Data visualisation software with packaged solutions for data graphing are designed to enable fast effortless creation of graphs. Search for “Data visualisation software” and you will see plenty of examples, some more well-known than others. This is their value proposition – to spit out data graphs without much prior thought from the user. This functionality may be desirable by those who need fast off-the-cuff reports but it is certainly not the way to go for analysing complex multidimensional data. Of course, these software offer more advanced features, but I do not agree with their list of defaults on the basis of the identified data properties.
In fact, I find this offering of default graphs to be a false pretense on behalf of these software. They give a misleading sense of proficiency. Do not be fooled by how quickly you can graph data using such software. You, the designer, must conceptualise the graph beforehand, either by having a mental image of you want to achieve or, even better, by having several competing forms.
“Writing takes thought. You can’t just plug your results into a computer program and hope to have readable, useful paragraphs. Similarly, graphics takes thought. You can’t just plug your results into a graphics program and hope to have readable, useful graphs”
As with any statistical estimator, also with a data graph estimator it holds that its design must be informed by certain principles and standards for evaluating accuracy in estimation. I collect these principles as qualities of data graphs.
Pen and paper
Although it is understandable for novices to rely on canned solutions from software to make graphs at first, over time and with plenty of practice you will learn that these packaged solutions are quite limited in their offering.
The greatest data graphs of all time have nearly always begun with a pen and a piece of paper or at least a mental image thereof (image courtesy of WikiHow):
This is how I start designing every new form of graph. I use the Graph Workflow model as a guide for drawing some doodles, which I then translate into visual implantations and retinal variables. Starting with pen and paper helps think out-of-the-box and design graphs with novel features. Some examples are provided in the homepage of Graph Workflow.