Demonstrating Editorial Focus and Learning About Your Data - Data Visualization: A Successful Design Process - page 56

This is a perfect demonstration of how important it is to handle data resolution
issues as early as possible so we know what treatment to apply to our data.
When you are faced with similar decisions, albeit perhaps rarely on the same scale,
you will typically have these options available to you:
• Full resolution : Plotting all data available as individual data marks.
• Filtered resolution : Exclude records based on a certain criteria.
• Aggregate resolution : "Roll-up" the data by, for instance, month, year, or
specific category.
• Sample resolution : Apply certain mathematical selection rules to extract
a fraction of your potential data. This is a particularly useful tactic during
a design stage if you have very large amounts of data and want to quickly
develop mock-ups or test out ideas.
• Headline resolution : Just showing the overall statistical totals.
Consolidating : When you originally access your data, you will likely believe,
or hope that you have everything you need. However, it may be that after the
examination and preparation work, you identify certain gaps in your subject matter.
Additional layers of data may be required to be combined ("mashed-up") with
our existing dataset, applied to perform additional calculations, or just to sit
alongside this initial resource to help contextualize and enhance the scope of our
communication. Always spend a bit of time considering if there is anything else
you anticipate needing to supplement your data to help frame the subject or tell
the stories you want to communicate.
Seasoned designers will confirm that acquiring, handling, and preparing your
data is often the most time-consuming and intensive activity involved in any
visualization project.