How to predict floods

File:Llanrwst Floods 2015 1.ogvI started my grown-up working life on a project seeking to predict extreme ocean currents off the north west coast of the UK. As a result I follow environmental disasters very closely. I fear that it’s natural that incidents in my own country have particular salience. I don’t want to minimise disasters elsewhere in the world when I talk about recent flooding in the north of England. It’s just that they are close enough to home for me to get a better understanding of the essential features.

The causes of the flooding are multi-factorial and many of the factors are well beyond my expertise. However, The Times (London) reported on 28 December 2015 that “Some scientists say that [the UK Environment Agency] has been repeatedly caught out by the recent heavy rainfall because it sets too much store by predictions based on historical records” (p7). Setting store by predictions based on historical records is very much where my hands-on experience of statistics began.

The starting point of prediction is extreme value theory, developed by Sir Ronald Fisher and L H C Tippett in the 1920s. Extreme value analysis (EVA) aims to put probabilistic bounds on events outside the existing experience base by predicating that such events follow a special form of probability distribution. Historical data can be used to fit such a distribution using the usual statistical estimation methods. Prediction is then based on a double extrapolation: firstly in the exact form of the tail of the extreme value distribution and secondly from the past data to future safety. As the old saying goes, “Interpolation is (almost) always safe. Extrapolation is always dangerous.”

EVA rests on some non-trivial assumptions about the process under scrutiny. No statistical method yields more than was input in the first place. If we are being allowed to extrapolate beyond the experience base then there are inevitably some assumptions. Where the real world process doesn’t follow those assumptions the extrapolation is compromised. To some extent there is no cure for this other than to come to a rational decision about the sensitivity of the analysis to the assumptions and to apply a substantial safety factor to the physical engineering solutions.

One of those assumptions also plays to the dimension of extrapolation from past to future. Statisticians often demand that the data be independent and identically distributed. However, that is a weird thing to demand of data. Real world data is hardly ever independent as every successive observation provides more information about the distribution and alters the probability of future observations. We need a better idea to capture process stability.

Historical data can only be projected into the future if it comes from a process that is “sufficiently regular to be predictable”. That regularity is effectively characterised by the property of exchangeability. Deciding whether data is exchangeable demands, not only statistical evidence of its past regularity, but also domain knowledge of the physical process that it measures. The exchangeability must continue into the predicable future if historical data is to provide any guide. In the matter of flooding, knowledge of hydrology, climatology, planning and engineering, law, in addition to local knowledge about economics and infrastructure changes already in development, is essential. Exchangeability is always a judgment. And a critical one.

Predicting extreme floods is a complex business and I send my good wishes to all involved. It is an example of something that is essentially a team enterprise as it demands the co-operative inputs of diverse sets of experience and skills.

In many ways this is an exemplary model of how to act on data. There is no mechanistic process of inference that stands outside a substantial knowledge of what is being measured. The secret of data analysis, which often hinges on judgments about exchangeability, is to visualize the data in a compelling and transparent way so that it can be subjected to collaborative criticism by a diverse team.

#executivetimeseries

ExecTS1OxfordDon Wheeler coined the term executive time series. I was just leaving court in Oxford the other day when I saw this announcement on a hoarding. I immediately thought to myself “#executivetimeseries”.

Wheeler introduced the phrase in his 2000 book Understanding Variation: The Key to Managing Chaos. He meant to criticise the habitual way that statistics are presented in business and government. A comparison is made between performance at two instants in time. Grave significance is attached as to whether performance is better or worse at the second instant. Well, it was always unlikely that it would be the same.

The executive time series has the following characteristics.

  • It as applied to some statistic, metric, Key Performance Indicator (KPI) or other measure that will be perceived as important by its audience.
  • Two time instants are chosen.
  • The statistic is quoted at each of the two instants.
  • If the latter is greater than the first then an increase is inferred. A decrease is inferred from the converse.
  • Great significance is attached to the increase or decrease.

Why is this bad?

At its best it provides incomplete information devoid of context. At its worst it is subject to gross manipulation. The following problems arise.

  • Though a signal is usually suggested there is inadequate information to infer this.
  • There is seldom explanation of how the time points were chosen. It is open to manipulation.
  • Data is presented absent its context.
  • There is no basis for predicting the future.

The Oxford billboard is even worse than the usual example because it doesn’t even attempt to tell us over what period the carbon reduction is being claimed.

Signal and noise

Let’s first think about noise. As Daniel Kahneman put it “A random event does not … lend itself to explanation, but collections of random events do behave in a highly regular fashion.” Noise is a collection of random events. Some people also call it common cause variation.

Imagine a bucket of thousands of beads. Of the beads, 80% are white and 20%, red. You are given a paddle that will hold 50 beads. Use the paddle to stir the beads then draw out 50 with the paddle. Count the red beads. Repeat this, let us say once a week, until you have 20 counts. The data might look something like this.

RedBeads1

What we observe in Figure 1 is the irregular variation in the number of red beads. However, it is not totally unpredictable. In fact, it may be one of the most predictable things you have ever seen. Though we cannot forecast exactly how many red beads we will see in the coming week, it will most likely be in the rough range of 4 to 14 with rather more counts around 10 than at the extremities. The odd one below 4 or above 14 would not surprise you I think.

But nothing changed in the characteristics of the underlying process. It didn’t get better or worse. The percentage of reds in the bucket was constant. It is a stable system of trouble. And yet measured variation extended between 4 and 14 red beads. That is why an executive time series is so dangerous. It alleges change while the underlying cause-system is constant.

Figure 2 shows how an executive time series could be constructed in week 3.

RedBeads2

The number of beads has increase from 4 to 10, a 150% increase. Surely a “significant result”. And it will always be possible to find some managerial initiative between week 2 and 3 that can be invoked as the cause. “Between weeks 2 and 3 we changed the angle of inserting the paddle and it has increased the number of red beads by 150%.”

But Figure 2 is not the only executive time series that the data will support. In Figure 3 the manager can claim a 57% reduction from 14 to 6. More than the Oxford banner. Again, it will always be possible to find some factor or incident supposed to have caused the reduction. But nothing really changed.

RedBeads3

The executive can be even more ambitious. “Between week 2 and 17 we achieved a 250% increase in red beads.” Now that cannot be dismissed as a mere statistical blip.

RedBeads4

#executivetimeseries

Data has no meaning apart from its context.

Walter Shewhart

Not everyone who cites an executive time series is seeking to deceive. But many are. So anybody who relies on an executive times series, devoid of context, invites suspicion that they are manipulating the message. This is Langian statistics. par excellence. The fallacy of What you see is all there is. It is essential to treat all such claims with the utmost caution. What properly communicates the present reality of some measure is a plot against time that exposes its variation, its stability (or otherwise) and sets it in the time context of surrounding events.

We should call out the perpetrators. #executivetimeseries

Techie note

The data here is generated from a sequence of 20 Bernoulli experiments with probability of “red” equal to 0.2 and 50 independent trials in each experiment.

Does noise make you fat?

“A new study has unearthed some eye-opening facts about the effects of noise pollution on obesity,” proclaimed The Huffington Post recently in another piece or poorly uncritical data journalism.

Journalistic standards notwithstanding, in Exposure to traffic noise and markers of obesity (BMJ Occupational and environmental medicine, May 2015) Andrei Pyko and eight (sic) collaborators found “evidence of a link between traffic noise and metabolic outcomes, especially central obesity.” The particular conclusion picked up by the press was that each 5 dB increase in traffic noise could add 2 mm to the waistline.

Not trusting the press I decided I wanted to have a look at this research myself. I was fortunate that the paper was available for free download for a brief period after the press release. It took some finding though. The BMJ insists that you will now have to pay. I do find that objectionable as I see that the research was funded in part by the European Union. Us European citizens have all paid once. Why should we have to pay again?

On reading …

I was though shocked reading Pyko’s paper as the Huffington Post journalists obviously hadn’t. They state “Lack of sleep causes reduced energy levels, which can then lead to a more sedentary lifestyle and make residents less willing to exercise.” Pyko’s paper says no such thing. The researchers had, in particular, conditioned on level of exercise so that effect had been taken out. It cannot stand as an explanation of the results. Pyko’s narrative concerned noise-induced stress and cortisol production, not lack of exercise.

In any event, the paper is densely written and not at all easy to analyse and understand. I have tried to pick out the points that I found most bothering but first a statistics lesson.

Prediction 101

Frame(Almost) the first thing to learn in statistics is the relationship between population, frame and sample. We are concerned about the population. The frame is the enumerable and accessible set of things that approximate the population. The sample is a subset of the frame, selected in an economic, systematic and well characterised manner.

In Some Theory of Sampling (1950), W Edwards Deming drew a distinction between two broad types of statistical studies, enumerative and analytic.

  • Enumerative: Action will be taken on the frame.
  • Analytic: Action will be on the cause-system that produced the frame.

It is explicit in Pyko’s work that the sampling frame was metropolitan Stockholm, Sweden between the years 2002 and 2006. It was a cross-sectional study. I take it from the institutional funding that the study intended to advise policy makers as to future health interventions. Concern was beyond the population of Stockholm, or even Sweden. This was an analytic study. It aspired to draw generalised lessons about the causal mechanisms whereby traffic noise aggravated obesity so as to support future society-wide health improvement.

How representative was the frame of global urban areas stretching over future decades? I have not the knowledge to make a judgment. The issue is mentioned in the paper but, I think, with insufficient weight.

There are further issues as to the sampling from the frame. Data was taken from participants in a pre-existing study into diabetes that had itself specific criteria for recruitment. These are set out in the paper but intensify the questions of whether the sample is representative of the population of interest.

The study

The researchers chose three measures of obesity, waist circumference, waist-hip ratio and BMI. Each has been put forwards, from time to time, as a measure of health risk.

There were 5,075 individual participants in the study, a sample of 5,075 observations. The researchers performed both a linear regression simpliciter and a logistic regression. For want of time and space I am only going to comment on the former. It is the origin of the headline 2 mm per 5 dB claim.

The researchers have quoted p-values but they haven’t committed the worst of sins as they have shown the size of the effects with confidence intervals. It’s not surprising that they found so many soi-disant significant effects given the sample size.

However, there was little assistance in judging how much of the observed variation in obesity was down to traffic noise. I would have liked to see a good old fashioned analysis of variance table. I could then at least have had a go at comparing variation from the measurement process, traffic noise and other effects. I could also have calculated myself an adjusted R2.

Measurement Systems Analysis

Understanding variation from the measurement process is critical to any analysis. I have looked at the World Health Organisation’s definitive 2011 report on the effects of waist circumference on health. Such Measurement Systems Analysis as there is occurs at p7. They report a “technical error” (me neither) of 1.31 cm from intrameasurer error (I’m guessing repeatability) and 1.56 cm from intermeasurer error (I’m guessing reproducibility). They remark that “Even when the same protocol is used, there may be variability within and between measurers when more than one measurement is made.” They recommend further research but I have found none. There is no way of knowing from what is published by Pyko whether the reported effects are real or flow from confounding between traffic noise and intermeasurer variation.

When it comes to waist-hip ratio I presume that there are similar issues in measuring hip circumference. When the two dimensions are divided then the individual measurement uncertainties aggregate. More problems, not addressed.

Noise data

The key predictor of obesity was supposed to be noise. The noise data used were not in situ measurements in the participants’ respective homes. The road traffic noise data were themselves predicted from a mathematical model using “terrain data, ground surface, building height, traffic data, including 24 h yearly average traffic flow, diurnal distribution and speed limits, as well as information on noise barriers”. The model output provided 5 dB contours. The authors then applied some further ad hoc treatments to the data.

The authors recognise that there is likely to be some error in the actual noise levels, not least from the granularity. However, they then seem to assume that this is simply an errors in variables situation. That would do no more than (conservatively) bias any observed effect towards zero. However, it does seem to me that there is potential for much more structured systematic effects to be introduced here and I think this should have been explored further.

Model criticism

The authors state that they carried out a residuals analysis but they give no details and there are no charts, even in the supplementary material. I would like to have had a look myself as the residuals are actually the interesting bit. Residuals analysis is essential in establishing stability.

In fact, in the current study there is so much data that I would have expected the authors to have saved some of the data for cross-validation. That would have provided some powerful material for model criticism and validation.

Given that this is an analytic study these are all very serious failings. With nine researchers on the job I would have expected some effort on these matters and some attention from whoever was the statistical referee.

Results

Separate results are presented for road, rail and air traffic noise. Again, for brevity I am looking at the headline 2 mm / 5 dB quoted for road traffic noise. Now, waist circumference is dependent on gross body size. Men are bigger than women and have larger waists. Similarly, the tall are larger-waisted than the short. Pyko’s regression does not condition on height (as a gross characterisation of body size).

BMI is a factor that attempts to allow for body size. Pyko found no significant influence on BMI from road traffic noise.

Waist-hip ration is another parameter that attempts to allow for body size. It is often now cited as a better predictor of morbidity than BMI. That of course is irrelevant to the question of whether noise makes you fat. As far as I can tell from Pyko’s published results, a 5 dB increase in road traffic noise accounted for a 0.16 increase in waist-hip ratio. Now, let us look at this broadly. Consider a woman with waist circumference 85 cm, hip 100 cm, hence waist-hip ratio, 0.85. All pretty typical for the study. Predictively the study is suggesting that a 5 dB increase in road traffic noise might unremarkably take her waist-hip ratio up over 1.0. That seems barely consistent with the results from waist circumference alone where there would not only be millimetres of growth. It is incredible physically.

I must certainly have misunderstood what the waist-hip result means but I could find no elucidation in Pyko’s paper.

Policy

Research such as this has to be aimed at advising future interventions to control traffic noise in urban environments. Broadly speaking, 5 dB is a level of noise change that is noticeable to human hearing but no more. All the same, achieving such a reduction in an urban environment is something that requires considerable economic resources. Yet, taking the research at its highest, it only delivers 2 mm on the waistline.

I had many criticisms other than those above and I do not, in any event, consider this study adequate for making any prediction about a future intervention. Nothing in it makes me feel the subject deserves further study. Or that I need to avoid noise to stay slim.

Soccer management – signal, noise and contract negotiation

Some poor data journalism here from the BBC on 28 May 2015, concerning turnover in professional soccer managers in England. “Managerial sackings reach highest level for 13 years” says the headline. A classic executive time series. What is the significance of the 13 years? Other than it being the last year with more sackings than the present.

The data was purportedly from the League Managers’ Association (LMA) and their Richard Bevan thought the matter “very concerning”. The BBC provided a chart (fair use claimed).

MgrSackingsto201503

Now, I had a couple of thoughts as soon as I saw this. Firstly, why chart only back to 2005/6? More importantly, this looked to me like a stable system of trouble (for football managers) with the possible exception of this (2014/15) season’s Championship coach turnover. Personally, I detest multiple time series on a common chart unless there is a good reason for doing so. I do not think it the best way of showing variation and/ or association.

Signal and noise

The first task of any analyst looking at data is to seek to separate signal from noise. Nate Silver made this point powerfully in his book The Signal and the Noise: The Art and Science of Prediction. As Don Wheeler put it: all data has noise; some data has signal.

Noise is typically the irregular aggregate of many causes. It is predictable in the same way as a roulette wheel. A signal is a sign of some underlying factor that has had so large an effect that it stands out from the noise. Signals can herald a fundamental unpredictability of future behaviour.

If we find a signal we look for a special cause. If we start assigning special causes to observations that are simply noise then, at best, we spend money and effort to no effect and, at worst, we aggravate the situation.

The Championship data

In any event, I wanted to look at the data for myself. I was most interested in the Championship data as that was where the BBC and LMA had been quick to find a signal. I looked on the LMA’s website and this is the latest data I found. The data only records dismissals up to 31 March of the 2014/15 season. There were 16. The data in the report gives the total number of dismissals for each preceding season back to 2005/6. The report separates out “dismissals” from “resignations” but does not say exactly how the classification was made. It can be ambiguous. A manager may well resign because he feels his club have themselves repudiated his contract, a situation known in England as constructive dismissal.

The BBC’s analysis included dismissals right up to the end of each season including 2014/15. Reading from the chart they had 20. The BBC have added some data for 2014/15 that isn’t in the LMA report and not given the source. I regard that as poor data journalism.

I found one source of further data at website The Sack Race. That told me that since the end of March there had been four terminations.

Manager Club Termination Date
Malky Mackay Wigan Athletic Sacked 6 April
Lee Clark Blackpool Resigned 9 May
Neil Redfearn Leeds United Contract expired 20 May
Steve McClaren Derby County Sacked 25 May

As far as I can tell, “dismissals” include contract non-renewals and terminations by mutual consent. There are then a further three dismissals, not four. However, Clark left Blackpool amid some corporate chaos. That is certainly a termination that is classifiable either way. In any event, I have taken the BBC figure at face value though I am alerted as to some possible data quality issues here.

Signal and noise

Looking at the Championship data, this was the process behaviour chart, plotted as an individuals chart.

MgrSackingsto201503

There is a clear signal for the 2014/15 season with an observation, 20 dismissals,, above the upper natural process limit of 19.18 dismissals. Where there is a signal we should seek a special cause. There is no guarantee that we will find a special cause. Data limitations and bounded rationality are always constraints. In fact, there is no guarantee that there was a special cause. The signal could be a false positive. Such effects cannot be eliminated. However, signals efficiently direct our limited energy for, what Daniel Kahneman calls, System 2 thinking towards the most promising enquiries.

Analysis

The BBC reports one narrative woven round the data.

Bevan said the current tenure of those employed in the second tier was about eight months. And the demand to reach the top flight, where a new record £5.14bn TV deal is set to begin in 2016, had led to clubs hitting the “panic button” too quickly.

It is certainly a plausible view. I compiled a list of the dismissals and non-renewals, not the resignations, with data from Wikipedia and The Sack Race. I only identified 17 which again suggests some data quality issue around classification. I have then charted a scatter plot of date of dismissal against the club’s then league position.

MgrSackings201415

It certainly looks as though risk of relegation is the major driver for dismissal. Aside from that, Watford dismissed Billy McKinlay after only two games when they were third in the league, equal on points with the top two. McKinlay had been an emergency appointment after Oscar Garcia had been compelled to resign through ill health. Watford thought they had quickly found a better manager in Slavisa Jokanovic. Watford ended the season in second place and were promoted to the Premiership.

There were two dismissals after the final game on 2 May by disappointed mid-table teams. Beyond that, the only evidence for impulsive managerial changes in pursuit of promotion is the three mid-season, mid-table dismissals.

Club league position
Manager Club On dismissal At end of season
Nigel Adkins Reading 16 19
Bob Peeters Charlton Athletic 14 12
Stuart Pearce Nottingham Forrest 12 14

A table that speaks for itself. I am not impressed by the argument that there has been the sort of increase in panic sackings that Bevan fears. Both Blackpool and Leeds experienced chaotic executive management which will have resulted in an enhanced force of mortality on their respective coaches. That along with the data quality issues and the technical matter I have described below lead me to feel that there was no great enhanced threat to the typical Championship manager in 2014/15.

Next season I would expect some regression to the mean with a lower number of dismissals. Not much of a prediction really but that’s what the data tells me. If Bevan tries to attribute that to the LMA’s activism them I fear that he will be indulging in Langian statistical analysis. Will he be able to resist?

Techie bit

I have a preference for individuals charts but I did also try plotting the data on an np-chart where I found no signal. It is trite service-course statistics that a Poisson distribution with mean λ has standard deviation √λ so an upper 3-sigma limit for a (homogeneous) Poisson process with mean 11.1 dismissals would be 21.1 dismissals. Kahneman has cogently highlighted how people tend to see patterns in data as signals even where they are typical of mere noise. In this case I am aware that the data is not atypical of a Poisson process so I am unsurprised that I failed to identify a special cause.

A Poisson process with mean 11.1 dismissals is a pretty good model going forwards and that is the basis I would press on any managers in contract negotiations.

Of course, the clubs should remember that when they look for a replacement manager they will then take a random sample from the pool of job seekers. Really!

Deconstructing Deming XI B – Eliminate numerical goals for management

11. Part B. Eliminate numerical goals for management.

W. Edwards Deming.jpgA supposed corollary to the elimination of numerical quotas for the workforce.

This topic seems to form a very large part of what passes for exploration and development of Deming’s ideas in the present day. It gets tied in to criticisms of remuneration practices and annual appraisal, and target-setting in general (management by objectives). It seems to me that interest flows principally from a community who have some passionately held emotional attitudes to these issues. Advocates are enthusiastic to advance the views of theorists like Alfie Kohn who deny, in terms, the effectiveness of traditional incentives. It is sad that those attitudes stifle analytical debate. I fear that the problem started with Deming himself.

Deming’s detailed arguments are set out in Out of the Crisis (at pp75-76). There are two principle reasoned objections.

  1. Managers will seek empty justification from the most convenient executive time series to hand.
  2. Surely, if we can improve now, we would have done so previously, so managers will fall back on (1).

The executive time series

I’ve used the time series below in some other blogs (here in 2013 and here in 2012). It represents the anual number of suicides on UK railways. This is just the data up to 2013.
RailwaySuicides2

The process behaviour chart shows a stable system of trouble. There is variation from year to year but no significant (sic) pattern. There is noise but no signal. There is an average of just over 200 fatalities, varying irregularly between around 175 and 250. Sadly, as I have discussed in earlier blogs, simply selecting a pair of observations enables a polemicist to advance any theory they choose.

In Railway Suicides in the UK: risk factors and prevention strategies, Kamaldeep Bhui and Jason Chalangary of the Wolfson Institute of Preventive Medicine, and Edgar Jones of the Institute of Psychiatry, King’s College, London quoted the Rail Safety and Standards Board (RSSB) in the following two assertions.

  • Suicides rose from 192 in 2001-02 to a peak 233 in 2009-10; and
  • The total fell from 233 to 208 in 2010-11 because of actions taken.

Each of these points is what Don Wheeler calls an executive time series. Selective attention, or inattention, on just two numbers from a sequence of irregular variation can be used to justify any theory. Deming feared such behaviour could be perverted to justify satisfaction of any goal. Of course, the process behaviour chart, nowhere more strongly advocated than by Deming himself in Out of the Crisis, is the robust defence against such deceptions. Diligent criticism of historical data by means of process behaviour charts is exactly what is needed to improve the business and exactly what guards against success-oriented interpretations.

Wishful thinking, and the more subtle cognitive biases studied by Daniel Kahneman and others, will always assist us in finding support for our position somewhere in the data. Process behaviour charts keep us objective.

If not now, when?

If I am not for myself, then who will be for me?
And when I am for myself, then what am “I”?
And if not now, when?

Hillel the Elder

Deming criticises managerial targets on the grounds that, were the means of achieving the target known, it would already have been achieved and, further, that without having the means efforts are futile at best. It’s important to remember that Deming is not here, I think, talking about efforts to stabilise a business process. Deming is talking about working to improve an already stable, but incapable, process.

There are trite reasons why a target might legitimately be mandated where it has not been historically realised. External market conditions change. A manager might unremarkably be instructed to “Make 20% more of product X and 40% less of product Y“. That plays in to the broader picture of targets’ role in co-ordinating the parts of a system, internal to the organisation of more widely. It may be a straightforward matter to change the output of a well-understood, stable system by an adjustment of the inputs.

Deming says:

If you have a stable system, then there is no use to specify a goal. You will get whatever the system will deliver.

But it is the manager’s job to work on a stable system to improve its capability (Out of the Crisis at pp321-322). That requires capital and a plan. It involves a target because the target captures the consensus of the whole system as to what is required, how much to spend, what the new system looks like to its customer. Simply settling for the existing process, being managed through systematic productivity to do its best, is exactly what Deming criticises at his Point 1 (Constancy of purpose for improvement).

Numerical goals are essential

… a manager is an information channel of decidedly limited capacity.

Kenneth Arrow
Essays in the Theory of Risk-Bearing

Deming’s followers have, to some extent, conceded those criticisms. They say that it is only arbitrary targets that are deprecated and not the legitimate Voice of the Customer/ Voice of the Business. But I think they make a distinction without a difference through the weasel words “arbitrary” and “legitimate”. Deming himself was content to allow managerial targets relating to two categories of existential risk.

However, those two examples are not of any qualitatively different type from the “Increase sales by 10%” that he condemns. Certainly back when Deming was writing Out of the Crisis most OELs were based on LD50 studies, a methodology that I am sure Deming would have been the first to criticise.

Properly defined targets are essential to business survival as they are one of the principal means by which the integrated function of the whole system is communicated. If my factory is producing more than I can sell, I will not work on increasing capacity until somebody promises me that there is a plan to improve sales. And I need to know the target of the sales plan to know where to aim with plant capacity. It is no good just to say “Make as much as you can. Sell as much as you can.” That is to guarantee discoordination and inefficiency. It is unsurprising that Deming’s thinking has found so little real world implementation when he seeks to deprive managers of one of the principle tools of managing.

Targets are dangerous

I have previously blogged about what is needed to implement effective targets. An ill judged target can induce perverse incentives. These can be catastrophic for an organisation, particularly one where the rigorous criticism of historical data is absent.