Have you ever been asked to communicate data with someone but then not been sure of the best way to share the information with them? You are not alone! Choosing the right data communication type will help you present data in an appropriate way for your purpose and audience.
My Plastic Footprint started in an attempt to understand my plastic use. The process challenged my thinking on the role data visualisation has when communicating — should it make us care more about the things we measure? I hope this physical representation of data helps expand the idea of what a ‘data visualisation’ can be.
Sounds simple enough... but there are two parts to this request: the writing of the story, followed by its telling. To tell a data story, you have to write it first - unless you were born with the innate ability of story creation on the fly, and eat data for breakfast (but even then you’ll want to allow time for quality checks...).
The psychology of colour is fixed in us from an early age and businesses have a long-standing obsession with traffic light inspired data visualization. But how effective are these red-green dashboards when viewed by someone who can’t differentiate between these colours?
The importance and popularity of data storytelling have grown in recent years. The way in which data insights are communicated can determine how successfully they're used. While data storytelling began as a buzzword (and some would argue still is), it is generally agreed on as a term to describe the insight communication process. Data storytelling is a form of communication.
Adobe Illustrator is my preferred tool for data visualisation. The majority of my work focuses on data storytelling and this software works well in an area requiring very custom graphics. Experimenting with different tools is not something I do often - like many, I stick with what I know. But data visualisation tools have increased in both number and quality on what was available even a year ago. More tools are available for more effective visualisation.
Choosing the right graph type is an important part of any data visualisation project, with each graph usually following its own set of design rules. But the majority of graph types use only one or two basic design elements to display data. By understanding best practice visualisation of these elements, graphs can always be designed correctly.
Anyone working in analytics will be familiar with the Analytics Cycle, in some shape or form. It’s an Analyst’s version of the Scientific Method (perhaps even, justification of the term “data science”). The Analytics Cycle outlines the high-level process followed by Analysts to uncover data insights and ultimately add business value.