Data Visualization and Data Visualization Workflow Read Chapter 2 attached in the question and answer the below question with APA formatting.ASSESS YOUR CU
Data Visualization and Data Visualization Workflow Read Chapter 2 attached in the question and answer the below question with APA formatting.ASSESS YOUR CURRENT WORKFLOWReflect on your most recent visualisation project and try to sketch or write out the approach you took. What stages of activity did you undertake and in what sequence? Did it feel efficient or chaotic? Was it interrupted by changes, uncertainty or a sense of too much choice? Before you can seek to improve your ongoing approach it is worth unpicking what you currently do and how you do it.Additional content for completing this assignment – http://book.visualisingdata.com/chapter/chapter-2 2 Visualisation Workflow
Clear, effective and efficient thinking is the critical difference between a visualisation that succeeds and one that
fails. You cannot expect just to land accidentally on a great solution. You have got to work for it.
In this chapter I will outline the data visualisation workflow that forms the basis of this books structure and
content. This workflow offers a creative and analytical process that will guide you from an initial trigger that
instigates the need for a visualisation through to developing your final solution.
You will learn about the importance of process thinking, breaking down the components of a visualisation
design challenge into sequenced, manageable chunks. This chapter will also recommend some practical tips and
good habits to ensure the workflow is most effectively adopted.
2.1 The Importance of Process
As I have already established, the emphasis of this book is on better decision making. There are so many
different things to think about when creating a data visualisation, regardless of whether the output will be the
simplest of charts or the most ambitious of multi-faceted digital implementations.
The decisions you will face will inevitably vary in the weight of their significance. There will be some big
choices matters like defining your editorial angles and selecting the best fit chart type and many seemingly
small ones such as picking the precise shade of grey for a charts axis labels. The process of creating a
visualisation generally follows the Pareto principle, whereby 20% of decisions made have implications for
about 80% of the final visible design. However, just because some decisions will appear more significant in the
final output, as visualisers we need to attend to every single decision equally, caring about detail, precision and
accuracy.
To repeat, one of the main mental barriers to overcome for those new to the field is to acknowledge that the
pursuit of perfect in data visualisation is always unfulfilled. There are better and there are worse solutions, but
there is no perfect. Perfect exists in an artificial vacuum. It is free of pressures, has no constraints. That is not
real life. There will always be forces pushing and pulling you in different directions. There may be frustrating
shortcomings in the data you will have to work with or limitations with your technical capabilities. As
discussed, people your audience members introduce huge inconsistencies. They we are complex,
irrational and primarily different. Accepting the absence of perfection helps us unburden ourselves somewhat
from the constant nagging sense that we missed out on discovering the perfect solution. This can prove quite
liberating.
That is not to say our ambitions need to be lowered. Quite the opposite. We should still strive for best, the
absolute optimum solution given the circumstances we face. To achieve this requires improved effectiveness
and efficiency in decision making. We need to make better calls, more quickly. The most reliable approach to
achieving this is by following a design process.
The process undertaken in this book is structured around the following stages (Figure 2.1).
F igure 2.1 The Four Stages of the Visualisation Workflow
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Here are a few observations about this process ahead of its commencement.
Pragmatic: This process aims to provide a framework for thinking, rather than instructions for
learning. As described in the Introduction, there are very few universal rules to rely upon. While the
comfort provided by rules is what many might seek at the beginning of their learning journey, flexible
pragmatism beats dogmatism in any situation. Useful rules do exist in visualisation but are often related
to quite micro-level matters. I will come to discuss these in due course.
R educing the rando mness o f yo ur appro ach: The value of the process is that it guides your
entry and closing points: where and how to begin your work as well as how and when it will be
finished. When you are new to data visualisation, the sheer extent of things to think about can be quite
an overwhelming prospect. This workflow approach aims to break down activities into a connected
system of thinking that will help to organise and preserve the cohesiveness of your activities. The process
incrementally leads you towards developing a solution, with each stage building on the last and
informing the next. The core purpose of the approach is to give you a greater sense of the options that
exist at each stage and provide you with better information with which to make your choices.
Pro tect experimentatio n: The systematic approach I am advocating in this book should not be
seen as squeezing out the scope for creativity or eradicating any space for experimentation. It is natural to
want to reduce wasted effort, but at the same time it is absolutely vital to seek opportunities in the
right places for imagination to blossom. In reality, many of the projects you will work on will not
necessarily rely on much creative input. There will be projects that have pressures on time and a need
to compromise on experimenting in favour of the desire for efficiency. There will be subjects or datasets
that you work with that are just not congruent with overt creative thinking. It is about striking a
balance, affording time on those activities that will bring the right blend of value to suit each context.
I truly feel that experimentation (even for the sake of experimentation) is important, and I would strongly
encourage it. There are infinite possibilities in diagramming and visual communication, so we have much to
explore yet. I think a good rule of thumb is to never allow your design or implementation to obscure the
reader understanding the central point of your piece. However, Id even be willing to forsake this, at times,
to allow for innovation and experimentation. It ends up moving us all forward, in some way or another.
Kennedy Elliott, Graphics Editor, The Washington Post
F acilitate adaptability and iteratio n: This workflow is characterised as a design process rather
than a procedure. A good process should facilitate the adaptability and remove the inflexibility of a
defined procedure of operation. Although the activities are introduced and presented in this book in a
linear fashion, inevitably there is much iteration that takes place. There will be times when you will have
to revisit decisions, maybe even redo activities in a completely different way given what you have
discovered further down the line. If you make mistakes or bad calls and everyone does it is
important to fail gracefully but recover quickly. You will need to be able to respond to changes in
circumstances and accommodate their impact fast. A good process cushions the impact of situations
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arising like this.
T he first o ccasio n, no t the last: It is important to note that the tasks you face at each stage in
the process will represent the first occasion you pay attention to these matters, but not the final
occasion. There is something of a trickle-down effect here. Many of the concerns you will be faced with
at the start of a challenge will likely continue to echo in your thoughts right through to the end. Some
things are just not possible to close off that easily. Take the ongoing demands of profiling who your
audiences are and what they might need. That thinking starts early and should actually never drop off
your radar. The nature of the process gives you the best chance of keeping all the plates spinning that
need to be spun, knowing which ones can be left to drop and when.
Alw ays the same pro cess: The range of visualisation challenges you will face in your career will
vary. Even if you are producing the same work every month, no two projects will provide the same
experience: just having an extra month of data means it has a new shape or size. It is different. Some
projects you work on will involve fairly simple data, others will involve hugely complex data. In some
cases you will have perhaps two hours or two days to deliver a solution, in others you might have two
months. The key thing is that the process you follow will always require the same activities in the same
sequence, regardless of the size, speed and complexity of your challenge. The main difference is that any
extremes in the circumstances you face will amplify the stresses at each stage of the process and place
greater demands on the need for thorough, effective and timely decision making.
Partitio ning yo ur mindset: Within each of the sequenced stages listed in Figure 2.1 there will be
different demands on your mindset: sometimes you are thinking, sometimes you are doing, sometimes
you are making. When you are working alone, especially, it is important to appreciate the activities that
will require different mindsets:
Thinking: The duties here will be conceptual in nature, requiring imagination and judgment,
such as formulating your curiosity, defining your audiences needs, reasoning your editorial
perspectives, and making decisions about viable design choices.
Doing: These are practical tasks that will still engage the brain, obviously, but manifest
themselves through more hands-on activities like sketching ideas and concepts, learning about a
subject through research, gathering and handling your data.
Making: These involve the constructive and technical activities that specifically relate to the
production cycle as you face the challenge of translating promising, well-considered design
concepts into effective, working solutions.
You need a design eye to design, and a non-designer eye to feel what you designed. As Paul Klee said, See
with one eye, feel with the other. Oliver Reichenstein, founder of Information Architects (iA)
2.2 The Process in Practice
Throughout this book I will call out key points of advice in the form of useful tips, tactics or good habits you
should be looking to consider employing. Many of these have been informed by interviews with some of the
brilliant people working in this field today. As you are about to commence the design workflow here are some
pieces of advice that transcend any individual stage of the process.
M anaging pro gress and reso urces: Good planning, time and resource management keep a
process cohesive and progressing. They represent the lubricant. You will rarely have the luxury of
working on a project that has no defined end date and so adhering to imposed or self-imposed
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timescales is especially important. It is very easy to get swallowed up by the demands of certain activities,
particularly those involved in the working with data stage. Similarly the production cycle (which takes
place during and beyond Part C), as you iterate between idea, prototype and construction, can at times
appear never to have an end in sight. As one task is finished, another two always seem to appear. As you
get closer to a deadline you will either sink or swim: for some the pressure of time is crippling; others
thrive on the adrenaline it brings and their focus is sharpened as a result. Recognising the need to factor
in time for some of the broader responsibilities clerical tasks, arranging demo meetings and skype calls,
file management and running backups will prove hugely beneficial by the end.
R o o m to think : On the theme of task duration and progress, it is important to build in the capacity
to think. The notion of brain states is relevant here, in particular the alpha state which kicks in most
commonly when we are particularly relaxed. Being in this state helps to heighten your imagination,
learning and thought process. Apologies for the mental image but I do some of my most astute thinking
in the shower or bath, and just before going to sleep at night. These are the occasions when I am most
likely drifting into a relaxed alpha state and help me to contemplate most clearly the thoughts and ideas
I might have. I find train or air travel achieves the same as does lying on a beach. Unfortunately in the
latter scenario I just dont care enough about work in that moment to note down my frequent genius
ideas (what do you mean which ones?). If I have a task that will take two days of my time but the
deadline is further away, I typically try to break down the time I give to it across smaller clusters of three
to four hours spread across four days of activity in order to create sufficient opportunities for my brain
to tick over during the intervening gaps and hopefully allow good ideas to ferment.
H euristics to suppo rt decisio ns: As I have discussed, there will be occasions when the best
choice does not present itself, when time is pressurising you and when you will need to make a call. You
might have to occasionally rely on heuristic techniques that help to speed up your decision-making at
certain stages. Although this might seem an unsatisfactory tactic to consider, given the previously stated
need to eliminate arbitrary choices, heuristics can remain consistent with this desire when they rely on
educated, intuitive or common-sense judgements. As you develop your experience, the astuteness of
such heuristic judgments will be increasingly reliable to fall back on when the need arises.
Pen and paper: The humble pairing of pen and paper will prove to be a real ally throughout your
process. I will not over-sentimentally claim this is the most important tool combination because, unless
you are producing artisan hand-drawn work, you will have other technical tools that would probably
rise up the importance list. However, the point here is that capturing ideas and creating sketches are a
critical part of your process. Do not rely on your memory; if you have a great idea sketch it down. This
activity is never about artistic beauty. It does not need you to be an artist, it just needs you to get things
out of your head and onto paper, particularly if you are collaborating with others. If you are incredibly
fortunate to be so competent with a given tool that you find it more natural than using pen and paper
to sketch ideas quickly, then this is of course absolutely fine, as long as it is indeed the quickest
medium to do so.
No te-tak ing: Whether this is via pen and paper, or in Word, or a Google doc, note-taking is a vital
habit to get into. This is about preserving records of important details such as:
information about the sources of data you are using;
calculations or manipulations you have applied to your data;
assumptions you have made;
data descriptions, particularly if explanations have been offered to use verbally by somebody who
knows the data well;
questions you have yet to get answers to;
the answers you did get to your questions;
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terminology, abbreviations, codes things you need to remember the meaning and associations in
your data;
task lists and wish lists of features or ideas you would like to consider pursuing;
issues or problems you can foresee;
websites or magazines that you saw and gave you a bit of inspiration;
ideas you have had or rejected.
Note-taking is easier said than done, and I am among the least natural note-takers to roam this Earth, but I
have forced it into becoming a habit and a valuable one at that.
Because I speak the language of data, I can talk pretty efficiently with the experts who made it. It doesnt
take them long, even if the subject is new to me, for them to tell me any important caveats or trends. I also
think thats because I approach that conversation as a journalist, where Im mostly there to listen. I find if
you listen, people talk. (It sounds so obvious but it is so important.) I find if you ask an insightful question,
something that makes them say oh, thats a good point, the whole conversation opens up. Now youre both
on the same side, trying to get this great data to the public in an understandable way. Katie Peek, Data
Visualization Designer and Science Journalist
C o mmunicatio n: Communication is a two-way activity. Firstly, it is about listening to stakeholders
(clients or colleagues) and your audience: what do they want, what do they expect, what ideas do they
have? In particular, what knowledge do they have about your subject? Secondly, communication is
about speaking to others: presenting ideas, updating on progress, seeking feedback, sharing your
thoughts about possible solutions, and promoting and selling your work (regardless of the setting, you
will need to do this). If you do not know the intimate details of your subject matter you will need to
locate others who do: find smart people who know the subject better than you or find smart people
who do not know the subject but are just smart. You cannot avoid the demands of communicating so
do not hide behind your laptop get out there and speak and listen to people who can help you.
R esearch: Connected to the need for good communication is the importance of research. This is an
activity that will exist as a constant, running along the spine of your process thinking. You cannot know
everything about your subject, about the meaning of your data, about the relevant and irrelevant
qualities it possesses. As you will see later, data itself can only tell us so much; often it just tells us where
interesting things might exist, not what actually explains why they are interesting.
Research is key. Data, without interpretation, is just a jumble of words and numbers out of context and
devoid of meaning. If done well, research not only provides a solid foundation upon which to build your
graphic/visualisation, but also acts as a source of inspiration and a guidebook for creativity. A good
researcher must be a team player with the ability to think critically, analytically, and creatively. They should
be a proactive problem solver, identifying potential pitfalls and providing various roadmaps for overcoming
them. In short, their inclusion should amplify, not restrain, the talents of others. Amanda Hobbs,
Researcher and Visual C ontent Editor
Attentio n to detail: Like note-taking, this will be something that might not be a natural trait for
some but is so important. You cannot afford to carry any errors in your work. Start every project with
that commitment. This is such an important ingredient to securing trust in your work. The process you
are about to learn is greatly influenced by the concept of aggregation of marginal gains. You need to
sweat the small stuff. Even if many of your decisions seem small and inconsequential, they deserve your
full attention and merit being done right, always. You should take pride in the fine detail of your design
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thinking, so embrace the need for checking and testing. If you are so immersed in your work that you
become blind to it, get others to help call on those same smart people you identified above. As
someone who once published a graphic stating Irans population was around 80 billion and not 80
million, I know how one tiny mistake can cause the integrity of an entire project to crumble to the
ground. You do not get a second chance at a first impression, somebody once said. I forget who, I
wasnt paying attention
M ak e it w o rk fo r yo u: The only way you will truly find out whether a process works for you is if
you practise it, relentlessly. As I have stated, every project will be different even if only in small ways.
However, if you just cannot get the approach presented in this book to fit your personality or purpose,
modify it. We are all different. Do not feel like I am imposing this single approach. Take it as a
proposed framework based on what has worked for me in the past. Bend it, stretch it, and make it
work. As you become more experienced (and confident through having experienced many different
types of challenges) the many duties involved in data visualisation design will become second nature, by…
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