as the drawing of a baton. Force is also shooting someone dead. There is a wide definition of force. Force, in police recording terms, does not mean taking the suspect to the ground in a violent bundle.


  • The whole method of recording has changed, a fact the paper skips neatly over. Too complicated to explain I imagine. The simple fact is that comparing these new figures generated and recorded one way with the past where they were not recorded in the same way, if at all, is simply invalid. It is far too soon to tell.


  • The politician, David Lammy MP, famous for trying to whip up stories like this to create indignation – I say this merely because he is a public figure who regularly tortures data or chooses to use tortured data –  betrays a lack of understanding when he talks about the criminal justice system and the police. The police in London are merely one small part of this national system. Saying there is systemic racism at each stage of the system in a piece targeted at the police in London does smack of trying to score wider points and not, in my opinion, worthy of inclusion. It weakens any point trying to be made. It is good to have Lammy on board for a bit more clickbait type appeal though. He has a large Twitter following and retweeted the article almost immediately. Surely not because he is mentioned in it.


  • As Matt Twist of the Met Police said, “…the figures should not be compared with population demographics. He said: “The collation of these figures is still in its early stages, and as this is new data, there are no previous benchmarks to compare it to. Therefore any conclusions drawn from them must be carefully looked at against this context, and should only be compared with those individuals who have had contact with officers, rather than the entire demographic of London.” You may think he is a police stooge but it does not make his statement incorrect.


  • The paper even says it is comparing  FY 2017/18 to FY 2018/19. This means that from April 6th 2017 to April 5th 2018 and similarly for 18/19. This is important because the new recording system was introduced from April 2017. The data being quoted is April to August 2017. It is being compared with April to August 2018. What happened to the seven months in between? Does this show a steady rise instead of a jump? Has anything else changed in this time? For example: the new system may not have started well and overlooked items or officer engagement was not what it should be, resulting in pressure from Borough Commanders down to record more accurately, leading to an apparent jump in incidents when it is actually a rise in adherence. Just ignoring a seven month gap is concerning. Why? An oversight or intentional?


Images are worth a thousand words. Misleading images are still far more impactful than poor descriptions. I reproduce this because it is a howler of a poor and misleading graphic.  The article is using the Financial Year for measurement, hence the mention of 2019 whilst we are in 2018.  Laying out the same images of London side-by-side implies that a comparison is about to be demonstrated.  However, the left hand image uses Westminster and mentions five other boroughs, none of which are referenced in the right hand image. This makes the image of questionable value, other than adding to the devaluation of credibility.  The source attribution should say that this is where the data was sourced, not presented in this way. I rather implies that this graphic is from the Met Police. It isn’t.

I think this was rather twisted to produce the graphics: Met Police Use of Force information.

howler of a bad graphic

Interestingly, the Met Police data gives this caveat, albeit buried on the third tab of their use of force stats page and not linkable by a URL. It explains areas where the data may be misinterpreted. The journalists don’t bother to tell us if they have taken this into account or not. We’ll never know.

CoversheetSo you can see that this article – and many stories on many topics – is riddled with inconsistencies. To me, I just dismiss it because it hasn’t got some of the basics right. It may be speaking a degree of truth, but that truth is devalued by the poor presentation.

Data is fine. Data is useful. Data is just digital exhaust. Data without context is just numbers and means nothing.

As I said previously, “Data only helps you take a problem apart and understand its pieces. It is not suited to put them back together again. The tool needed for that is the brain.”

Try to dissect stories quoting numbers. Be they in the press or someone making a commercial claim in order to influence your actions.


Here are some likely data sources for the story and for you to use when reading these type of stories using numbers to give credibility to their assertations.:

UK Government crime statistics

Metropolitan Police data

Office for National Statistics

UK Data Service video


PS: Anecdotally: I have known many types of officer. From 6’3″ tall Senegalese immigrants, who started as a PCSO in London and is now policing rural Oxfordshire, to short white people that are Reading natives who are born and bred and police there. They vary in their attitudes and actions because they are people. The huge majority want to make their communities better places. I do have no doubt that amongst them there are a few racist thugs, albeit a tiny and ever-decreasing amount. A bit like regular people I suppose. 


PPS: There may be typos. I try very hard to proofread. I am a terrible typist though.