The design methodology described in this book is intended to be portable to any visualization challenge. It presents a sequence of important analytical and design tasks and decisions that need to be handled effectively.
As any fellow student of Operational Research (the "Science of Better") will testify, through planning and preparation, and the development and deployment of strategy, complex problems can be overcome with greater efficiency, effectiveness, and elegance. Data visualization is no different.
Adopting this methodology is about recognizing the key stages, considerations, and tactics that will help you navigate smoothly through your visualization project.
Remember, though, design is rarely a neat, linear process and indeed some of the stages may occasionally switch in sequence and require iteration. It is natural that new factors can emerge at any stage and influence alternative solutions, so it is important to be open-minded and flexible. Things might need to be revisited, decisions reversed, and directions changed. What we are trying to do, where possible, is find the best path through the minefield of design choices.
Some may feel uncomfortable at the prospect of following a process to undertake what is fundamentally an iterative, creative design process. But I would argue everyone should find value from working in a more organized and sequenced way especially if it helps to reduce inefficiency and wasted resource.
The design challenges involved in data visualization are predominantly technology related; the creation and execution of a visualization design will typically require the assistance of a variety of applications and programs. However, the focus of this methodology is intended to be technology-neutral, placing an emphasis on the concepting, reasoning, and decision-making.
The variety, evolution, and generally fragmented nature of software in this field (there is no single tool that can do everything) highlights the extra importance of reasoned decision-making, regardless of the richness and power individual solutions can offer.
Another key point to remark on is to emphasize, if it wasn't already clear, that data visualization is not an exact science. There is rarely, if ever, a single right answer or single best solution. It is much more about using heuristic methods to determine the most satisfactory solutions.
On that note, the content of the methodology intentionally avoids any sense of dogmatic instruction, preferring to focus on guidelines over explicit rules; sometimes an ounce of chaos, a certain license to experiment, a leaning on instinct, and a sense of randomness can spark greater creativity and serendipitous discovery.
The methodology is intended to be adopted flexibly, based on your own judgment and discretion, by simply laying out all the important things you need to take into account and proposing some potential solutions for different scenarios.
Finally, as I stressed with my definition of the subject earlier, I'm not suggesting this is a ground-breaking new take on the creative process. It is merely a personal interpretation based on experience and also exposure to the many brilliant people out there who share their own design narratives. It is, though, consistent with how most established observers of the subject would recommend you undertake this task. Moreover, it is an approach that I fundamentally believe works and it has genuinely helped me improve my own work since I've adopted it more deliberately, allowing me to cut through projects with the efficiency and elegance I've always yearned for.