A data visualization is not the result of a data science project. A visualization is the tool by which the meaning(s) of a research project becomes instantaneously clear. As a Chief Data Officer you may ask, “What creates clarity?” Clarity is produced when the relationships between the important variables immediately come to life in the minds of the viewers. If the creator of the visual piece doesn't understand those relationships, then those relationships won't be encoded into the representation.
The causes for unhelpful visualizations fall mainly into two categories. One can misapply a function. I remember the first time I saw a scatter diagram from a social science paper with a line running through the points to indicate the correlation, except, there was no correlation. In the bad graphics category, I recently saw a presentation where the visualizer simply applied a heat map color palette to a table of data, and simply replaced the numbers in the table with scaled colors. It baffled and perplexed!
As the CDO, you will often have to pair subject matter expertise with computer graphical talent to produce visualizations that work. This is an important exercise for all involved. Subject matter experts might truly understand their data sets, but have no clue as to how to make them easy for non-experts to grasp. The graphics person makes the big contribution by drawing out meaning from the expert and translating it into a winning visualization.
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You can easily tell when a data science project has worked. The visualizations produced are immediately understandable, and their connections to the real world are undeniable. Short of this, the team did not succeed in delivering the promised insight.
If there's a place where a picture must be worth a thousand correct words, it's in the results of a data science project. Every word you need to use to explain your visualization gets subtracted from that value. If it takes 2500 words to explain your visualization, you've muffed it badly. You owe your customer at least 1500 words.
Worse than that, you may have demonstrated how little you yourself understand the problem you were hired to solve.
When you're working in the unknown, humility is the virtue that must guide you. In the unknown, the subjects you study are always waiting to punish you, and hand you your own head on a silver platter. But with a great data science team, you can all have each others' backs. You bring the Full Stack because you need all the help you can get to answer tough questions well.
Even in pure research contexts
it's all about problem solving.
Problem solving always begins with
careful problem characterization.
Innovation is the art of turning
a great solution into a great application.