Building Community

And Engagement

Around Data

In the context of politics, media, and tech, we tend talk about “community engagement” like it was any other bit of buzz: an abstract concept made to be nodded at during meetings and then “solved” by tossing around some social media accounts and page-hit or API-call metrics and calling it a day.

But if we actually value the idea that real human beings can come together in the creation, use, and stewardship of technology and data, we need strategies that enable that activity—strategies that create opportunities for “engagement” that are meaningful, not abstract.

What follows is an exploration of deprofessionalizing and debuzzing the whos and whats of “community engagement” generally and in the context of data specifically. We’ll start with the former because — spoiler alert!—the basic tactics are the same.

Our goal is to impart an understanding of “data engagement” that is simplistic, but honest, and that enables those just setting out in the creation of an engagement strategy to move forward with practical next steps.

This article is the product of…

(1) a workshop I ran in London at Mozilla Festival in October 2014 (“Offline: Bridging Digital Divides in Open Data”), and

(2) a talk I gave at Washington, DC’s first Data Literacy Bootcamp during Open Data Day 2015 (“Building Community And Engagement Around Data”) that was created based on the conversations and outcomes of the Mozilla Festival workshop. (Most of the images are slides from this 2015 event.)

Many thanks to those whose thoughts, impressions, and inputs have shaped the content along the way.

Engagement is often talked about as A Thing, a specific, singular activity that a person can be doing or not doing—or something that can be done to a person. (“Our work engages voters.”)

But this framing takes for granted what the definition of engagement literally is: people paying attention and interacting with each other and with a particular subject.

Basically: People doing stuff.

Most engagement strategies are founded with this basic understanding: We want to incentivize and capture human interaction with the work we put out in the world (data, tech, other). We want to give folks a shared sense of ownership and investment in our work. We want people to care as we care. We want people to do stuff with our stuff.

The problem is that it’s hard to envision any other way of engaging with what we’ve created than the way that we engage with it. So we put feedback forms on data portals. We invite people to edit documents on GitHub. We create the opportunities that we wish were available to us from the start, and then stop.

Comfortable wielding hammers, when it comes time to hang art on the wall, we forget not only the utility of screws and screwdrivers, but that they even exist.

When you have a hammer, everything looks like a nail, so it makes sense that the parallel strengths and merits of the humble screwdriver are hard to see…unless you take the time to make yourself.

If we’re going to talk concretely about engagement with data, then we have to get comfortable talking about inclusivity and exclusivity—the people whose actions we enable and those we do not through the engagement strategies we choose.

In The Participatory Museum, Nina Simon cites a study by Forrester Research on the diversity of roles played by online audiences who use “social technologies”. The researchers grouped the folks interacting with the tools used in the study by 6 categories:

Simon notes:

These percentages add up to more than one hundred percent because the categorizations are fluid and many people fall into several categories at once. I fall into all of the first five categories. I’m a creator when I blog, a critic when I make comments on others’ sites, a collector when I assemble “favorites,” a joiner on many social networks, and a spectator when I consume social media.

Read the list again carefully and consider your biases. Some roles may read as more “active” to you than others, but each should be considered equally valuable.

Remember, all engagement means is “people interacting with [your] stuff”. People interact, attend, learn, and do in a myriad of different ways depending on what they’re interacting with, who they are, where they come from, how they feel at the moment, etc.

How people engage and whether they engage has to do with how openly or narrowly you, as a creator, structure opportunities for engagement. When it comes to data, if you only value certain forms of engagement that favor your “creators” (developers, data providers, etc), you constrict the potential for other kinds of participants to feel substantially included enough to dabble in different roles—and you affect the size and diversity of your participant pool.

Simon continues:

Some people believed that the ease of Web-based publishing tools would turn everyone into a journalist, a musician, or a contributor to a wiki. But that’s not the case. There are some people who are drawn to create, but many more prefer to participate in other ways, by critiquing, organizing, and spectating social content. This isn’t just a question of making creative tools as easy to use as possible. There are some people who will never choose to upload content to the Web, no matter how easy it is. Fortunately, there are other participatory options for them.

How people engage has a much to do with their natural inclination as their identity: in reviewing the study above, Simon discusses how the percentages of activities change dramatically when viewed based on country, gender, and age group. In light of this, it’s especially important that we consider the final group listed—the “inactives”.

Don’t let the arts and education schtick throw you: although this text reviews engagement strategies for museums and libraries, specifically, the insights are applicable across-disciples. You can read The Participatory Museum online or buy the book here: http://www.participatorymuseum.org/

Inactives are defined as those who don’t visit or participate in the digital space that was studied. When we start from a constricted view of engagement, it’s easy to see everyone who doesn’t act the way we act as “unengaged” or inactive. However, as we break engagement down into more varied, more human, and more realistic kinds of behavior, we begin to get a clearer picture of what inactivity means, too.

Inactive participants might be those choosing not to engage with our stuff or they might be folks who have simply not been invited to participate—systematically, explicitly, or otherwise.

When we set out to build opportunities for engagement, we must keep in mind both the idea that there are a variety of active participants with equally valid and varied desires for engagement and that, depending on the approaches we take to cater to the former, there will be inactive participants that we exclude (purposefully or incidentally).

The truth is, no engagement strategy will reach “everybody”—not literally. But we can’t make informed decisions about who to target and how if we can’t get comfortable not just with the need to design for inclusion, but the understanding that real inclusion involves give and take.

We might want an engagement strategy for our data that particularly motivates creators or critics, but without mindfulness about who currently feels welcome to engage with data in these ways, we can’t design engagement opportunities that invite “inactives” and other participants to explore these roles, too.

In order to beat our biases and create opportunities for inclusive engagement with data, we have to address how we think about and talk about data.

Data is information — digital information. Full stop. Open data is about making that information as accessible as possible to as many people as possible. Although there are far more technical definitions for both, if we approach engagement design from this simpler, more common frame, “data” transforms from a specialized thing that requires specific specialized skillsets to a universally comprehensible good that encompasses many skillsets and many actors—and that comes with a legacy of human work and import.

Aliya Rahman, second from left, at New America’s “Technology for the People, by the People”. February 18. 2015. Image credit: New America Foundation

In a recent panel discussion on issues related to talent, diversity, and the technology industry at the New America Foundation, Aliya Rahman, Program Director of Code for Progress, referred to Ida B. Wells as her favorite data scientist. In the late 1800s, Wells traveled around the country investigating the lynchings of black men and women, scrutinizing the charges given to justify the murders versus the events that actually took place.

No, she did not have a PostgreS [open source] database…But, she, to me, is one of the most innovative people ever because she found a way to look at data science — and she actually did data science, right? she calculated means and everything — and make social impact with it.

There times when it makes sense to keep our definitions of data and those who wield it specific and modern in way that we understand “modern”. But if our goal is engagement around data, if our goal is to create opportunities for a variety of different people to interact with data and for them to interact in a variety of ways as they are so inclined, then it is practical for us to start with an understanding of data that itself is more inclusive than exclusive.

Ida B. Wells, data scientist, investigative journalist, social justice warrior.

This exercise in expanding our understanding of data in order to make engagement opportunities less obtuse (and specific) and more concrete (and expansive) is part of the idea of moving at the speed of inclusion.

The pace of technological development has infected our expectations of everything that touches technology—from policy to engagement strategies to the creation of tech itself. Speed perpetuates speed. In 2015, we are more interested in rapid prototyping than we are in questioning why we are prototyping, if what we’re prototyping has signficant potential for real-world impact, who is involved in the prototyping process, and who isn’t, but should be.

In contrast to this tool-centric perspective, which prioritizes production above all else, the speed of inclusion is a normalized pace of operation that incorporates mindfulness and review as inherent to its process, with the understanding that only through awareness can we create and wield the right tools for the right reasons.

When we design effective engagement strategies—around data or any subject matter, really—we must build at the speed of inclusion and measure our success by this same standard.

Let’s apply this perspective to strategizing engagement with data.

To put the learnings above into action, your approach to designing an engagement strategy should follow three steps.

STEP 1: CLARIFY YOUR VISION: Go through the exercise above of simplifying the subject/data you’re working on, then consider what engagement with it means using our simplified definition of “engagement.” Take note of all the kinds of activities (i.e. coding, scraping, searching), identities (moms, youth, entreprenuers), and roles (developers, researchers) that come to mind. You’ll need them in a second.

Ask yourself: Why do I want people to engage with this data? What will they get from it? What will happen/what will the world look like if there’s more interaction with this data?

STEP 2: CONSIDER POTENTIAL ACTORS: Think (and even list out) through the variety of ways people interact with information (data), generally, and the data of interest specifically.

Just as we explored the world beyond creators and critics in our review of social technologies, stop to consider whose engagement you can see (or preferentially value) and whose you can’t see (or don’t value as highly). When you encounter compound roles (i.e. a developer who advocates for the release of this data), list them separately (developer, advocate). Add your list of activities, identities, and roles from Step 1.

Then, review: Which actors are listed most often? Which actors are listed least often? Does this list of actors reflect the vision I outlined for engagement? For the kinds of actors that don’t seem to fit the vision, are there actors listed that could serve as intermediaries, identifying or clarifying how/whether to engage with the groups that, in this exercise, don’t immediately seem to fit?

STEP 3: IDENTIFY INTERVENTION POINTS FOR ENGAGEMENT

Depending on your field and frame of reference, different opportunities or intervention points for engagement with data will more prominently come to mind before others. Technologists tend to be interested in Creation, Standardization, “Use,” and Advocacy; journalists focus on Advocacy and Analysis; data policy folks, in addition to some of the other categories listen, often add Prioritization into the mix.

Think about these activities as individual platforms for engagement. Each will naturally draw certain kinds of actors, but can also be open to others if approached at the speed of inclusion.

Outline what kinds of opportunities fit the vision for engagement you laid out in Step 1. Then review your list of actors you identified in Step 2 and add any additional kinds of engagement implied by their roles. Review the participatory online audience guide above and make sure you’re not underestimating or obscuring methods of non-technical engagement. (For example, if “moms” made it all the way to this stage as a type of relevant actor, consider what roles moms play in their communities—educators, connectors, and so on—before attempting to connect them to any particular bucket of engagement opportunity.)

Progress from this point is hard to generalize, as the tactics you’ll want to wield will be more specific to the vision you outlined in Step 1, the kinds of people you’re looking to connect to from Step 2, and the opportunities you identified to potentially activate those people in Step 3. However, some resources that might help you begin to activate and refine your plan include:

  • Why Open Data? — crowdsourced series by the Sunlight Foundation that offers responses to common challenges posed against open data (great for engaging with data providers and advocates)
  • Crafting Civic Tech—kickstarter guide for community collaboration on civic technology with case studies and a deeper dive on tactics for spurring interaction (great for engaging with connectors, organizers, and developers)
  • So You Think You Want to Run a Hackathon? Think Againtactics for leveraging community space for data and tech exploration and training your brain to think out-0f-the-box about what “data engagement” can look like (great for engaging with educators, creators, and connectors)
  • Code for America’s Event Guide—event formats for civic-hacking communities (great for developers and advocates)
Stock photo of “community.” I hate this picture.

Note that we haven’t much dug into the idea of “community building”. That’s because “community” is not a meaningful unit for us to start developing opportunities from engagement from. Undefined, “community” is a just an abstract mass of humanity. We can throw abstract engagement tactics at it all we want, but they’ll never connect in the meaningful, inclusive way that we intended to cultivate at the start.

Even if we do go so far as to define “community”, if we simplify it and drill down to the notion of “specific groups of people who share similar interests, investments, and spaces (be they physical or digital)”, I would argue that the goal of data engagement isn’t to build community at all. It’s not our job to tell people what to care about. It’s our job to meet folks where they are, in the communities they’ve chosen, and integrate ways to engage with data (advocacy, creation, prioritization, analysis, visualization, dance, you name it) into their contexts.

This is the ethos of “build with, not for”—the idea that, when it comes to public interest work (generally and regarding technology specifically), we must identify the real people our work is intended to benefit or engage and we have to literally work with together. Without collaboration, without valuing the diverse contributions and skills of the variety of actors involved, how can we achieve “engagement”, let alone social impact?

Picard loves it when “engagement” transitions from an abstract idea to concrete, collaborative action. Loves. It. Also, this was the final slide from the Data Literacy Training. All Picard, all the time.

Remember: especially when it comes to something as complex as data can be, if you want to meaningfully involve and earn the investment of real people in the work, you can’t act like you only have a hammer.

You have a toolbox.

Your data engagement toolbox. Not pictured: chewing gum, tooth picks, and sundry Macgyver additions.