A Messy Guide to Dealing With the Messy Truth
Reflections from the Data Viz Society’s discussion on how to deal with complex truths and stakeholder conflict
On a dedicated channel, #dvs-topics-in-data-viz, in the Data Visualization Society Slack, our members discuss questions and issues pertinent to the field of data visualization. Discussion topics rotate every two weeks, and while subjects vary, each one challenges our members to think deeply and holistically about questions that affect the field of data visualization. At the end of each discussion, the moderator recaps some of the insights and observations in a post on Nightingale. You can find all of the other discussions here.
Inspired by the growth of the Data Visualization Society, I decided to try out my first visualization competition. I am a massive environmentalist, especially on issues like climate change, so I cheerfully set out to compete in the National Geographic Plastic Pollution competition. The competition had the explicit goal of convincing individuals to reduce their plastic consumption for the health of the planet, but the more I looked, the more I ran into a problem.
It turns out plastics are freaking amazing (kind of, not really, but still good)! They are lightweight, durable, opaque or transparent, and come in many different varieties. But they’re also made out of fossil fuels, and if mismanaged they end up in the oceans where they break into pieces, infiltrating and infecting every level of our ecosphere.
Everything was more complex than I expected, each truth made up of multiple parts, trade-offs, and nuances. My goal was to reduce CO2 emissions, but emissions drop only 3 percent with total global recycling. Meanwhile, 99 percent of emissions happen before a product reaches a consumer. And in many cases, plastic is actually great at reducing emissions. All these facts were in direct conflict with the goal of the competition.
Even when you explain this, or remind people of the sheer magnitude of the problem, or even show where and how plastic is mismanaged, you get the opposite reaction to what you want. You get helplessness. Aimlessness. A lack of responsibility.
Distraught, I decided to give up. I had good intentions, National Geographic had good intentions, we both wanted to help the world. It was as if my “client” and I had different perspectives, and this made a mess of the data and fluid truth. I did not know how to balance the intended outcome with my research, or how to make the situation any better.
So, I hosted a discussion for the Data Visualization Society on dealing with complex truths and connected stakeholder conflict instead.
Libbie Weimer uses a legal lens to deconstruct complex truths into three distinct categories. Something that seemed simple often can be shown to be complicated through “uncertainty … contradictory or lack of evidence … [or] contextualisation of the evidence.” She writes that the notion that “99 percent of CO2 emissions happen upstream of the consumer … complicates Natgeo’s narrative about plastic pollution” for the general population, by contextualising the evidence away from their direct choices. She explains that you must be comfortable reacting and working with each category to be legally sound and achieve a bulletproof result. In data visualization, taking the same approach should lead to enhanced durability.
One could argue in return, “why are we focusing on emissions?” or “people can choose more sustainable products,” contextualizing the evidence again. Despite thinking you have an escape hatch, just the conflict of ideas can lead to more problems like contradiction and uncertainty. Those messages might not even reach your audience. Instead, when working with organizations, Weimer first tries to emphasize a shared value and image of credibility. By being brave with the truth and purposefully elaborating on the nuanced situation, you must now trust the audience, and then the messy truth flows freely from a place of respect. In turn, you can achieve effective, credible communication, by being secure on all fronts. Her experience tells her that it is hard to get everyone on board doing this, but “the essential component is the relationship” with the client. When you trust each other, it is easier to trust others. By leveraging any shared values, she concludes that anyone can achieve pluralistic and multi-faceted visualizations.
Credibility is not the only value critical to dealing with messy truths. Ben Olmos shared a collection of his tried and tested values to rationalize decision-making around complex truths. The ones that stuck out to me personally were:
Trust: I value and want to maintain your trust
Accountability: Don’t attach your name to anything that sucks
Integrity: Be open, honest, and real
In explaining his values, Ben emphasized the idea of his own personal brand as well as the company’s, noting that if you “compromise your integrity and reputation … you may find it to be far more difficult earn it back.” Instead, he says that you should not embed yourself or your client into the data, just allow the truth to naturally reveal itself. He writes:
“As data analysts, scientists, and or visualization experts, our job is to tell a story with data. I was hired to help, educate, inform, and ultimately make those who hired me look great. I don’t manufacture the data, the organization or the environment does. All I can do is ensure we are properly collecting it, ethically cleaning it, and accurately illustrating it, so that it is as solid as it can be.”
In the same scope, Bill Shander sees himself as a “data fiduciary” rather than someone who can deliver an outcome from what he gets. Like the “ghost-whisperer” of your information, he takes a relative perspective of the “truth” and tries to build a direct communication link with stakeholders, minimizing any surprise shocks.
Even when given a desired outcome, Elijah Meeks recalls one incident where there was an implied critique of his work, “that he could not ‘pull it off.’” It was seen as his “professional failing” for not being able to produce insights that aligned with the client’s idea of the truth. Others also mentioned that they have interacted with people who disregarded key insights or asked them to spin the data to make the truth more palatable. Meeks explains that the conflict between stakeholders over the truth and the accompanying complex approach, makes the truth singular. He writes:
“Instead of building data products to support impact and decision making, the lowest common denominator is invariably: ‘Can we agree from an engineering perspective that the data is good’ and then the metrics used for decision making are simple counts of that. Then it’s just [a] Move Number.”
When arguing about nuances and complexity, the messy approach comes out of favour. The only thing that can circumvent the war of ideas is the data and its inherent credibility. The situation is often complex, so something like the number of plastic bags wasted, in its tangibility and purity, becomes the key metric that defines success (the Move Number). Everyone reacts to that number now, and that shapes outcomes, insights, and discourse. Even if cotton bags and paper bags are worse in different ways, all that is counted is the fact they contributed in not wasting a plastic bag.
On this subject, I commented that it was “as if the race for optimization and dashboarding stands in the way of slow exploratory thinking, and that the struggle to find the answer is not worth its weight if we assume every other answer will come from the same place.”
Bridget Cogley was on this same boat, saying: “People want to achieve X, so they hyperfocus into Y metric at the expense of so many other data points.” Stating more generally that focusing on specific singular aspects while missing the complex view is part of our human nature, she concludes that we have to define success differently and that ethics-based collaboration is key. Her success comes from walking people through the epiphany, the “aha” moment, and exploring possibilities from there together. You can put the seed of an idea in their head and watch it grow within them, with their own complex rationalisation. During their subsequent dive into the messy truth, you can share the conflict and come up with solutions together.
In hosting the discussion, I learned that everyone experiences the problem of complex truths, and people are surprisingly brave in discussing conflict and responsibility. From people’s comments, I also realized that overly explaining the truth may only gift confusion and a loss of focus. Perhaps, just the fact the people want to try to recycle, even it is not the most effective action, is enough. Perhaps a half-lie that tells a more succinct truth can provide better outcomes and is more convincing — so it is worth the compromise. But where do we draw the line? How much can we trust people with the full, messy truth? Is doing that creatively not our entire job as visualization practitioners?
If we are truth and beauty operators, then complexity in the truth is just as important as unraveling data hairballs and producing beautiful graphics.
Sometimes the hairball is behind the scenes and between the lines.
Sometimes the connections and conflicts between people and ideas outnumber the edges in our networks and the overlaps in our data.
Sometimes the best improvement is not a new method, but rather an improvement in the data collection for a new idea of the truth, or a new framework in understanding what truth in the data represents.
The truth is rarely pure and never simple. We should act with patience and understanding for everything that entails.
Thanks to everyone who contributed!
