Mechanics for the Formula 1 of Science

I could not resist writing this short blog post after having a such a nice conversation with Scott Davidoff yesterday. Scott is a manager at the Human Interfaces Group at NASA JPL and he leads a group of people that takes care of big data problems at NASA (I mean big big data as those coming from telescopes and missions).

While on the phone he said:

You know Enrico … the way I see it is that we are mechanics for scientists … the same way Formula 1 has mechanics for their cars“.

What a brilliant metaphor! Irresistible. It matches perfectly my philosophy and at the same time, sorry to say, I think it does not match very well with the way most people see vis right now.

It reminds me the brilliant “Computer Scientist as a Toolsmith“, the fantastic essay written by Fred Brooks (ACM Turing Award) which I have adopted a long time ago as my personal manifesto. Fred Brooks advocated for a different way to see the role of Computer Science (one I am sure many of my colleagues refuse) as an engineering discipline whose purpose is to provide services to scientists. He famously stated that:

IA > AI (Intelligence Amplification can beat Artificial Intelligence).

That is, a machine and a mind can beat a mind-imitating machine working by itself.

And this all reminds me why I do what I do and why I think we should do more. Much more. In 2011 I was invited at Visualizing Europe, and event organized by Visualizing.org, and I gave a talk that pretty much covered the same ground: “Data Visualization is NOT Useful. It’s Indispensable“.

Talking with Scott, once again I realized how many people out there need our help. These are the people who may discover the next cure for cancer, help us going to Mars, find a way to preserve our planet, prevent terrorist attacks or disasters, just to name a few. You may think these people already have the necessary knowledge, means and skills to tackle big data problems on their own but you are wrong. These people are busy with their science, and for a good reason!

All these people need us! Let me repeat it: all these people need us! It’s up to us to show them what they can do with our tools and skills. Most of them simply do not imagine how powerful some of the things we do may be for them.

Let me tell you one thing: I have collaborated with a few scientists in my career so far and they love it when we make their life easier. Often they are blown away by simple trick we take for granted.

So if you are passionate about data and data visualization I urge you to think about this: you can decide to tackle hard problems with data. You can decide to make a big difference with pairing up with people who deal with hard scientific problems and help them make progress. It’s up to you to make this choice.

C’mon!

My biggest ambition is to be a mechanic. A mechanic for the the Formula 1 of science.

And you?

 

7 thoughts on “Mechanics for the Formula 1 of Science

  1. sprugman

    I love this idea. As someone working with big data in the advertising industry, I sometimes wonder how to go about putting my skills to use in a more meaningful way. Any pointers you may have would be interesting to read…

    1. FILWD

      Hey sure! I suggest you start from Fred Brooks essay linked above. You may also want to read Tools for Thought by Howard Rehingold. It’s a great summary of many of these ideas. If you want something a bit more academic and recent I suggest you to read “Illuminating the Path” by Jim Thomas and Kristin Cook a visual analytics agenda and the European response to this: “Mastering the information age: solving problems with Visual Analytics” by Keim et al.

      1. sprugman

        Heh. Those are great suggestions, but was thinking more practically in terms of how to find opportunities to collaborate with scientists. :-)

        1. FILWD

          Ah! I see … well … that’s the problem we all have! :) You have to knock their door. Convince them you can bring some value, which is not easy at all, they are interested in science not learning how to use yet another tool. It’s hard. See this paper here: http://www.cs.utah.edu/~miriah/publications/vis-collab.pdf. And you can fail a lot as often a collaboration seems useful but it goes nowhere. I don’t know, unfortunately I think there’s no silver bullet.

  2. M___Townsend

    Thank you for this thoughtful post: it is worth remembering, as Fred Brooks had also argued, that our real work is help our colleagues in other domain areas to do their work better. Now… how do we better connect practitioners in the field with people who need Vis services? What more should we do to persuade domain experts that they stand to gain great improvements in their research workflow and outcomes?

    Perhaps, in addition to having portfolios of work-to-date, we might consider creating demonstrations based on an expert’s previous work, along with related metrics and costs of producing the vis demonstration. It could be an expensive gamble for the practitioner, but it may lead to longer-term work.

    On the educational side of things, Vis programs might consider including technical internships with local non-academic companies and studios as a way to bring practitioners and non-academic experts together.

    As always, Enrico, your posts are engaging and enjoyable. Thanks!

    1. FILWD

      I think the trick is to keep reaching out. It’s too easy at times to just do everything “in house”. We need to teach vis practitioners how to talk to people who need vis and this is not easy. For instance, asking them what do they want or need is not very effective. You need to sit next to them, look into their work and practices and establish a relationship of mutual respect. Vis is currently too much focused on itself, I don’t see a lot of people advocating for this kind of relationships and that limits innovation and usefulness. But, as always, we need to build the world we like … so let’s get back to work!

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