• Data Viz

    My favorite visualization this week:

    I came across this viz at Notabilia and it made my heart sing and my brain buzz. It ties together three things that are compelling: data viz, Wikipedia and human agreement.

    From the authors Moritz Stefaner, Dario Taraborelli, Giovanni Luca Ciampaglia:

    “The visualization above represents Wikipedia’s 100 longest discussions that resulted in the deletion of the respective article.

    • AfD (Article for Deletion) discussions are represented by a thread starting at the bottom center.
    • Each time a user joins an AfD discussion and recommends to keep, merge, or redirect the article a green segment leaning towards the left is added.
    • Each time a user recommends to delete the article a red segment leaning towards the right is added.
    • As the discussion progresses, the length of the segments as well as the angle slowly decay.

    (Go here to play with it live. Be sure to read the full story about the data and what it represents.)

    Why the love?

    It shows a history of collective participation spiraling towards outcome. This is asynchronous agreement emerging over time, which is a very difficult thing to see and track in the moments as it’s happening. Conversations that inform consensus like this can take weeks, months, years to come to decision or agreement, and that’s if you’re all in the same room.

    I’m thinking that this would be an amazing way to visualize collaborative agreements on UX and project-related work as well. Even adding a simple reporting to the end of each meeting could show the swaying lines of how the experience is progressing toward a desired end.

    Getting the data isn’t easy. Wikipedia’s model ensures that there is a direct data trace for the recommended action on an AfD thread. Everyday conversations don’t have the three things required:

    • a clearly defined outcome (go/no go);
    • interim recommendations (warmer/cooler)
    • a way to track data (Um, “Wait a minute while I post my interim recommendation about this conversation to my iPhone App.” – NOT.)

    There are some consensus-oriented conversations that do track data over time and could be viewed this way, like votes in Congress in relation to pass/fail on a specific issue over time, or jury pre-votes in relation to guilty/not-guilty verdicts.

    Which could mean…

    So I’m curious…for project work, what’s the smallest thing we could do to add simple mechanisms to get this data?

    Here’s three possible scenarios:

    Context: Project Work

    Clearly Defined Outcome: Success

    Interim Recommendation: After each team interaction or meeting, everyone would voice in on how they felt towards the potential success of the project (warmer/colder, or positive/negative.)

    Result: We could learn how the overall trajectory is feeling across time, and at the end of the project, identify the whorls that contributed to the success / failure overall.

    Context: Personal Relationship

    Clearly Defined Outcome: Satisfaction

    Interim Recommendation: At the end of each day, the two people note how satisfied they felt in the relationship that day.

    Result: We could see how aligned or not aligned the emotions are, with the goal being to get further and further towards satisfaction.

    Context: Grass-roots support for votes on bills and initiatives

    Clearly Defined Outcome: Decision on pro/con

    Interim recommendation: constituent surveys on thumbs up/thumbs down for support.

    Result: We could see how the public opinion impacted (or did not impact) the final decision and vote.

    Now obviously, a side effect of tracking behavior is that you create a feedback loop that changes behavior. So the resulting visual becomes a tool for changing the system, not just capturing it.

    (But that would be interesting, too.)

    Oh data viz, you make my head swim with ideas.



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