Gauge & Bullet Charts
By: Darío Weitz
Tags: Big Data, Data Science, Business, Data Visualization, Storytelling

Business Intelligence (BI) is a set of methodologies and resources (theoretical concepts, algorithms, software, tools, technologies) used in business environments and whose fundamental objective is to transform information into knowledge. The purpose of Business Intelligence is to support better business decision making to improve the productivity and performance of any business organization.

BI relies heavily on a core set of analytics tools: Balanced Scorecards, Dashboards, and Key Performance Indicators.

The Balanced Scorecard (BS) is a management tool that allows the integration of short-term operational control with the long-term vision and strategy of the company under study. To this purpose, the BS formulates, implements, controls, and manages the organization’s strategy, efficiently linking strategic planning, operational management, and evaluation of group and individual performances.

Key Performance Indicators (KPIs) are the most important metrics that any organization needs to measure, monitor, and perform against in order to reach its strategic plan.

A Business Intelligence Dashboard is a tech-driven approach that uses data visualization tools to display KPIs in order to track the main factors that contribute to the operation and success of the business. It is a graphic representation of the main metrics involved in the pursuit of the objectives set by the company.

Business owners and corporate executives always want simple solutions. In that sense, data analysts must make dashboards easy to look at, navigate and understand. Two traditional graphics used to display KPIs on dashboards are Gauge Charts and Bullet Charts.

Let’s analyze them separately.

1.- Gauge Charts

AKA: Speedometer charts, Dial charts, Angular Gauge charts

WHY: widely used in Business Intelligence (BI) visualizations, particularly in dashboards, to indicate whether a data or measurement is within, below, or above a certain range of values. They are very simple graphics that manage to capture the attention of the audience very quickly, even the non-specialized one. They provide information about a single quantitative measurement, comparing its present value to a goal and to a series of ranges indicated by bands of different colors. The following figure indicates that the current sales value (420) is within a satisfactory range but 30 units below the predetermined goal.

Fig. 1: a standard gauge chart. Target is 450. Thresholds at 250 and 350.

HOW: Although there are different formats, the most common corresponds to the speedometer of a vehicle: a radial numerical scale separated into sectors, each identified by a particular color; a pointer or a needle moving over the radial scale; a minimum or lower limit value, a maximum or upper limit value and a target value. The number of ranges or sectors is variable, but usually there are three sectors, identified by a red color the sector that is below the lower threshold, a yellow color between thresholds, and a green one the sector that is above the upper threshold. It is not recommended to display more than five ranges or sectors.

The following are some of the possible qualitative values associated with the different colors: poor, satisfactory, and good; green for satisfactory, yellow for caution, and red for alarm; poor, average, and excellent; red for low performance, yellow for normal performance, and green for high performance. These ranges of qualitative values are widely used in a BI presentation or on a dashboard to show the business performance of a given company. The target value can be an objective to be achieved, a benchmark or a previous value to be exceeded.

Do not confuse gauges charts with donut graphs (Doughnut charts). While the former only reports a quantitative measurement, donut charts are related to a whole that can be divided into individual parts and how each part is related -in a relative or absolute way- with the total amount (Part of a whole analysis).

Gauge charts have several virtues when telling a story with data:

· They are very simple, easy to understand, and quickly capture the attention of the audience; · Audiences are familiar with them: car speedometers, indicators in computer games, temperature indication in ovens or stoves, etc. Unlike more complex graphics, there is no need to “waste time” in explaining them; · The sequence of colors, from red to green, is intuitive and immediate to understand; · Most business and corporate executives prefer them because they transmit key data simply, clearly, and immediately;

But they are also riddled with disadvantages, which must be taken into account at the moment of the storytelling:

· Their simplicity does not allow them to portray the context; · They often mislead the audience by omitting key information, resulting in poor storytelling. · Humans have difficulty comparing numerical values by means of angles; we always prefer to make comparisons by means of lengths starting from a common baseline (bullet charts); · They take up a lot of physical space and their format can be distracting to the audience. Dashboards and Scorecards are often cluttered with indicators, which makes the storytelling process much more complicated; · Remember that color blindness (color vision deficiency) affects approximately one in twelve men and one in two hundred women. The most common and problematic form of color deficiency is called “red-green color blindness,” precisely two of the colors commonly used in gauges. For these reasons, the actual trend is to encode the sectors as a single hue with distinct intensities ranging from dark to light. Darker colors intensities are used for low or poor values, while lighter colors intensities indicate satisfactory or excellent values (Fig. 2); The last warning comes from Robert Kosara (#1): “In data visualization, gauges simply solve the wrong problem. Business decisions cannot be made only on the current value of some measure, but need history and context. Values also typically do not change as fast, nor are as directly coupled, as the amount of pressure on the accelerator and the car’s speed.”

Fig. 2: Angular Gauge chart. Created with Vizzlo with permission (#2).

The Matplotlib library does not have a function that allows us to draw a gauge chart directly. A very ingenious solution to the problem can be found on the following website: https://waterprogramming.wordpress.com/2018/06/04/creating-shaded-dial-plots-in-python/. A simpler solution is based on the Plotly graphic library which has an Indicator function: https://plotly.com/python/gauge-charts/.