Issue 8: What does 'good' look like? And why?

Welcome back to the Radical Pragmatist...a monthly newsletter for thoughtful practitioners designed to inform, stimulate and enlighten.

Over the last couple of newsletters I have criticised conventional reporting practice on two counts.

Firstly focussing on single data points means that we are unable to distinguish between meaningful signals and noise. We make this worse by comparing data with arbitrary and often politically charged fixed targets. As a result we can mislead and confuse.

Focussing on single data points means that we are unable to distinguish between meaningful signals and noise. We make this worse by comparing data with arbitrary and often politically charged fixed targets.

Second, by using numerical tables to present the results we compound our crime, because it is inherently difficult for our brains to assimilate and make sense of dynamic performance data when it is presented in this form.

It is inherently difficult for our brains to assimilate and make sense of dynamic performance data when it is presented in tabular form.

But, even if you are inclined to agree with me, you might think that I landed a few cheap shots.

My goal today is to back up my claims by showing you how you can use these insights to radically enhance the quality of your reports.

Take a look at the chart below.

This chart was produced using exactly the same data that was used to create the table that was the object of my criticism in the last newsletter.

First, let me explain how the chart works.

This chart uses a measure called Moving Annual Totals; MAT for short.

Each data point used to construct the lines on the chart comprises the total actual or planned revenue (in this case) for the previous twelve monthly periods. Hence the name.

Thus period 14 represents the revenue for periods 2 to 14, and period 15 the total for 3-15, and so on. Plots of actual revenue (shown in red) over time give you a view of the trend in performance which can easily be enhanced by comparing it with the MAT for plans or forecasts calculated in the same manner (shown in blue).

Now try and answer the same four questions that we struggled with last time.

1. How good is performance?
2. Are things getting better or worse?
3. Has anything changed recently?
4. How credible is the plan for Year 3?

Referring back to the chart, I hope that you will now clearly see that after a period of rapid growth actual revenue peaked in about period 6 and then declined slightly. So, the answer to questions 1-3 is that the current level of performance is weak since it is below both the plan and previous levels of revenue growth with the reversal in fortunes commencing six periods ago.

And this begs another question, 'why'? - a step on the journey to actionable insights.

Looking forward rather than back, the plan for year three assumes that the recent trend will change around period 17, growing thereafter at roughly the same rate as that experienced before period 6. Thus, the credibility of this plan (question 4) rests on the answer to the questions 'what will happen in period 17 to cause the current trend to change?' and 'why do we think that we can grow rapidly subsequent to this?'

I hope you find this simple and straightforward. Perhaps so much so that you feel that I have patronised you.

If so, good. It has worked!

I hope you find this simple and straightforward. Perhaps so much so that you feel that I have patronised you.

If so, good. It has worked!

The question is: why is it so easy to make sense of data when presented in this way?

Firstly, the measures have been carefully chosen.

Using MAT's dampens the noise in the original data. But because we have used a series of totals we have not 'discarded' any data, unlike what we did the we just looked at the number for the last financial quarter and year end. As a result we can clearly see how trends have evolved.

And, although this is not obvious, by choosing an annual moving range we have eliminated the impact of repetitive annual effects such as the impact of the seasons, events such as Christmas and any accounting machinations around financial period ends (a significant source of distortion in many businesses).

Another factor that will contribute to its 'legibility' is that I have chosen to use an annual moving total rather than an average because I believe my audience is much more likely to already have an idea of what a good (or bad) annual total for revenue looks like than the equivalent average.

Secondly, from a presentational point of view, by using a line chart I have exploited the fact that our brains have evolved to rapidly recognise and make sense of shapes. In particular, we are hypersensitive to movement and trajectory so it is easy for us to parse the line charts into brain food. And the use of different colours makes it easy to distinguish between the two different types of data.

You may also notice that there are relatively few superfluous lines on the chart, such as vertical gridlines or frames around the chart area. This is because they do not code for, or help us interpret, data and so they have been eliminated. They are just 'junk', according to Edward Tufte, the data visualisation guru.

The net result is that the meaning of the chart is obvious at a single glance. Compare that with the the table I shared in the last newsletter which we struggled to wrestle any sense out of even after 12 passes of our eyes.

So, this kind of analysis generates clearer insights and it is more efficient. Just as important, the chances of different decision makers drawing different conclusions from the same data set has been drastically reduced.

And it is not an accident. This approach to reporting can be replicated and scaled.

That is because what might, at first glance, look like a trivial and inconsequential piece of visualisation is actually a manifestation of an approach founded on a deep appreciation of the statistical properties of data and the practical application of neuroscientific insights.

What might at first glance, look like a trivial and inconsequential piece of visualisation is actually a manifestation of an approach founded on a deep appreciation of the statistical properties of data and the practical application of neuroscientific insights.

Was this helpful?

If so, stay tuned for more insights from 'Present Sense'. And if you want to learn more, check out my LinkedIn page. And you will find my blog at?Satoripartners.co.uk?where you can also buy a hard or electronic copy of the book.

And remember to subscribe to this newsletter if you haven't already. And please pass this on to anyone who you think might benefit.