How to Visualise Readiness by Function, Country and Project Wave

The readiness pack arrives: fourteen slides, a stacked bar chart per question, a RAG table, and an executive summary saying readiness is “broadly on track with some areas requiring attention.”

Everyone nods. Nobody acts.

This is usually a visualisation problem rather than a data problem. The pack contains the finding — manufacturing has fallen off a cliff since the last wave — but has encoded it in a way that requires the reader to reconstruct it from fourteen slides. Nobody does that in a steering meeting.

Readiness data has three dimensions that leaders genuinely care about: function, country or site, and project wave. Getting all three into a readable form is a specific design problem with a small number of correct answers.

Start with the question, not the chart

Leadership asks three distinct questions of readiness data, and each needs a different form. Most packs answer none of them because they are organised by survey question rather than by decision.

• Where is the risk? → a grid you can scan. Heat map.
• Is it getting better or worse? → movement between waves. Slope or line.
• Is this group actually aligned, or split? → distribution, not average.

Build those three and you can delete most of the rest of the pack.

The three-dimension problem

Function, country and wave are three dimensions. A page is two.

So the rule is simple and rarely followed: put two dimensions in the grid and repeat the grid for the third. One small chart per country, identical in every respect except the data. Never attempt to encode the third dimension by stacking, nesting or adding a second colour scheme — that is the point at which readiness packs become unreadable.

Which two go in the grid depends on where your variation is. If the programme runs in one country with many functions, use function × wave and forget the third. If it runs across twelve countries with the same four functions, use country × function and repeat per wave.

The heat map, done properly

For “where is the risk,” nothing beats a grid of cells. Here is the form, with function down the side and wave across the top.

The eye goes to the bottom-right corner immediately, which is the point. Manufacturing has gone 66 → 58 → 47 and is now the most urgent conversation in the programme. That finding was in the fourteen-slide pack too. It just could not be seen.

Four rules make this work:

• Print the number in every cell. Colour finds the problem; the number states it.
• One hue, light to dark, and say which direction means bad. A legend reading “darker = lower readiness” removes all ambiguity at no cost.
• Sort by the current wave, worst at the bottom (or top — but sort). An alphabetical heat map wastes the strongest thing a grid can do.
• Show suppressed groups as suppressed. A blank cell looks like an error; “n/a — under 5 responses” is a statement about method that protects anonymity and your credibility.

Why not RAG

Red-amber-green is the default in transformation reporting and it is a poor instrument for readiness, for two independent reasons.

It fails a predictable slice of your audience. Red-green colour vision deficiency affects roughly one in twelve men of northern European descent. On a steering committee of twelve, the odds are decent that someone cannot reliably separate your red cells from your green ones. If colour is the only encoding, that person is reading a different chart from everyone else.

Three bins throw away most of the data. A function at 51 and a function at 79 are both “amber,” which is a 28-point difference rendered identical. Worse, the bands invite a conversation about whether something is really amber rather than about what to do — and boundary-negotiation is the least useful discussion a readiness pack can produce.

If governance requires a RAG column, keep it — but put the number beside it, and never let the colour travel alone.

What colour can and cannot do

There is measured evidence on this, and it is unusually actionable.

Cleveland and McGill’s experiments on graphical perception, published in the Journal of the American Statistical Association, ranked how accurately people decode different visual encodings. Position along a common scale is the most accurate. Length comes next, then angle and slope, then area. Shading and colour saturation come last.

That is not an argument against heat maps. It is an argument about what to ask them to do. A heat map is excellent at locating — drawing the eye to the corner of the grid that needs attention — and poor at comparing. Nobody can reliably tell you whether one shade of blue is eight points darker than another.

So: use colour to find the problem, and position to measure it. Once the heat map has told you that manufacturing and supply chain are the issue, the next chart should put those functions on a common scale where the reader can judge the gaps precisely. This is also why the number belongs in every cell — it restores the precision the encoding cannot carry.

Showing movement between waves

Level tells you where you are. Movement tells you whether anything you did worked, and it is the more decision-relevant of the two.

For three or four waves, a simple line or slope chart with one line per function is the right form — position on a common scale, the encoding people read most accurately. A few rules keep it honest:

• One y-axis, always. Never plot readiness and response rate on two scales in one chart; the alignment between them is arbitrary and invents a relationship that is not in the data. Two measures means two charts.
• Colour follows the function, not its rank. If supply chain is the second colour this wave, it is the second colour next wave too, even if it has moved from fifth place to first. Recolouring by rank misleads anyone who learned the chart last quarter.
• Label the lines you are talking about, not every point. Two or three direct labels at the right-hand end; let the rest recede.
• Beyond about six lines, stop. Fold the rest into “other functions” or split into small multiples. Eight overlapping lines is a decorative object, not a chart.

Movement is also where the number becomes interpretable at all. Nobody knows whether 67 is good; everybody understands 67 down from 74.

Country: use small multiples, not a bigger grid

The instinct with twelve countries is to add them as another layer — nested rows, grouped bars, a second colour dimension. It never reads.

Repeat the same small grid once per country instead, laid out in a row or a block. The reader learns the shape once and then scans twelve copies of it, which is a far cheaper cognitive operation than decoding a single complicated chart.

Three conditions make small multiples work, and breaking any one of them ruins the comparison:

• Identical scale in every panel. A panel with its own axis range is a different chart wearing the same clothes.
• Identical row order in every panel — ideally sorted once, globally, so the eye can move down the column.
• Identical colour bands across panels. Re-binning per country makes the grids incomparable, which was the entire purpose.

Sort the panels themselves by something meaningful — lowest current readiness first, or go-live date — rather than alphabetically.

The chart nobody produces: distribution

Every visualisation above shows an average, and averages hide the most important property of readiness data.

Bryan Weiner’s theory of organisational readiness for change treats readiness as a shared property — shared resolve, and shared belief in collective capability. A function at 68 might be uniformly moderate, or it might be half the team at 90 and half at 45. Those need completely different interventions and they produce an identical cell in your heat map.

The cheapest fix is one extra column: percentage of respondents below your threshold. “Supply Chain 64, and 34% below 50” is a different and much more useful statement than 64 alone. If you have room for a proper distribution strip per function, better still — but the single percentage captures most of the value for almost no space.

A minimum standard for the readiness pack

Rule	Why
Numbers visible on every coloured cell	Colour is the least accurate encoding; it locates, it does not measure
One hue, light to dark, direction stated	Removes ambiguity; avoids the colour-vision failure of RAG
Rows sorted by current readiness	The grid’s main advantage is wasted alphabetically
Change since last wave shown	The only figure that is interpretable without a benchmark
Small multiples for the third dimension	Never nest or double-encode; repeat the panel
Identical scale, order and bands across panels	Otherwise the panels cannot be compared
Distribution or % below threshold	The mean conceals splits, which readiness is defined by
Suppressed groups labelled, not blank	Protects anonymity and shows the method was applied
One y-axis per chart	Dual axes invent relationships that are not in the data

The test

Put your readiness pack in front of someone who has not seen it, give them fifteen seconds, and ask which group they would worry about first.

If they cannot answer, the problem is not that leadership is disengaged or that the data is inconclusive. The finding is in there. It has simply been encoded in a way that requires work to extract, and in a forty-minute steering meeting covering nine agenda items, nobody is going to do that work.

Good readiness visualisation is not about making the pack attractive. It is about making the most important finding impossible to miss.

For how to build the underlying number, see turning survey data into a readiness score leaders act on. For converting what the charts reveal into owned actions, see building a 30/60/90-day plan from readiness evidence and designing metrics that create decisions. The change readiness resources cover the wider measurement picture.