Yesterday in a meeting with our industrial partners I received yet another lesson. Simply put: though fancy and well-crafted visualization is useless if it doesn't help people take actions.
Ok I must admit it, this is maybe only true in business sectors (is it?) but what I come to realize is that we infovis enthusiasts are too much focused on the never ending refrain that visualization is useful to explore data and that we need it to make sense of things.
This is certainly true but this is only part of the story. Take the million managers out there. Not trained to cope with complex stats or charting tools but desperately in need to take decisions based on data. What do they need? To explore and make sense of thing? Sure, to some extent ... but ultimately to take complex decisions in a very constrained setting and tight time limits.
By not taking into account this perspective in our work as researchers and designers we miss a bunch of fabulous opportunities:
- Better constraints: If we take the ultimate business goal in mind when designing a visualization tool, we have additional constrains and constraints in design are not just good, they are fantastic! By having constraints we can focus on clear objectives and guide our work through them.
- Measures of success: If the tools we design help people make decisions, take action, and see the outcome, the measure of our success is suddenly clear: we are successful if our users/customers are able to take clever decisions in a short time, and ultimately if they have success with them. It reminds me the never aging and inspiring advice of Prof. Brooks in his Computer Scientist as Toolsmith essay:
"If we perceive our role aright, we then see more clearly the proper criterion for success: a toolmaker succeeds as, and only as, the users of his tool succeed with his aid. However shining the blade, however jeweled the hilt, however perfect the heft, a sword is tested only by cutting. That swordsmith is successful whose clients die of old age"
- Conquer market segments: If we are able to give people what people really need it is a win-win situation. They can do their work faster, better, with higher accuracy and we let out field thrive and become more known, more useful, more developed and more mature. Oooh and yes ... for those in academia like me: we should not underestimate the need to have successful products in the market coming out from our discipline. Our success depends also on them.
So in short, I believe an excessive focus on data exploration, sense making and the like is detrimental to our discipline and to the pool of our potential users. Don't get me wrong, I still believe data exploration and sense making are the cornerstone of visualization and by no means I am suggesting to abandon them. I just believe that taking decisions and actions in mind as guiding principles can add up something to what we have already in the box and create a more winning formula.
Data Mining vs. VisualizationOn a side note, I think it is useful to make a parallel between data visualization and data mining and understand how they differ, how they are perceived and why their success is different.
I don't think you can call me heretic if I say that Data Mining has had a far better success than visualization so far. And I think the main motivation resides on what I am suggesting here. The good thing about data mining and statistics is that they can produce better actionable knowledge than infovis. In a typical scenario, data mining can crunch some numbers and spit the response about which customers are more likely to respond to a marketing campaign. That simple: crunch some numbers, produce a list of prospective customers, send letters to them. The last point is what matters: "send letters to them", an action.
Note one very important thing: in data mining people don't even need to make sense of things to make decisions, they just need to have "reasonable" confidence on the quality of results. I agree that this is also the limit of this domain and that the excessive reliance on a black-box way of doing data analysis can be dangerous. But this is what works and the results are not bad then! If we want to evolve and become better we have to accept this state of things and create a better formula. Visualization has the power of opening the black box and at the same time retain the same power of the existing tools. But I don't see many solutions out there going into this direction.
I don't think that necessarily we have to make visualization tools that overlap with the goals covered by data mining but I'm totally sure that this shift in perspective can enormously help us making our infovis edge a lot sharper.
Do you agree? Or maybe disagree? Any comments? Suggestions?