Book Image

Data Visualization: a successful design process

Book Image

Data Visualization: a successful design process

Overview of this book

Do you want to create more attractive charts? Or do you have huge data sets and need to unearth the key insights in a visual manner? Data visualization is the representation and presentation of data, using proven design techniques to bring alive the patterns, stories and key insights locked away."Data Visualization: a Successful Design Process" explores the unique fusion of art and science that is data visualization; a discipline for which instinct alone is insufficient for you to succeed in enabling audiences to discover key trends, insights and discoveries from your data. This book will equip you with the key techniques required to overcome contemporary data visualization challenges. You'll discover a proven design methodology that helps you develop invaluable knowledge and practical capabilities.You'll never again settle for a default Excel chart or resort to "fancy-looking" graphs. You will be able to work from the starting point of acquiring, preparing and familiarizing with your data, right through to concept design. Choose your "killer" visual representation to engage and inform your audience."Data Visualization: a Successful Design Process" will inspire you to relish any visualization project with greater confidence and bullish know-how; turning challenges into exciting design opportunities.
Table of Contents (13 chapters)
Data Visualization: a successful design process
About the Author
About the Reviewers

Visualization as a discovery tool

One of the most compelling arguments for the value of data visualization is expressed in this quote from John W Tukey (Exploratory Data Analysis).

The greatest value of a picture is when it forces us to notice what we never expected to see.

Through visualization, we are seeking to portray data in ways that allow us to see it in a new light, to visually observe patterns, exceptions, and the possible stories that sit behind its raw state. This is about considering visualization as a tool for discovery.

A well known demonstration that supports this notion was developed by noted statistician Francis Anscombe (incidentally, brother-in-law to Tukey) in the 1970s. He compiled an experiment involving four sets of data, each exhibiting almost identical statistical properties including mean, variance, and correlation. This was known as "Anscombe's quartet".

Sample data sets recreated from Anscombe, Francis J. (1973) Graphs in statistical analysis. American Statistician, 27, 17–21

Ask yourself, what can you see in these sets of data? Do any patterns or trends jump out? Perhaps the sequence of eights in the fourth set? Otherwise there's nothing much of interest evident.

So what if we now visualize this data, what can we see then?

Image published under the terms of "Creative Commons Attribution-Share Alike", source:

Through the previous graphical display, we can immediately see the prominent patterns created by the relationships between the X and Y values across the four sets of data as follows:

  • the general tendency about a trend line in X1, Y1

  • the curvature pattern of X2, Y2

  • the strong linear pattern with single outlier in X3, Y3

  • the similarly strong linear pattern with an outlier for X4, Y4

The intention and value of Anscombe's experiment was to demonstrate the importance of presenting data graphically. Rather than just describing a dataset based on a selection of some of its key statistical properties alone, to make proper sense of data, and avoid forming false conclusions we need to also employ visualization techniques.

It is much easier to discover and confirm the presence (or even absence) of patterns, relationships, and physical characteristics (such as outliers) through a visual display, reinforcing the essence of Tukey's quote about the value of pictures.

Data visualization is about a discovery process, enabling the reader to move from just looking at data to actually seeing it. This is a subtle but important distinction.