As the mother of a 4-year-old, my life is surrounded by Legos. Daily, I watch my son tear down part of his monster lunar lander to quickly repurpose it as an alien observation tower with a parking garage for “the cars of the aliens.” The kid thinks in chunks, seeing his Legos not as specific blocks, but as cross-functional units ready to be quickly repurposed into other “stuff.”
Legos: cross-functional units ready to be quickly repurposed into other “stuff.”
It’s a good way to think about content, too—be it words or data. There’s no way every single one of your readers are going to read everything that you push out. And its likely that the things you write about have staying power well beyond the day you publish them, right?
But the minute you post your content, it is immediately competing against the news feeds of everything that comes after it. The social streams of Facebook, Twitter, and other social media, plus the news feeds of news aggregators have very short memories. People move on.
Chunks: flexibility in writing and repurposing for later
But if you think and write your content in standalone, succinct “chunks,” (going back to the Lego analogy here), you can use these chunks later. You can combine them and thread them together into an overview narrative, like this morning’s “Three Technology Revolutions” story by the Pew Research Center’s Internet Project. Notice how the main narrative in the story consists of just three short sections (a summary paragraph followed by a large data graphic).
What’s interesting about this is not the data itself, but rather how, if you click the links inside each paragraph, you’ll notice that they link to older articles published last year. And each of those articles is largely concentrated on a data graphic with a small amount of text for context.
As you can see, using (and writing) content in “chunks” allows you lots of flexibility as a writer, and even more flexibility in repurposing your content. (Last year I wrote about taking a similar approach to building infographics.) It’s tough to do this.
Builds discipline
As both a writer and a data designer, I think writing and designing in chunks builds discipline. You have to prioritize. You have to write or show what matters. You have to always think about your audience’s needs and your responsibility to convey the right thing in a short format. That need is heightened because you don’t have the luxury of space or length. You have to know when to stop.
It’s harder to repurpose “chunks” if you don’t think about it upfront, but it’s possible. Here are two ways:
Plan it out that way
One way is to plan it out over time. Think about the longer narrative, split it up into standalone, succinct sections, release it over a period of time, then wrap it up with broader narrative that allows you to create “new” content that links back to your old stuff, providing value and framing for the reader. Write and illustrate those chunks as pieces of content that stand on their own, and keeping them short is important. This makes it easier to repurpose them later. Not all stories lend themselves to this approach. But even for longer-form content, this is a nice way to tease out the central messages if you’re trying to reach a broader audience.
Or… mine old content for new ideas
If you haven’t done that, it’s always possible (and a good idea) to look over what you’ve published over time and see if there are any natural patterns that allow themselves to be threaded together into new content. This might be harder if it wasn’t part of your original approach. But if you’re struggling to produce value content, it’s a good idea to try.
And keep in mind that this stuff isn’t low-value content that you’re pushing out for the sake of traffic. Presumably, you thought it was relevant enough to publish the first time. Repurposing older content with a new, more current context (tied to something in the news cycle, for example) can be a good deal for your readers.
New isn’t always better. Relevant context is.
“New” isn’t always better. Sometimes packaging it up differently and concisely is a great way to get people to find something they may not have read the first time, and gives you the ability to publish “new” content. Or it gives you the ability to build a rockin’ alien observation tower with a parking garage.
When I lived in a dodgy part of Washington, D.C. in the early 90s, I used to get my food from either the pizza joint six blocks down the street, or Dottie’s Liquor on the corner of the dilapidated English basement that I called home. My hours were irregular (hey, I was young and having fun), but I could always count on Dottie’s Liquor to furnish more than a six-pack. I could buy high-fat, high-sodium canned concoctions called “soup” for 99 cents, sugary fruit drinks, and the occasional yellowed roll of toilet paper that the elderly African American cashier would silently pull off the dusty top shelf that hung precariously behind the counter. I didn’t care much about my diet—I was a bike messenger—I could burn off anything. And I never noticed the young Latino and African American families that would crowd the aisles (it was a small store, it only took one family to do that), with kids in tow. It never occurred to me that this was their grocery store because back then, there were no other options within walking distance.
As I got older, I began hearing about “food deserts,” pockets in low-income neighborhoods where a paucity of fresh food and vegetables was the norm. And what little quality food there was cost a fortune. The media coverage would typically feature a few quotes from a researcher and perhaps a food advocate, along with a reasonable-sounding statistic in support.
That framing fit neatly into my personal narrative. I found myself in quick agreement when food activists decried the situation. I never questioned the statistics, either. And when policy makers joined with grassroots campaigns to turn advocacy into policy, I supported it with a sense of satisfaction—in my lifetime, things were changing. Move over Dottie’s Liquor. Farmer’s market produce, come on in. And then, earlier this week, an article on Slate claims that food deserts do not exist—that the claims were made based on inaccurate interpretations of various research studies.
The psychology of data
The idea that by introducing healthy, fresh food one could measurably improve poor health outcomes in low-income populations seemed, not too good to be true, but rather too good to question. So, when Slate published their article questioning claims made about the existence of food deserts, I was surprised and disappointed.
And therein lies the psychology of data. When it proves something you agree with, how likely are you to question it? For a lay person, it’s a question of how well-informed we are. For a policy maker, the burden is much higher.
And the challenge we face, no matter how well informed we attempt to be as members of the general public, is that we are hostage to the facts that trusted messengers—among them, policy makers, journalists and advocates—put in front of us. (For a discussion of the designer’s role, read this previous post.) That’s a big responsibility for them, and the responsibility for us is to question them and hold them to it.
More important than debating the merits of whether or not food deserts truly exist, is examining how the claim of food deserts came to be proven and then disputed. It allows us to walk through the evolution of an idea from the ground up (from advocates, to policy makers, and back to us, the public), and understand the role that data and data literacy plays out across the different actors.
And that’s what this post is about.
Let’s take a quick look at the Slate article and a few of the studies that it references. These studies examine food deserts via the lens of health outcomes, diet and the availability and proximity of healthy food. According to Slate, the increase of healthy food initiatives (those aimed at reducing food deserts and thus, disparities in the health outcomes of low-income populations) has risen sharply in the U.S., due to the largely successful efforts of food activists who lobbied for fresh, affordable food in poor neighborhoods to reduce disparities in health outcomes of low-income people. The charge has even been taken up by Michele Obama.
How did food desert initiatives originate?
In Britain in the mid-90s, there were a few studies (note that Slate describes them as “preliminary”) that suggested that a “a link might exist between distance to a grocery store and the diets of poor people.” Already you can see how easily a well-intentioned health advocate or policy maker can jump to the conclusion that a correlation exists between poor health outcomes and lack of access to fresh, affordable food available from a local grocery store. And this is exactly what happened. The Slate article traces the history of the food desert movement. In a nutshell—a few studies in Britain in the 90s were followed by a Pennsylvania law in 2004 that funded fresh food programmes, followed in quick succession by adoption of similar programs in 22 U.S. states (to date), according to Slate.
But the data cited by advocates in these studies doesn’t entirely support that correlation. Here is a summary of a few studies that refute this (one of which is written by an author who wrote a study that is often misquoted).
A widely-cited study used to support the existence of food deserts is inconclusive
Researchers analyzed how often individuals ate fast food, how much of it they ate, the quality of their food diet, and how much they ate of fruits and vegetables, as well as the availability of fast food restaurants and supermarket grocery stores (measured at different distances). You can read the study for yourself—but it concluded that the evidence showing a correlation between bad food resources and poor diet and obesity are mixed, at best.
“Neighborhood supermarket and grocery store availability were generally unrelated to diet quality and adherence to fruit and vegetable recommendations, with similar associations across income levels.”
So as you can see, the conclusions from the JAMA study didn’t quite square with how they were being used by policy makers—other factors were at play. Low-income men were more apt to consume nearby fast food more (and, conversely, did have a better diet when there were supermarkets nearby), but low-income women were not statistically significant. Middle-income individuals showed varied significance (described by the researchers as “weak” and “inconsistent with significant counterintuitive associations in high-income respondents”).
Tensions between the aspirations of social change and the reality of evidence-based research
An essay in the Journal of Epidemiol Community Health, “Good intentions and received wisdom are not enough,” features a powerful (and damning) indictment of the touchy dynamic between the pressures of social change and the research that underscores it. From the authors:
“There is a common view amongst social and public health scientists that there is an evidence-based medicine juggernaut, a powerful, naive, and overweening attempt to impose an inappropriate narrow and medical model of experimentation onto a complex social world.”
The essay pointedly calls out the resistance (“hostility”) of social scientists, health policy makers and advocates to attempts by researchers to use the evidence-based approach traditionally used in medicine, but not public policy (systemic reviews of data or experimental designs, for example). Why? The authors of the essay claim that social change advocates view the real world as too messy and a far cry from the controlled environment of academic and medical research. This applies, the authors note, particularly to what I’ll describe as social issues of the day—issues where good intentions and raw emotions are at the surface as well-intentioned advocates and policy makers attempt to use data to alleviate the very real and valid human suffering that is so visible to all of us. Read it here.
Assertions quickly become facts in the public sphere
“Assertions can be reported so often that they are considered true (“factoids”). They may sometimes even be used to determine health policy when empirical information is lacking.”
It’s telling that this was written in 2002, approximately two years before the elimination of food deserts became a part of American public policy.
The paper attempts to track the rise of the food desert assertion in the UK. It points to three main UK studies that were frequently cited by advocates and policy makers (two are noted above) and systematically dismantles what it characterizes as erroneous assertions by advocates to correlate food deserts with poor health outcomes. How? You can read about it for yourself, but here’s one example.
The study found that, though healthier food costs more than unhealthy food in low-income areas, both actually cost less in low-income areas. Advocates, however, routinely cited a study but claimed simply that good food cost more than bad food. The nuance here is an important one, and the authors point out that it was never made.
The authors also discuss a different study that has been cited by advocates that is also not as conclusive as widely reported—the study shows that small grocery stores have more expensive food and a narrower range of options—but doesn’t compare how this plays out by income distribution (low- versus high-income neighborhoods).
Lastly, the authors refer to a 1992 study (also frequently cited) which compared the cost and availability of a basket of healthy versus unhealthy foods in poor and more affluent neighborhoods. The study (ironically, also published by Macintyre) was simply a pilot study and didn’t use random sampling, significance tests, and other statistical methods that a more robust study would have used. It was, after all, only intended to be a pilot study. Macintyre herself points out that it was widely (and wrongly) cited across the UK and America as evidence of food deserts.
I’ll leave you with another quote by Macintyre:
“If the social climate is right, facts about the social world can be assumed and hence used as the basis for health policy in the absence of much empirical information.”
That pretty much sums it up.
In fairness, these studies also raise many questions. Who are the authors, how are they funded, and how legitimate are the claims they themselves make? But the questions posed by the authors of these studies serve to at least merit a closer examination of the relationship between data and policy.
Implications for social change advocates and public policy
What are the implications for those of us who care about social and public policy?
Not being critical thinkers and examiners of data puts our credibility on the line in the arena of public perception. It arms our opponents with legitimate counter-criticism to our views.
It can distract us from other, more viable paths to social change that truly can be substantiated and measured. And it obscures the broader, but as important, good intentions behind our convictions. In this case, for low-income people who disproportionately suffer from poor health outcomes, what are the contributing factors that have been credibly examined (long hours working several jobs, the stress and worry that accompany poverty, or the lack of education about what constitutes good health habits,)? That’s where public policy can be directed.
Valuing proper research, taking the time to understand it, and respecting its limitations strengthens our arguments
It’s tough for me to write this post. I’m Hispanic and I have spent my entire career in the advocacy and public policy field. This is very much my world and I see every day how hard my friends and professional colleagues toil to right the wrongs that society allows. The passion, integrity and commitment that advocates and policy makers bring to their work can not be underestimated. And that’s why I write this, because valuing proper research, taking the time to understand it, and respecting its limitations only makes our positions stronger.
In an earlier post, I wrote about how lack of data literacy can put social change organizations behind the curve in advancing their goals. In this case, it can do the same to good intentions, and good outcomes.
But let me conclude by saying that just because the data may not support the public narrative of food deserts, that doesn’t mean that it’s okay for poor people to eat bad food. That’s a patently unfair situation for those who live in poverty. There are many benefits to eating fresh, affordable fruits and vegetables. I make that assumption from what I read in mostly reputable news sources. I further assume that avoiding high-fat, low-nutrition food that delivers scant nutrition for the money is good for other reasons. At least, I want to believe that. But as good as that sounds to me, perhaps I should do a little digging to substantiate my convictions.
The shooting death of Trayvon Martin, the 17-year-old black Florida high school student who was killed by George Zimmerman on February 26, 2012, spurred one of the most widely reported, painful and controversial public conversations on race and social justice in recent memory. The story started as a local news piece, and quickly morphed into a national debate in newspapers and radio stations; on YouTube, Twitter, Facebook, Reddit and other social media channels; on front stoops, in office cubicles, and at kitchen tables; across marches, rallies and demonstrations; and through online petitions and campaigns. These events coalesced and influenced the actions of news organizations, citizens, politicians and thought leaders in a very public way. This offline/online “networked” public discourse was a far cry from the analog (print, radio) news model of the past.
Understanding how information and news networks relate and influence one another helps you decide where to take your message, and to thus influence and help set the agenda for public debate. This is where today’s social change organizations will succeed or fail in their efforts to remain relevant and effective agents of change.
“The Battle for Trayvon Martin: Mapping a Media Controversy Online and Off-line” study analyzes, piece by piece, each facet of the intersection between the offline and online reactions, advocacy, citizen journalism and organized media coverage of the Trayvon Martin news event, and presents an analysis that takes us to the very epicenter of the intersection between media coverage, online and offline activism on both a personal and grassroots level, and the results through the lens of public discourse. This pioneering February, 2014 study was authored by Erhardt Graeff, Matt Stempeck and Ethan Zuckerman of the MIT Center for Civic Media. The goal of the study is to analyze the evolution of the Trayvon Martin story and to understand the role that activists in how the story played out across offline and online media channels .
Using data to quantify influence in public discourse
To the best of my knowledge, the authors are doing something that no one has done before for traditional and digital media (the methodology will give you a headache, in a good way)—they attempt to quantify and measure far beyond the “clicks” on articles that many of us traditionally use to measure engagement and, from that, to glean our influence over the message (I know I’m over simplifying but not by much).
Rather, they map the spread and cross-pollination of those ideas across all media (offline and online, traditional and participatory) and make correlations around consumption (who is clicking) and engagement (what they do and share afterwards), tracking it all back to the message (how does all of this effect how analog and digital news outlets cover the issue). It’s a fascinating cycle, and one that any organization interested in shaping public opinion and effecting social change would be better served to learn.
This post attempts to translate the findings of the study into takeaways that organizations who focus on social change can use to better understand the correlation between traditional, digital and social media today.
First, let’s take a look at one of the most helpful parts of the study—an analysis of the journalism ecosystem of today.
Yesterday’s traditional news gatekeepers are gone—replaced by “the networked public sphere.”
To be effective, social change organizations need to understand how to work and communicate in what the study defines as the “ecosystem” of news and information today.
I think of this in broader terms—to me it’s more of an information ecosystem. Regardless, it is not the topdown gatekeeper model from back in the day of print news—the managing editor, the reporter, and you—cultivating a personal relationship with a network of journalists to pitch your story. Don’t get me wrong, that world exists. But it has expanded by so much that if you don’t understand where else others are engaging, you’ll be talking to an empty room, albeit a virtual one.
The study underscores this by helpfully describing the new world of media as an ecosystem rather than an environment. The distinction may be lost on some of us, but the definition the authors present is clear.
“[Today’s media ecosystem] is not monolithic or hierarchical—[rather] dynamic networks of media linked together by transmedia audiences [those who hop from one media and social platform to another—my take] coalescing around particular stories at particular times, [following] literal hyperlinks [to seek] the most influential source at a given moment.”
So what comprises digital media today? The study emphasizes both professional content (journalists) and amateur content (“citizen journalist” bloggers, for example). Add to this, everyone—those who write 50-word posts on Facebook that get shared, Tweeted and discussed; 140 characters on Twitter, those who post Instagram photos and opinions; the discussions on Reddit, etc.). The authors describe this as “the networked public sphere.” And it’s a big universe with lots of moving parts. If you’re trying to control it, give up (that’s yesterday’s model). If you’re trying to be a smart influencer, read on.
The traditional gate-keeper role of the media has been upended by the democratization of information, which gives social change organizations the opportunity to seize and set the agenda of public discourse.
What’s cool about this networked public sphere model, and critical for social change organizations to understand, is that it presents unheralded opportunities for these organizations to actually set the agenda for public discourse. As noted above, the traditional gate-keeper role of the media has been upended (to a degree) by the democratization of information. If social change organizations (and more importantly, the individuals who serve as their advocates and ambassadors) choose to engage in digital media (carrying out conversations and sharing information on Twitter, Facebook; pushing cultivating relationships and content with bloggers, etc.,) their message becomes the news, and they get to frame it.
Use social media effectively and your message becomes the news—you get to frame the debate.
The study references recent media research around the revolution in Egypt (2010), and likens the Trayvon Martin story to that revolution, in terms of how it played out across all media and public dialogue. For example, the authors cite how Twitter’s #egypt hashtag reflected a blend of both personal political expression and a more conventional media push around a central message. To me, the Twitter conversation represents a hybrid of these formerly distant messaging cousins (the individual and the media outlet).
Think of it this way: Twitter users pushed out their own message about the revolution framed in a way that expressed their common sentiment, then the more “authoritative” (traditional) media outlets began reporting on that “framed” message, and that particular framing was—in turn—disseminated even further by the readers of those outlets. This is one way in which social media is influencing how even traditional news media are shaping and forming the message behind a story.
Data tools used to track and analyze coverage
To trace the path, evolution and influence of the Trayvon Martin story, the authors use Media Cloud and Controversy Mapper (which are, by the way, two tools developed by the authors in conjunction with Harvard’s Berkman Center for Internet and Society.) This is good stuff (case in point: Controversy Mapper’s data visualization on SOPA/PIPA)—imagine being able to analyze not just what the media is covering but how (the message, the interpretation, the framing, the influence) in a rigorous, quantitative way. Well, they did that.
Media Cloud collects articles from more than 27,000 mainstream media outlets and blogs, and follows and tracks links mentioned in these sources to explore the coverage even further. Archive.org’s TV News archive helped the authors analyze broadcast TV (they mine transcripts of broadcast TV). On the digital side, the authors also used Google Trends to analyze searches, General Sentiment to track Tweets and hashtags, and the url tracking and shortening tool, bit.ly.
Data that the authors examined
First the broader media coverage: social media and professional news outlets. The authors examined the number of times the story was referenced, Tweets and hashtags, TV, Google searches for the subject, location of coverage (e.g., front page indicating editorial prioritization) in national papers, and the public’s online activism (for example, a petition on change.org). The methodology and data collection were far more involved than my crude summary attempt. Because the goal of this post is to translate the study for a more general audience, I don’t do the methodology much justice. It merits a closer read.
Trayvon Martin
Let’s shift to the Trayvon Martin story. As you know, this unfolded offline initially—it was local—hyperlocal at first, narrowly framed and, as the authors describe it, “a fight between two people in an area known for occasional violence, stood little chance of attracting significant media attention.”
An initial amount of national media coverage gets returns
Why then, didn’t the story die? The difference was the immediate and unrelenting efforts of the Martin family to share their story. By quickly retaining Benjamin Crump, a pro bono civil rights attorney (interestingly, one who, according to the study’s authors, ascribed the failings of a previous probono effort in part to an inadequate media publicity strategy) who brought on a local attorney who, in turn, recruited a pro bono publicist (Ryan Julison). Julison was able to get coverage from two national media outlets, which later snowballed into other national media.
From a fraternity listserv to a national online petition: Leveraging online activism yields big results
According to the study, the story (spurred by the initial limited national coverage) was mentioned on a Howard University listserv. A Howard law grad got involved and launched a Change.org petition. His rationale was the lack of national coverage. He emailed his petition to other students at the university. Yep, email is how this got started.
More national coverage, social organizations step in, and Change.org becomes “an early leader” in media attention
The Huffington Post, Global Grind, a self-described multi-racial news and lifestyle website and activist organizations (ColorOfChange and the Black Youth Project) began covering the story, described by the authors as “early amplifiers.” As a result, the change.org petition began picking up speed (growing from 217 initial signatures on day one—March 8—to over 30,000 signatures five days later (March 13).
Change.org attracts celebrities, and even more attention
Something interesting happened on the sixth day after the petition was launched. A change.org employee asked a target group of celebrities whom he thought would be sympathetic to the cause to share the petition with their fans (Mia Farrow, Spike Lee, to name a few). They were interested, and they did share—to the tune of over 80,000 signatures a few days later (a 900% increase in signatures over the course of 3 days, according to the authors of this study).
The shift to mainstream media as the news authority on the story
The pattern until March 17 (when the publicist released 911 tapes to the public and the media) was as follows: low-profile, hyperlocal news story; narrow coverage on a national level that spurs a rapid rise of personal and social activism; which yielded high-profile coverage by celebrities and a resulting increase in national coverage.
When the probono civil rights attorney (Benjamin Crump) released the 911 call to the media, coverage—particularly in mainstream broadcast radio and TV—predictably mushroomed. The authors of the study specifically point out that the audio nature of the 911 call may have made it more appealing for radio and TV to cover.
But social change and race-based organizations and celebrities continue the momentum
Reddit’s /r/blackculture subreddit featured the change.org petition and Reverend Al Sharpton’s involvement continued the publicity. By now, civil rights and political leaders all over the country were taking up the charge through political demonstrations and rallies. The authors cite the Million Hoodie March in New York (spearheaded by a digital strategist) as a catalyst for more coverage. Interestingly, the authors point out, larger media didn’t feature the story on their front pages until after the march, positing that the actuality of the march made for an easier story to cover.
News hooks in traditional media need “real” events
There’s an interesting pattern here of mainstream media not covering the Martin story until something “real” happens (the authors describe these as “actualities”). Note how radio and TV began covering the story after an audio recording was released, and front page newspaper coverage began after an actual march took place. After Zimmerman was finally taken into custody (another “real” event) six weeks after the shooting, newspaper coverage peaked.
Who influences how a message is framed by national media outlets?
Let me answer that simply—it’s not the media outlets. Nowadays, the spin on a story often takes place outside of national media news sources. Frequently, by the time they report on something, they’re simply capturing what has already happened.
So if you want to influence how the a major news outlet writes a story, your message can begin in social and digital media, and with your activists and ambassadors.
Let’s look at how the conservative movement was able to influence the debate. The study cites how one notable conservative blogger (Dan Linehan, of the Wagist blog) claimed that Trayvon was a drug dealer. As you would expect, this message was spread and picked up by like-minded right-leaning blogs, and eventually did make its way to mainstream media (the Miami Herald), where it was amplified. So, regardless of the accuracy of the claim (and it was not credible), right-wing bloggers became effective ambassadors to mainstream media.
The study’s authors actually cite research that shows that repeating a myth in order to deny its credibility may have the opposite effect.
“Research has shown that restating a myth in order to negate it can actually produce familiarity and thereby help further propagate the misinformation.”
This has strong implications for social change organizations of all stripes—the public debate is often played out as a series of narratives that are alternately supported and refuted by proponents and opponents, respectively.
Two graphics show the networks of media that mentioned “marijuana” (figure 8 in the study) and “drug dealer” (figure 9) during this period (notice how prominent the right-wing Wagist bubble is in both graphics.) The large size of the Miami Herald bubble signals high frequency of news mentions of the word “marijuana” in the story; as does the similarly large sized Wall Street Journal bubble (“drug dealer” in the same context).
Figure 8: Network of interlinked media mentioning ‘marijuana’ as taken from the authors’ studyFigure 9: Network of interlinked media mentioning ‘drug dealer’ as taken from the authors’ study
How opponents can inadvertently strengthen your messaging goals
What’s interesting is how left-wing blogs and organizations join the fray and, by refuting the right-wing claims, nonetheless continue to keep the negative framing in the limelight, as evidenced by the largish bubbles that represent ThinkProgress.org, for example. The author’s conclusions:
“This suggests a strategy for reframing a story—if an activist is able to gain mainstream coverage for [framing a message a certain way], opponents are likely to respond, [thus] perpetuating a debate that features the desired framing [of the activist].
Remember, these two graphs reflect the prominence of Trayvon Martin and the words “drug dealer” and “marijuana,” an association that his supporters deemed undesirable. All started by a right-leaning blogger, perpetuated by those who countered the claim, and widely covered (eventually by papers ranging from the Wall Street Journal to the New York Times).
Piggybacking to a related cause: Stand Your Ground Laws under attack
The authors describe how an organization with a different focus, The Center for Media and Democracy (CMD), a left-leaning organization, injected its concerns about the influence of the American Legislative Exchange Council (ALEC), a conservative lobbying organization that became an outspoken proponent of the “stand your ground laws” that were used in Zimmerman’s defense of the Trayvon Martin shooting) into the debate. The Center for Media and Democracy had launched a campaign against ALEC prior to the shooting but used the controversy to strengthen its campaign. And like-minded progressive organizations formed a cascading effect, as they piggybacked off the Center’s research to pressure corporations to withdraw ALEC funding. Eventually, even Paul Krugman of the New York Times wrote an op-ed (March 25) about Trayvon and the Stand Your Ground Laws, and change.org followed suit with many petitions to dismantle these laws. According to the authors of the media mapping study, on April 17 ALEC terminated its controversial task force on those laws.
Correlation between digital and traditional media coverage and reader engagement: All news sources tend to cover issues even after reader interest wanes
The study’s findings show that all media sources, (traditional and digital) are roughly correlated (when one was covering the story, so were the others)—this extends to news articles, TV coverage, searches, petition signatures, and clicks to links (via bit.ly) on this coverage. The conversation on Twitter appeared to peter out after a while, and the authors speculate that this was either because campaigns had used Twitter early on or simply because social media may be quicker than other mediums to move away from one story to the next. Overall, however, the “tail” of news coverage went beyond actual reader engagement (sharing, clicking on links to articles, etc.) which the authors believe may indicate that readers simply lost interest even whilst the media continued coverage.
Conclusions:
1. Broadcast media still matters.
Broadcast media amplifies (spreads the story through coverage) and serves as a gatekeeper (what it chooses not to cover has a harder time getting out into the public debate, and how it frames what it does cover tends to stick). But activists who use other media channels and platforms (petitions, social media, blogging, leveraging like-minded organization and allies, personal networks) are now solidly influencing how the message is shaped and formed (framing).
2. Social change organizations can spin traditional media for their own purposes.
Even though broadcast media still serve as strong gatekeepers to what does/doesn’t get covered and how it is framed, smart organizations leverage existing coverage to inform their supporters, piggyback off the coverage to mobilize their allies, and spin it (reframing) to meet their own messaging goals. And, from a messaging perspective, it’s promising, as evidenced by how successfully many Trayvon Martin proponents were able to shift the media narrative (the outcome of the trial is another matter).
3. The blogosphere covers issues long after broadcast media coverage peaks.
Smart organizations know this, and court bloggers accordingly, understanding what motivates them to write and when. So understanding who is blogging (or has the potential to blog) about your issues and cultivating those relationships is key.
4. Contemporary news outlets today are increasingly more likely to get the maximum out their investment of time and journalists to cover a story.
News outlets will cover a story even after readers have disengaged. Don’t get too excited. This has not been covered in a flattering light (see McJournalism).
5. Social media can create related micro-stories from broader events.
These micro-stories then become news events in themselves and create a longer tail for the original story (the Million Hoodie March, for example).
6. Social media can side-step traditional media gatekeeping functions if you have good content.
Some social media platforms that are particularly well-suited to a specific type of content (YouTube or Facebook for video-sharing, for example) quite powerfully and effectively side-step traditional media’s gatekeeper role, and thus are demonstrably able to shape public opinion. Organizations that know how to create relevant content for these and other platforms can get their message across in huge ways.
7. Social media is so much more than spreading the word.
Because it is so heavily reliant on personal interpretation (one person sharing his or her opinion about a news event, in addition to simply sharing news of the event itself), social media is a powerful force in shaping the message and framing—and the public perception—about an event.
8. Deviant discourse: Social media upends the traditional notion that mainstream media are, indeed, the gatekeepers for news content and opinion
This has its downfalls. In the past, gatekeeper news organizations simply wouldn’t cover extreme views that were a small minority of public debate. Today, if enough people talk about it, it does indeed become mainstream news (the authors point to the widespread coverage of Obama’s citizenship as a case in point). The authors explain this “deviant discourse,” as they put it, brilliantly, and it’s worth quoting here:
“Our work suggests a mechanism through which social media users introduce potentially deviant frames into the mainstream: they harness ideas to a high attention story already underway and attempt to direct the attention generated by the story towards their interpretations and views.”
9. Use finding #8 (above) for good, and not for evil, okay?
(My opinion; not the authors’.)
Hope you enjoyed this post. Mad props to the geniuses at the MIT Center for Civic Media for this incredibly data-rich study. Mindblowing stuff.
These days, you can throw a rock and reliably hit any number of articles and headlines proclaiming the power of big data, open data, and transparency. The acceptance and adoption of using large, public sets of information to make informed decisions represents a sea change in how the corporate world, inacademia, think tanks and large NGOs are investing in their capacity to crunch more than numbers. No surprise there. But how does the little guy—the small grassroots organization with a small budget and a big mission around social change—fare?
I’ve been thinking a lot about this lately. Last year, I made a job shift. I moved from a very large, well-funded nonprofit to a relatively small healthcare advocacy organization. In my old job, I worked in data visualization and regularly called upon the considerable financial, technological and statistical resources that my employer afforded me. Today is a different story. I work with supremely talented and passionate people, but the data resources that I once took for granted are gone. The “data divide” is now staring me in the face. And that’s the reason for this post—the reality that, for all the promise that big data and technology claims to offer, many of today’s smaller nonprofits and grassroots organizations are not equipped to collect, understand and harness information to move their social mission. We are the “have nots” who look out onto the world of the “haves” with statistical modeling tools, economists or statisticians on hand, coders on staff or on contract.
The data divide—what is it?
The “data divide” is by now a familiar term to many of us. The Guardian wrote about “data apartheid” when it reported on the findings from the recent 2013 Open Data Index last November. Similar findings are in the Open Data Barometer 2013 report released late last year too. And we know how it exacerbates problems faced by developing countries in fostering an open, transparent government and an informed, participatory citizenry. As I wrote last year, a good example of how open data helps citizens overcome these hurdles lies in how La Nacion (Argentina’s national newspaper) teamed up with data journalists to publish data on a variety of indicators to the Argentine public—despite the government’s lack of a Freedom of Information law.
Data divide: Access to data does not translate into results
In a blog post dating back to 2011, Mike Gurnstein describes the data divide in a way that many health care advocates are talking about healthcare today. In discussing the Affordable Care Act, advocates regularly say that access to health care is not enough—it’s the quality of care that matters. And there is an entire movement around health system reform that underscores this. Gurnstein makes a similar point about data: “[A]ccess is not enough,” he writes. “[I]t is whether opportunities and pre-conditions are in place for the effective use of the technology particularly for those at the grassroots.” Go Mike. I haven’t a clue who you are, but you nailed it.
In the same way in which the “digital divide” of the 90s and 00s required education and digital literacy to make real the opportunities that online access offered, bridging the data divide for small organizations relies on more than making data available, but also in affording these groups the ability to use it effectively through knowledge (data literacy—an understanding of how to read data and how to represent/visualize it effectively for a common purpose) and resources (the realization of this understanding into actual tools).
How can data help grassroots organizations and smaller nonprofits?
Here in D.C., Applied Predictive Technology (APT), a tech firm that sells predictive analytics software, volunteered to analyze the data that a local charter school was collecting from the tablet apps that its students were using. APT used this “data dive” to help teachers assess how well the tablet reading apps were working for different kids—allowing teachers to tweak the reading curriculum and apply intervention to different types of students.
One of the best organizations out there is New York-based DataKind. If you really want to understand how socially-conscious data scientists are working to achieve social change through data, take five minutes to check out the variety of projects they work on. Over the past several years, DataKind has been launching “data drives” in cities around the U.S. Similar in nature to “hackathons” or “code-a-thons,” these DataDives team up volunteer data scientists/analysts and social organizations over the course of a few days to build apps or software that solve a well-defined data problem. And then they solve it.
When DataKind held a DataDive for D.C. Action for Children, a small organization that collects data on the indicators that affect the well-being of children to mitigate poverty, good things started to happen. The nonprofit also runs the DC chapter of the Kids Count program and, through Kids Count, it was doing a great job at collecting data (that was their mission). But the work that they were producing was static—PDFs—a situation common to many small organizations. Fortunately, they realized that, to make the data meaningful, easier to analyze, and more effective at highlighting the poverty problems that needed to be solved, they needed to visualize it. This is where DataKind came in. Their volunteers worked for a month to create an interactive data visualization tool (eDatabook) that mapped the well-being indicators and poverty clusters across the District. The best part? It’s replicable. Other DC Count programs across the country can adopt it as well.
Using data and hackathons to help on a local level
The vast global data modeling regularly published by the World Bank is impressive. But municipalities are using data to tackle local problems too. Like D.C. Action for Children, cities are pairing up with volunteer data analysts and coders to sidestep the issues of inhouse capacity and expertise.
To fight ongoing problems with obesity and diabetes, for example, New York City launched its first health data code-a-thon this past December. The result? An app called “Vera.” Based on a user’s risk for diabetes, Vera texts users reminders and tips for physical activity, glucose monitoring and even good food intake.
Leveraging hackathons for broader impact
Voting: On a broader scale, the Voting Information Project, a small group of elections experts who focus on improving the voting experience for the public through cutting-edge technology, held its first hackathon in November, 2013 (disclaimer, I was affiliated with the organization that funds this project). The hackathon yielded fast and effective results, including first-ever voter lookup tools, that were used by Americans everywhere.
Healthcare: On June 2, 2014 Health Code-a-palooza will bring together programmer teams who, over the course of 48 hours, compete to see who can use a Medicare data set to build the best app for doctors to use to improve the quality of care that they deliver to patients. This hackathon is part of the Health Data Consortium’s annual Health Datapalozza, an event that features data and healthcare experts discussing how open data can drive meaningful improvements in the health reform movement. But you have to admit, the coding is pretty cool too. If you’re interested in learning more about how open data is playing out in the field of healthcare, read more about the Health Data Consortium.
Challenges and questions around transforming the data culture in small nonprofits
Lack of data literacy can impede an organization’s ability to articulate its need.
As I mentioned, part of the problem is not just access to data, but being able to frame a goal, understand which data to collect and establish good collection practices—data literacy. For an organization taking nascent steps toward data collection, this can be daunting. It requires a change in the organization’s culture, investment of time (if not technology and staff) and a reprioritization of traditional methods of executing its goals. Much of that work is internal. But some of that can be helped by organizations such as DataKind’s, who actually mentors organizations to help them frame their problem and prepare for the end result.
Sustainability beyond the initial volunteer effort
And what happens after the project concludes? What if something breaks? How do you continue to foster an environment of learning and change in an organization after it takes its first steps toward a data culture? Again, an approach like DataKind’s is promising. They stick around, monitor the project and provide follow-up support to ensure that the work keeps going. That makes sense, because it’s part of DataKind’s mission. In future posts, this is something that I’ll be writing more about, as well as how data volunteers and organizations are finding each other. If you’ve got ideas or stories to share, let me know. You can follow me on Twitter at @uriona.
Today, I’m visualizing marriage equality for everyone. In the U.S., the Supreme Court is hearing two landmark cases on gay marriage. Hoping the long journey is nearing a happy and just conclusion.
Ever wonder how well states are running their elections systems? Want to know which state rejects the highest number of absentee ballots? Or which state has the lowest voting time? And which state has the highest rate of disability- or illness-related voting problems?
A new interactive elections tool by The Pew Charitable Trusts (the Elections Performance Index) sheds some light on many of the issues that affect how well states administer the process of ensuring that their citizens have the ability to vote and to have those votes counted. Measuring these and other indicators (17 in all, count ’em), Pew’s elections geeks (I was a part of the team) partnered with Pitch Interactive to develop a first-of-its-kind-tool to see how states fare. Today’s post is a quick take on how the project was created from a data visualization perspective.
Pew’s latest elections interactive: The Elections Performance Index
Lots of data here, folks. 50 states (and the District), two elections (2008 presidential and 2010 mid-term) and 17 ways to measure performance. Add to that the ability to allow viewers to make their own judgments–there is an overall score, for sure–but the beauty of this tool is that it allows users to slice and dice the data along some or all indicators, years and states to create custom views and rankings of the data.
The data will dictate your approach to a good visualization
When we sat down with Pitch to kick around ideas for the elections interactive, we were initially inspired by Moritz Stefaner’s very elegant Your Better Life visualization, a tool that measures 11 indicators of quality of life in the 30-plus member countries of the Organization for Economic Cooperation and Development (OECD). Take a look–it’s a beautiful representation of data.
And though, initially, we thought that our interactive might go in the same direction, a deeper dive into the data proved otherwise. Comparing 30 countries along 11 indicators is very different than comparing 50 states plus DC, 17 indicators and 2 election cycles. Add to that the moving target of creating an algorithm to calculate indicators for different user-selected combinations, and you’ve got yourself a project.
After our interactive was live, I talked to Wesley Grubbs (founder and creative director at Pitch) about the project. I was interested in hearing about the hurdles that the data and design presented and how his creativity was challenged when working with the elections data. One of the first things that he recalled was the sheer quantity of data, and the complications of measuring indicators along very different election cycles. If this sounds too wonky, bear with me. Remember, one of the cool things about this interactive is that it allows you to see voter patterns (e.g., voter turnout) along two very different types of elections–mid-term elections (when many states elect their governors, their members of Congress and, in many cases, municipal elections) and the higher-profile presidential elections. Pitting these two against one another is a bit like comparing the proverbial apples and oranges. Voting patterns are dramatically different. (The highest rate of voter turnout in 2008–a presidential election–was 78.1 % in Minnesota. Compare that to the highest rate in the 2010 midterm election–56% for Maine, and you’ll see what I mean.)
Your audiences will influence your design
Another challenge early on was the tension between artistry and function. In an ideal world, the most beautiful thing is the most clear thing (an earlier post, “Should graphics be easy to understand?“, delves into this further). I remember reviewing the awesomeness behind Wes and his team’s early representations of the data. From my perspective as a designer, these were breathtakingly visual concepts that, to those who hung in there, served up beauty as well as clarity. But from a more pragmatic perspective, an analysis of our audience (policymakers and key influencers as well as members of the media and state election administration officials) revealed that the comfort-level with more abstract forms of visualizations was bound to be a mixed bag. Above all else, we needed to be clear and straightforward, getting to the data as quickly as possible.
Wes decided to do just that. “It’s funny,” he said. “We don’t often use bar graphs in our work. But in this case we asked, what’s the most basic way to do rankings? And we realized, it’s simple. You put things on top of one another. So what’s more basic than a bar chart?”
“We had to build trust–you can’t show sparkle balls flying across the screen to impress [your users]–you have to impress them with the data.”–Wesley Grubbs, Pitch Interactive
When I asked Wes how, at the time, he had felt about possibly letting go of some of the crazy creativity that led him to create the Google weapons/ammunitions graphic, he simply responded, “Well, yes, we do lots of cutting edge, wild and crazy stuff. In this case, however, a good developer is going to go where the data leads them. In addition, the audiences for this tool are journalists, academics, media–the range of tech-saavyness is very broad. We had to build trust–you can’t show sparkle balls flying across the screen to impress them–you have to impress them with the data.”
Turn your challenges into an asset
When we brought up the oft-cited concern around vertical space (“How long do you expect people to scroll for 50 states, Wes?”, I remember asking) his approach was straightforward: “Let’s blow up the bar chart and make it an intentional use of vertical space. Let’s make the user scroll–build that into the design instead of trying to cram everything above the fold.”
I think it worked. This is a terrific example of visualization experts who, responsibly, put the data and the end users above all else. “We could have wound up with a beautiful visualization that only some of our audiences understood,” says Wes. “We opted to design something accessible to everyone.”
How did Pitch build the Elections Performance Index tool?
Primarily using D3, a javascript library that many developers are now using for visualizations. It was not without its drawbacks, however. When I asked Wes about lessons learned, the first thing that he mentioned was the importance of understanding the impact of end-user technology on different programming languages. “D3 isn’t for everyone,” he notes. “Take a look at your users. What browsers are they using? The older stuff simply won’t work with many of the best tools of today. You have to scale back expectations at the beginning. The hardest part can be convincing organizations that the cutting-edge stuff requires modern technology and their users may not be in line with that. It’s all about the end user.”
Well, as an end user and a participant in the process I’m pleased. I hope you’ll agree to take the tool for a spin.
In my last post, I discussed how expectations and perceptions of designers are as important to quality data visualizations as are more conventional resources, such as time, people and money. But there is also a flip side to this–there are times when, as designers, we may be faced with a choice to compromise on how we present data. The compromises we agree to–or reject–are as important to our field as anything else. (Kudos to me for resisting the urge to title this “drawing the line in infographics.”)
A friend related to me a recent conversation in which an art director who, when presented with a bar graph of extreme values (very high and very low), asked the designer to “fudge” the size of the smaller bars. (They were visible–not hairline–but too small to comfortably fit the values inside of them. Presumably the art director wanted to nudge them up so that the numbers would fit inside of the bars.) My initial reaction was er… not favorable. I felt like a mother bear protecting her cubs (the cubs, in this tortured analogy, are the data). I may have uttered a few choice words, even.
The ethics of compromise.
But, once I calmed myself down, it occurred to me that this might be something interesting to write about. I polled a few designer and non-designer friends. What do you think, I asked. Was this a bow to art or clarity? Was it an unintentional breach of ethics or a well-intentioned attempt to make information easier to understand? Was it goal-driven or just lack of creativity? Don’t jump on the art director just yet. This isn’t about the choice that person made (that’s the subject of another post). It simply reflects the reality that, as in other professions, we’ll all be asked to make choices that, to others, may appear to be inconsequential. We need to make sure we handle these choices intentionally and carefully.
Here’s what came to mind after my conversations with other designers.
Book-binding: an invisible art
Let’s think about the book-binding trade of back in the day. The men (mostly men, anyway) who bound books hundreds of years ago were tradesmen. They had a craft which they revered. They apprenticed and, as journeymen, they traveled from place to place, learning and honing their craft to become–eventually–book-binders. This is not unlike the path that many information designers take today.
For all the painstaking zeal and meticulousness put into the binding of the book, the end result was rarely if ever examined once produced. If the thing didn’t fall apart in your hands, you were satisfied as a consumer.
I won’t bore you with the mechanics, but suffice to say that binding a book involved a lot of work, much of which was invisible to the eventual and subsequent owners. Once purchased, the book was read, perhaps the craftsmanship briefly admired, and then it was shelved or passed on, sometimes for generations (think of the family Bible). And yet, for all the painstaking zeal and meticulousness put into the binding of the book, the end result was rarely if ever examined once produced. Again, not unlike the process of visualizing data, much of the effort and care involved in sewing pages into folios, hand-stitching the spine–remained largely unseen. If the thing didn’t fall apart in your hands you were satisfied. End of story. And yet, despite this invisibility, these bookbinders pursued their craft with diligence and and care nonetheless. How well or how poorly they plied their trade was not immediately evident, as these old books often outlived their makers. They had no immediate incentive to be unduly diligent. And yet, I like to think that most of them did not cut corners. Why? I’d say it was self-respect and public recognition of the importance of their craft. Maybe I’m over romanticizing books (I do collect them).
Our craft: Are we short-order cooks or visual content experts?
My point? This is an issue of the ethics of our craft. As designers, we need to ask ourselves: are we short-order cooks or visual content experts? Are we hacks or tradesmen/women? Is data visualization a craft or only a paycheck? Is data an obstacle to be overcome or a living boundary that, with each challenge, offers us the opportunity to learn, do better, and to empower our readers by bringing information to the surface in a manner that brings with it a new understanding? And while, from the perspective of the client (or, in this case, the art director) it may not always be apparent that the accommodations they ask us to make are not wise, it is–nonetheless–our responsibility to do the right thing, and bring others along. In this way, we advance the field forward and our professionalism as well.
And that’s the crux of this post.
Whatever your intentions, what is the effect of the small compromises that you make in being precise, transparent and correct in how you present data?
The more seasoned amongst you may shake your heads and think that these things are self-evident. But to those of you who are just starting out (be it as younger designers or managers in charge of new data viz projects), this may not be something you’ve thought much about. It may not even seem like much of a big deal to you.
Making those small compromises weighs on you, wears you down and–worse–makes the next compromise all the greater in scope and easier to bear.
What is the effect of compromise on the designer and the team?
So, what happens when a designer makes those compromises? When I asked a few designers, they all had one response in common: morale and self-esteem. Here’s the thing: making that one small edit will be invisible to everyone but you. It’s not like your readers will ask you to send them your Illustrator file so that they can measure pixels before they read further. Like the bookbinder who sewed thread onto page folios, no one but you will see the guts of your files. But making those small compromises weighs on you, wears you down and–worse–makes the next compromise all the greater in scope and easier to bear. And these things add up to the slow devolution of what was once a craftsman/woman (if I may be allowed to use such an archaic term) to a hack.
And what happens when an art director suggests those compromises? Well, you risk losing the respect of seasoned members of your team, that’s obvious. Worse, you risk creating an environment that is progressively sloppy. And while no one will catch the small compromises, they sure as hell will catch the big ones. Remember the infamous Fox piechart?
Other examples of altering data
It doesn’t stop with information designers, as I’m sure you know. Another designer who Photoshops medical imagery (for example, a CT scan or slides of cancer cells) told me about a doctor who, when preparing images of slides for a research publication, asked the designer to darken some areas to make them more visible (thus allowing him to better make his case). The designer balked–these aren’t just pictures, he told the doctor–they’re data.
And if you want a more mainstream example, how about the furor over the Time cover of OJ Simpson in 1994? Or, more recently (2008), the Hillary Clinton ad which featured then Presidential candidate Obama with arguably darker skin?
What is unacceptable compromise to one might be reasonable accommodation to another.
There may not be room to make the wrong compromises, but there is always opportunity for discussion.
No one is perfect. And each of the examples that I gave leave plenty of room for discussion. As a newspaper friend recently noted, some photographers are adamant about not retouching any photos they take–including not cutting subjects out of backgrounds. Others are not as rigid. And not all of the participants in my informal poll reacted with extreme horror at the thought of slightly lengthening bars. Some merely grimaced. But all agreed that if you’re going to tread on thin ice, you’d best aware of it. Another friend points out that he noticed a disconnect between his former employer (a newspaper) and his current one (a corporation). He’s doing the same work–designing information graphics. But whilst former journalist colleagues (having their own code of ethics) would never have asked him to fudge the appearance of data, he feels that–in his current role as a designer in the corporate world–his colleagues have a lesser understanding and appreciation of what asking this might mean.
This isn’t necessarily a bad thing–handled correctly, it can present an opportunity for education. But you have to be willing to put yourself out there–a place that not everyone (perhaps less experienced designers or as employees with less seniority) is comfortable occupying.
As designers, let us be keenly aware of how the small choices we make for ourselves can add up to large consequences for our profession. I’d love to hear more from you on this. Have you been place in similar situations? How did you handle them?
Does time equal quality in good infographics? Nope, not necessarily. I’ve been giving this a lot of thought lately and, in reading recent posts by Seth Godin and Alberto Cairo, it’s interesting to see how each touches upon what I see as the pressures and attitudes that affect how well we design good information graphics.
In Mr. Godin’s case, he mentions what he calls “the attention paradox.” While he’s not specifically writing about design, his comments nonetheless aptly relate to the work designers do. As more marketers crave attention, the more they’re willing to part with content that is good at reaching an audience, and terrible at retaining it. Makes sense, right? In a time in which we’re increasingly consumed with tracking metrics and measuring success by the numbers it is par for the course to get caught up in the rat race for the next big thing (big being determined by 30-second relevance and traffic for that day). Surprisingly, information graphics are no exception. And why should they be?
I recently mentioned that, because we’re all under pressure to create more and more content, “repurposing” content is seen as a good way to take advantage of the sweat equity put into other pieces (web articles, reports, data collection) and to convert that into an infographic. This pressure to produce can have real drawbacks–clients mistakenly assume that information can be quickly “designed” just because in their estimation, the facts and the message have already been proscribed. Here–quality can suffer from lack of time. But the point that I was really getting at in my post, which I unfortunately failed to articulate clearly–was the designer’s role.
When designers are treated as service desks and not content experts (“Here are the facts, here is the message, now please make this pretty. Call me when you’re done.”), you simply don’t get the best work.
Fortunately, Alberto Cairo, in “Empower your infographics, visualization, and data teams” gets to the point. According to Mr. Cairo (and I agree) the real problem is the limited perception of the designer’s role. He mentions how, in news rooms, graphic designers are often seen as “service desks.” This isn’t limited to news rooms. In my own life, I occasionally get requests to design graphics “you know, like the New York Times” (yes, I really do). As Mr. Cairo points out, we all laud the New York Times and other large media outlets (one of my personal favorites is New Scientist) for their high-quality information graphics–pieces that can take months to make with large teams of content producers and designers in place. I agree with Mr. Cairo’s perspective that this fact might lead you to erroneously conclude that time and staffing (more people, more time) equals great work (bluntly, he says, “You can’t.”).
The solution lies, in part, in treating and using your designers as partners who help to shape content effectively.
So, what does this mean, exactly? Bring your designer into the room when you’re having editorial discussions about how to create content, before you’ve decided on what shape that content will take. Listen to your designers and expect them to offer up ideas about how to turn that into information design (be it static, motion or interactive).
Designers should read the content.
Expect your designer to read, read, read and understand. I ask my designers to read research reports before they create infographics or data visualizations. This may be a “duh” moment to some of you, but you’d be surprised how many people (including designers) don’t think of this or, worse, don’t see this as part of the designer’s role. How do you design what you don’t understand? How do you filter out the best parts of information and data without having reviewed the source?
And don’t micromanage the design. Leave them alone to create and use their expertise. Trust them, as content partners, to visualize not just the data, not just the facts, but the voice that carries the design.
I’m sure there’s more and would love to hear from you about what other recommendations you have.
A few weeks ago, I posted a lengthy three-part series on building good infographics. To kick off the new year, here’s an infographic that I designed to illustrate the key concepts of that series. Enjoy!
In the first article of this series, we discussed how good planning and team dynamics can make or break even the best design ideas for an info graphic.
In the second article, you learned how to bring together your data and your story into a solid sketch.
In this final part, I’ll cover presenting the concept to your team effectively, managing expectations, and executing the rest of the design process so that your designer doesn’t fire you.
Part 4: Pitch the concept sketch to your team.
Schedule another meeting. Buy more donuts. Bring your designer. Keep it low-key if you can.
Get buy-in and set expectations. Now is the time to present to the team. The nice thing about sketches is that they appear to be so informal that you can share them with people quickly–they allow you to reach out individually to take the temperature of the team if needed and make adjustments as you see fit. This iteration helps because, when you get down to the formal presentation of the sketch, you’ve already established a bit of buy-in (team dynamics permitting, of course).
Before you launch into the sketch, start with a step back to cover the major points from the kick-off meeting.
It may seem repetitive, especially if you just met a few days ago, but it helps ensure that everyone’s on the same page and, if not, identifies those issues quickly. There is nothing worse than launching into a presentation only to find out later that not everyone shares the same goals for the graphic.
Reiterate what you’re creating, who it’s for, how you expect they’ll use it, what they’ll likely want to hear, and how the graphic will support that. Sometimes, this is where things can really get bogged down. The meeting that you had a few days or weeks ago (you remember the one–everyone nodded and seemed to be in happy agreement) suddenly becomes a distant memory as stakeholders, faced with a concrete presentation and decision point, decide to begin reevaluating the goals, objectives and purpose of the graphic. Well, better now than later. When this happens to me, I’m grateful for it, to be frank. It’s pretty much a hallmark of busy teams (you can’t get them to focus until you have something in front of them) or new teams (those who haven’t worked together before, or who haven’t created data viz products before).
Sometimes the difficult conversations tell you more about the team than anything else. At any rate, it’s valuable.
If this happens to you, try to relax and take it in. Sometimes the difficult conversations tell you more about the team than anything else. At any rate, it’s valuable. It’ll give you a sense of the red flags to watch for later in the process. If the team is too indecisive, bring in senior managers, if you can, or summarize the issues they raise and simply state that decisions need to be made before moving further. Don’t be discouraged if you’re suddenly back to the drawing board. These things happen. A lot.
If the conversation is moving smoothly, it’s helpful to talk about the data.
Talk about the things that you expected to find that you confirmed (likely they’ll expect those things also). For example, “I knew that widgets were gaining ground in G20 countries.” And talk about the findings that surprised you. “We talked about widgets gaining ground in our report, but when we looked at more data we saw the lead as slight, which belied our key message. So, to soften this, we added data about projected use for the next 8 years and were able to keep the message of increased widget-use.”
Sell your stakeholders on the problems and solutions that you encountered before getting into the nitty gritty of a sketch.
In other words, ensure that you have shared expectations about the graphic, then sell your stakeholders on the problems and solutions that you encountered before getting into the nitty gritty of a sketch. It’ll inform them and give them good perspective as they review. This sounds like a lot, but it’s important. In my experience, it can take anywhere from a few minutes with an experienced team to more than an hour.
Now, show the sketch(es). Pitch your concepts. Listen. Get people excited. Be open-minded (or, hell, just fake it). Listen, listen, and listen. When you’re not listening, ask questions. Have your designer on hand to participate with you.
Weighing the impact of team recommendations on scope and timing. Once the show and tell is over, talk about the production and design cycles in a general sense and, if you’re able (you’ll have to think on your feet), how the group’s feedback might affect the schedule and scope of the graphic.
Don’t speak for the designer if the designer is not there.
Be careful here–particularly if the designer (for whatever reason) is not part of the conversation. Don’t make assumptions about how easy or difficult it will be to implement a particular suggestion. What looks easy to you may take a long time to illustrate. What seems like a no-brainer idea may not be supported by the data. What seems like a good suggestion may have cascading effects on the design that only the designer can spot. When in doubt, be noncommittal.
Part 5: Keep iterating.
Once you’ve had these conversations, you can keep iterating the sketch as needed. Easy peasy, right? Sure. I usually allow for about 2-3 iterations of a sketch. More than that, and I like to put in a hard stop to bring the team back together to ensure that we’re not going off track. Each iteration should be more refined and have fewer issues. If you find yourself or your team continually revising or revisiting the same things, this may not be a good idea to execute as a graphic.
Part 6: Start illustrating and designing. When do you move from paper and pencil to design?
For me, once the structure, content and data are mostly locked down (this is what we want to say, in this order, this is the data we want to show, and this is how we want to show it) it’s safe to move to design.
Before you start designing, confirm that the data is final. Really, really final. Your designer will love you, you’ll save time, and you’ll make fewer mistakes.
Once in design, keep your design cycles lean and tight.
Remember all the work you put into the initial presentation discussions? Keep referring to the commitments that your team and reviewers made in terms of who sees what, when and how they’re allowed to influence the graphic. (Good luck with that.)
Again, if you find yourself or the team going through too many edits, stop. Revisit whether this project is feasible.
Understand the nature of edits, who’s making them, and why. Too many edits can be a result of:
Too many writers involved (or the wrong people suggesting edits).
Team members brought in to give feedback who were not made aware of the original goals, audience and dynamics of the project.
People (including the designer or project lead) who lack the necessary skills or experience.
Not enough of an overall direction given to team about what the graphic needs to accomplish, for whom and why.
Decision-makers not focused appropriately enough to give careful feedback (I notice this a lot when working with people who are very, very busy. You might see a lot of edits and back-and-forth when these folks aren’t able to focus on the product as a whole, and consequently keep “catching” things with each iteration.
The datachanges.
Rushed timing. Assumptions are made about how long an graphic will take to produce by the wrong people.
Wrong assumptions made about how “easy” it is to create a graphic from repurposed content.
Part 7: Lessons learned.
No matter how successful or unsuccessful your first efforts, you can learn from them. How you impart those lessons to your team is the subject of another post. But suffice it to say that you should keep a close eye on what worked and what didn’t, and get at least an informal sense from all team members involved in order to refine your process and your team for the next time.
Well, that’s it. Go forth, enjoy, and make things easy to understand for the rest of us. And be nice to your designers.
