Heat Maps

Heat Maps have been used for many years in the field of science. They provide two-dimensional colored matrices, with the objective to better present tabular data so that the audience can instantly see the big picture and spot outliers and groups very easily.
The example above shows a matrix of key financial metrics (overall growth, share of new products in % of revenue, overall market share, change in market share over the last three years, gross margins as a % of revenue, contribution margin, employee turn-over, and employee satisfaction), by regional sales district of this firm.
There are myriads of other applications as well: In a market assessment, you may look at several countries and map key macro-economic indicators. You could map competitors along a number of critical competitive metrics. The applications are really very broad.
The power of the heat map is to sort the lines, so that (in the example shown above) the districts with the most positive overall results show up at the top. You can potentially also sort the columns, which would give you “top left mostly green to bottom right mostly red” picture. But sometimes, the sorting doesn’t make much sense. In the example that I use, the columns are grouped by core categories (growth, market share, profitability, and capabilities), so it probably makes most sense to leave them in that specific order.