What I learnt from creating The Data Visualisation Catalogue

What I learnt from creating The Data Visualisation Catalogue

What I learnt from creating The Data Visualisation Catalogue

Before I started working on The data Visualisation Catalogue, my Chart toolbox was limited. I knew of only simple charts like a Bar Graph, Pie Chart, Pictogram Chart, Proportional Area Chart, et cetera. Y’know, the type of charts that fit well onto an infographic. However, this felt insufficient and I had strong urge to expand my knowledge of data visualisation.

Previously, I had studied information design, lightly touching on data visualisation, and I had already worked on a few infographic projects; but since I had only recently graduated from my Bachelors a couple of years ago, my knowledge was still at a “beginners” level when it came to data visualisation.  I also wanted to expand my options when working with data and easily identify the best way to showcase my data.

In the past, the only guides I knew for selecting charts were Ralph Lengler’s and Martin J.Eppler’s A Periodic Table of Visualization Methods and Christian Behrens’ InfoDesignPatterns project (no longer online). However, I needed a reference tool of my own that I could adapt to suit my own needs.

Read Also:
The Impact of Big Data and Analytics on Manufacturing Companies

So initially, I began by collecting a list of different chart types into a spreadsheet. This only included simple bits of data, such as photo of the chart, its common name and links to webpages with more information on it.

Eventually, once I had enough time to spare, I went about designing and developing a website based on the research I had. 

But it was not enough to effectively describe how each chart works or why it’s useful.

I wanted to have some kind of system to help me select a chart, but my knowledge of what distinguished each chart from one another was limited.  So I thought, for now I don’t have any overarching system to organise these charts into, but I do know that they all serve some kind of function. So for the time-being, I organised charts based on the functions they perform. It wasn’t a perfect system, but I thought that as I learnt more about data visualisation and each chart type individually, I would eventually develop a better system.

Read Also:
MDM Needs Data Governance

To build a solid understanding of each chart, how they’re constructed and what they’re useful for communicating, I would need to dig deeper.

I started to research further into each chart type I had recorded, using a number of online and print sources, to keep it as objective as possible. Some sources provided a good description of how the chart is drawn, while others might provide insight on how the chart is useful.

 



Sentiment Analysis Symposium

27
Jun
2017
Sentiment Analysis Symposium

15% off with code 7WDATA

Read Also:
5 Keys to Leading in the Age of Analytics

Data Analytics and Behavioural Science Applied to Retail and Consumer Markets

28
Jun
2017
Data Analytics and Behavioural Science Applied to Retail and Consumer Markets

15% off with code 7WDATA

Read Also:
Jobs of the Future Will Require Data Analysis

AI, Machine Learning and Sentiment Analysis Applied to Finance

28
Jun
2017
AI, Machine Learning and Sentiment Analysis Applied to Finance

15% off with code 7WDATA

Read Also:
How to Become a Data Scientist – Part 1
Read Also:
Smart Data: 3 Reasons it's the Future of FinTech

Real Business Intelligence

11
Jul
2017
Real Business Intelligence

25% off with code RBIYM01

Read Also:
5 Keys to Leading in the Age of Analytics

Advanced Analytics Forum

20
Sep
2017
Advanced Analytics Forum

15% off with code Discount15

Read Also:
Finding the Right Data Analytics Software for Your Needs

Leave a Reply

Your email address will not be published. Required fields are marked *