Decluttering and Focus: Data-Ink, Pre-attentive Attributes, Colour

Learning objectives

You can:

1. Compute an element census and a data-ink ratio for a chart before and after a rebuild, and state precisely what each figure does and does not prove.
2. Apply the eight-step delete order in sequence, naming what each step removes, what share of the non-data ink it recovers, and what information it costs.
3. Price a decoration in attention: state what a gridline, a border, a shadow, a gradient fill and a legend each cost, and compute which of them are visible at the back of a room.
4. Rank the pre-attentive attributes by decoding accuracy, and choose the encoding that carries your message at the viewing distance your audience actually sits at.
5. Apply the accent-plus-neutral formula to a chart of any number of series, and compute the emphasis ratio and the ink share it produces.
6. Choose between categorical, sequential and diverging palettes from the data type alone, and anchor a diverging scale where the data's meaning sits rather than at its mean.
7. Check contrast against the accessibility minimum in a light and a dark theme, and show why one accent value usually cannot serve both.
8. Test a palette for colour-blind safety by dichromat simulation and by the grey-scale test, and name the two encodings that make the hue question irrelevant.
9. Replace a legend with direct labels, computing the lookup cost the legend was imposing and the cases where a legend is still the right answer.
10. Decide between tidying a chart and redrawing it, and write the one-sentence annotation that carries the argument once the redraw is done.

Prerequisites & connections

Builds on. PR2.01, which taught you to pick the form from the message: bar for comparison across categories, line for change over ordered time, scatter for relationship, stacked bar for part-to-whole across few categories, slope chart for change between exactly two periods, small multiples wherever a dual axis was tempting. Everything below assumes that decision is already made and correct. From the mechanics branch, PR1.02's grid and type scale are assumed rather than retaught, and PR1.03's object mechanics are what you will use to execute a rebuild.

Feeds forward. PR2.03 takes a decluttered chart and gives it the action title, the single focal point, the small-multiple grid and the dashboard reading order, and it is the crown of this branch at a 0.90 gate. PR3.01 applies the same pre-attentive vocabulary to diagrams rather than to data. PR4.02 carries titles across a whole deck. The archetype nodes, PR5.01 to PR5.03, assume every exhibit in a consulting deck, a board pack or a research deck already passes the colour and declutter rules, and PR7.01 grades them at the capstone standard.

Six neighbours own material that borders this one, and none of it is rebuilt here. PR2.01 owns chart-form choice in full, including the permanent fails, the axis-baseline honesty rule and the sorting-as-argument idea; where a rebuild below changes a form, it names the rule and moves on. PR2.03 owns the action title, the focal point, small multiples and dashboards; titles appear below only as a given, because a chart cannot be decluttered toward a message that has not been written. PR1.02 owns alignment, distribution, grids, guides and the type scale, including the projection-distance argument for type size. PR1.03 owns objects, animation, export and the shipping checklist, including alt text and reading order as build habits. M10.03 owns exhibit design inside written deliverables, where a reader controls their own pace and a dense exhibit is legitimate. DA1.04 owns exploratory plotting in code, where the audience is you and the rules below are deliberately suspended.

1. Data ink, and the ratio that is a question rather than a score

Tufte's definition is simple enough to apply by hand. Take the ink used to print a graphic, split it into the ink that encodes data and the ink that does not, and form the ratio. Erase anything on the wrong side of the split.

Applied to the Nilaya Skincare funnel, with the plot area at 760 by 420 pixels and the page background excluded by convention, the arithmetic runs like this. Non-data ink totals 108,696 square pixels: 51,840 in bevels and drop shadows, 13,200 in the watermark, 9,100 in gridlines, 10,016 in the legend, 9,152 in duplicate data labels, and the rest in frames, ticks, axis lines and two titles. Data ink, the eight funnel segments themselves, totals 166,400. The ratio is 0.605.