Demonstrating Editorial Focus and Learning About Your Data - Data Visualization: A Successful Design Process - page 51

It is best to accept that there will be a certain amount of iteration as you alternate
between the mindset of a data scientist and a journalist progressing both issues
simultaneously.
Data is our raw material, the principle ingredient in our creative recipe. Irrespective
of what we intend or hope to show through our visualization design, the data will
ultimately do the talking.
If we don't have the data we want, or the data we do have doesn't tell us what we
hoped it would, or the findings we unearth aren't as interesting as we wish them to
be there is nothing we can (legitimately) do about it. That is an important factor to
remember. No amount of 3D-snazzy-cool-fancy-design dust sprinkled on to a project
can change that.
An incomplete, error strewn or just plain dull dataset will simply contaminate your
visualization with the same properties. So, the primary duty for us now is to avoid
this happening, remove all guessing and hoping, and just get on with the task of
acquiring our data and immerse ourselves into it to learn about its condition, its
characteristics, and the potential stories it contains.
To achieve this, we must go through the often painful mechanics of data
familiarization and preparation; as follows:
Acquisition : First, you need to get hold of your data. As we have discussed, this
might already be provided to you from those commissioning the work. You might
have independently formed a sense of the specific subject dimensions on which
you require data. Alternatively, it may be that you have yet to focus beyond a
broad subject level. It all really depends on how well-defined your requirements
or intentions already are.
The places where you might acquire your data and the methods to accomplish it
will be something you will know best. It could come from origins such as these:
• Obtained from a colleague, client, or other third-party entity
• A download taken from an organizational system
• Manually gathered and recorded
• Extracted from a web-based API
• Scraped from a website
• Extracted from a PDF file (you have my sympathies)