Illustration by Eric Kim
What Is a Dashboard?
Ask 10 people who build business dashboards to define a dashboard and you will probably get 10 different definitions. For the purpose of this book, our definition is as follows:
A dashboard is a visual display of data used to monitor conditions and/or facilitate understanding.
This is a broad definition, and it means that we would consider all of the examples listed below to be dashboards:
● An interactive display that allows people to explore worker compensation claims by region, industry, and body part
● A PDF showing key measures that gets e-mailed to an executive every Monday morning
● A large wall-mounted screen that shows support center statistics in real time
● A mobile application that allows sales managers to review performance across different regions and compare year-to-date sales for the current year with the previous year
Even if you don't consider every example in this book a true dashboard, we think you will find the discussion and analysis around each of the scenarios helpful in building your solutions. Indeed, we can debate the definition until we are blue in the face, but that would be a horrible waste of effort as it simply isn't that important. What is important – make that essential– is understanding how to combine different elements (e.g., charts, text, legends, filters, etc.) into a cohesive and coordinated whole that allows people to see and understand their data.
Final Thought: There Are No Perfect Dashboards
You will not find any perfect dashboards in this book.
In our opinion, there is no such thing as a perfect dashboard. You will never find one perfect collection of charts that ideally suits every person who may encounter it. But, although they may not be perfect, the dashboards we showcase in the book successfully help people see and understand data in the real world.
The dashboards we chose all have this in common: Each one demonstrates some great ideas in a way that is relevant to the people who need to understand them. In short, they all serve the end users. Would we change some of the dashboards? Of course we would, and we weigh in on what we would change in the author commentary at the end of each scenario. Sometimes we think a chart choice isn't ideal; other times, the layout isn't quite right; and in some cases, the interactivity is clunky or difficult. What we recognize is that every set of eyes on a dashboard will judge the work differently, which is something you also should keep in mind. Where you see perfection, others might see room for improvement. The challenge all the dashboard designers in this book have faced is balancing a dashboard's presentation and objectives with time and efficiency. It's not an easy spot to hit, but with this book we hope to make it easier for you.
PART I
A STRONG FOUNDATION
Chapter 1
Data Visualization: A Primer
This book is about real-world dashboards and why they succeed. In many of the scenarios, we explain how the designers use visualization techniques to contribute to that success. For those new to the field, this chapter is a primer on data visualization. It provides enough information for you to understand why we picked many of the dashboards. If you are more experienced, this chapter recaps data visualization fundamentals.
Why Do We Visualize Data?
Let's see why it's vital to visualize numbers by beginning with Table 1.1. There are four groups of numbers, each with 11 pairs. In a moment, we will create a chart from them, but before we do, take a look at the numbers. What can you see? Are there any discernible differences in the patterns or trends among them?
Table 1.1 Table with four groups of numbers: What do they tell you?
Let me guess: You don't really see anything clearly. It's too hard.
Before we put the numbers in a chart, we might consider their statistical properties. Were we to do that, we'd find that the statistical properties of each group of numbers are very similar. If the table doesn't show anything and statistics don't reveal much, what happens when we plot the numbers? Take a look at Figure 1.1.
Figure 1.1 Now can you see a difference in the four groups?
Now do you see the differences? Seeing the numbers in a chart shows you something that tables and some statistical measures cannot. We visualize data to harness the incredible power of our visual system to spot relationships and trends.
This brilliant example is the creation of Frank Anscombe, a British statistician. He created this set of numbers – called “Anscombe's Quartet” – in his paper “Graphs in Statistical Analysis” in 1973. In the paper, he fought against the notion that “numerical calculations are exact, but graphs are rough.”
Another reason to visualize numbers is to help our memory. Consider Table 1.2, which shows sales numbers for three categories, by quarter, over a four-year period. What trends can you see?
Table 1.2 What are the trends in sales?
Identifying trends is as hard as it was with Anscombe's Quartet. To read the table, we need to look up every value, one at a time. Unfortunately, our short-term memories aren't designed to store many pieces of information. By the time we've reached the fourth or fifth number, we will have forgotten the first one we looked at.
Let's try a trend line, as shown in Figure 1.2.
Figure 1.2 Now can you see the trends?
Now we have much better insight into the trends. Office supplies has been the lowest-selling product category in all but two quarters. Furniture trends have been dropping slowly over the time period, except for a bump in sales in 2015 Q4 and a rise in the last two quarters. Technology sales have mostly been the highest but were particularly volatile at the start of the time period.
The table and the line chart each visualized the same 48 data points, but only the line chart lets us see the trends. The line chart turned 48 data points into three chunks of data, each containing 16 data points. Visualizing the data hacks our short-term memory; it allows us to interpret large volumes of data instantly.
How Do We Visualize Data?
We've just looked at some examples of the power of visualizing data. Now we need to move on to how we build the visualizations. To do that, we first need to look at two things: preattentive attributes and types of data.
Preattentive Attributes
Visualizing data requires us to turn data into marks on a canvas. What kind of marks make the most sense? One answer lies in what are called “preattentive attributes.” These are things that our brain processes in milliseconds, before we pay attention to everything else. There are many different types. Let's look at an example.
Look at the numbers in Figure 1.3. How