Data Visualisation: A Handbook for Data Driven Design

by Andy Kirk

The process organises the activities into a sequence of manageable chunks so that the right things are tackled in the right order.

You will be aided by some additional practical tips and good habits to employ across the whole process.

The quality of your decision making is the main difference between a visualisation that succeeds and one that fails.

For those new to the field, one of the first things to grasp is the idea that any notion of perfect in data visualisation does not exist.

decision making is the key competency in data visualisation: namely, effective decisions, efficiently made.

These stages cover the preparatory work that informs what you are visualising, for whom and, crucially, why:

This stage is concerned with the how.

It incrementally leads you towards developing a solution, with each stage building on the previous one and informing the next.

Even if you are just re-producing the same report every month, no two instances of that report will involve the exact same context.

framework

A good process should offer adaptability and remove the inflexibility of a defined procedure.

A good process safeguards adaptability and cushions the impact of changing circumstances like these.

You will first encounter the need to define a profile of your anticipated audience’s characteristics during the first stage of the process, ‘Formulating your brief’. However, the concern about what they know, what they need to know, and how interested they will be will reoccur right through to the end. Concerns like these should never drop off your radar.

That said, each project introduces its own profile of demands, so always find time before you set off to estimate where your likely commitments will be most required.

defining your audience’s needs, reasoning your editorial perspectives, and making decisions about viable design choices.

holding discussions with a client, or checking data.

handling data, creating charts, and designing presentation features.

Occupying this state helps heighten your imagination and thought process.

Alpha state

It helps you document important details such as:

wish lists of features or ideas you would like to explore;

Add this to the beginnin of the notebook

It is about listening to stakeholders and to your audience: what do they want, what do they expect, what ideas do they have?

Add communication sessions for different purposes

presenting ideas, updat- ing on progress, seeking feedback, sharing your thoughts about possible solutions,

‘Because I speak the language of data, I can talk pretty efficiently with the experts who made it.

I find if you listen, people talk.

I find if you ask an insightful question, something that makes them say “oh, that’s a good point,” the whole conversation opens up.

You cannot know everything about your subject, about the meaning of your data, about the relevant and irrelevant qualities it possesses.

Data, without interpretation, is just a jumble of words and numbers – out of context and devoid of meaning.

They should be a proactive problem solver, identifying potential pitfalls and providing various roadmaps for overcoming them.

The process you follow embodies the concept of the ‘aggregation of marginal gains’.

When something is not working, learn to kill it.

What is presented in this book is proposed, not imposed. If you cannot get this approach to fit your personality, your project’s purpose, or the rhythm of how you need to work with others, modify it. We are all different.

framework

to forge initial clarity about the context and vision of your work.

The manifestation of a brief can be as informal or as formal as your situation requires.

a source of mutual understanding,

especially for matters to do with the expected deliverables.

The primary task of this stage is to establish why you are producing this data visualisation.

the influential contextual matters around the who, the where and the when.

early conceptual thinking about what it is you might be developing,

providing early clues about the best-fit tone, functional experience and style your visualisation may need to demonstrate.

what are you trying to accomplish? The type of understanding you are facilitating is important.

harnessing instinctive ideas that form in our minds, concerning the keywords, imagery, metaphors and external inspiration that might be relevant to the subject.

before any data is shared and certainly before any design work is commenced, a curiosity has formed:

Any visualisation work undertaken in the absence of a definable curiosity will lead to an uncertain and aimless decision-making process.

A key attribute of any curiosity is to recognise from whom it originates.

This type of work is often characterised as being a ‘pet’ or ‘passion’ project that is entirely self-initiated,

In these situations, you are inheriting their interest and you have to own it from then on.

That said, you should still seek constant dialogue with your stakeholder if you strongly feel another route might be more interesting.

Otherwise, you will need to make reasonable judgements to anticipate what most people will likely find most interesting.

reasonable to expect you to be aware of all the potential features of interest about your subject or in your data.