Step 5 in the Data Exploration Journey: The Magic of Collaboration

This article is part VI in a series on data exploration, and the common struggles that we all face when trying to learn something new. A list of previous entries can be found at the end of the article. I began this series while serving as the Director of Education for the Data Visualization Society, because so many people were asking to hear more about data exploration and the process of learning data vis. The series follows along with an exploratory project on the State of the Industry Survey data. It illustrates how I approach a new project, and the fact that no “expert” is immune from the challenges and setbacks of learning. In addition to working with a new dataset, I also used this project to take my first steps toward learning R. Let’s see where this journey takes us!

After several articles about data exploration and the “expand” stage of the survey data tools analysis project, the last installment identified the first steps of transitioning into the focus phase. After a big, unwieldy exploration process, focus is a time to cut things down, to work on the immediate task at hand, and to move forward on a single, manageable piece with a much tighter scope. 

Image of the double diamond model which shows the steps in tackling design projects. The leftmost point of the first diamond is "question, interest or idea." The designer then travels through the "expand/ideate" phase to reach the top of the first diamond, which is "max risk of overwhelm." Then, the designer travels down from that top point in the "focus/consolidate phase," which is the phase in which this column about collaboration is most relevant. The next phases (in the second diamond) are in light gray font, suggesting that those steps are yet to come, but they include "building/producing" and "deliver/deploying").

This is also a great time to look for a collaborator, especially if there are core skills that you don’t have. Some people are game for an open-ended collaboration, but I find that most people respond best when you have a clear idea of what you are trying to do. The end of the focus phase is a great time to make the ask. It’s a lot easier for someone to understand whether they’re interested and to set clear expectations for your collaboration when a project is already well-defined.

Why collaborate

A good collaborator offers companionship and a fresh perspective, and can help balance your skills. They might like something you don’t, or think in ways that feel foreign to you. Or, maybe you’re 100% in sync and it’s just nice to work with someone who likes to work the same way you do. There are many reasons to collaborate: 

  • Opportunity to learn, and to share skills. For me, the best collaborations happen when each person brings a different toolset to the project, and when everyone has something to learn. Most projects move a lot faster with a mix of skillsets; look for teammates whose technical, professional, or project management strengths balance your weaknesses, and vice versa. 
  • Spend time with people you like, doing things that interest you. Collaboration can be a great excuse to get together and work toward a common goal. Sometimes it’s worth cooking up a project with a friend for just this purpose. Other times, the project comes first and the people follow; either way, it’s a great way to get to know people or deepen an existing relationship (as long as your expectations are aligned!). 
  • Meet people with similar interests. Sometimes, an interesting project can be a way to start a new relationship with a stranger or someone that you don’t know well. Especially if you’re an introvert, working together on a project can make it a little less intimidating to get to know someone new. 
  • Support a common vision or cause. Some collaborations form around an idea, a principle, or an organization. There’s magic in people coming together for the sake of getting things done. 

Like any relationship, there are as many ways to collaborate as there are people, and every collaboration will be different. To me, an ideal partnership is one where both parties feel like the other one is doing all the hard work. That’s a sign that there is a good match in expectations and contribution effort, and a strong balance of skills.

Finding a collaborator

Lots of people struggle with finding the right collaborator. Fortunately, organizations like the Data Visualization Society (DVS) provide a great place to connect with other people, and many even provide structured activities and events to encourage collaboration and pairing up. The following tips might help to turn those opportunities into something more concrete:

  • Be visible. People can’t want to work with you if they don’t know that you exist. Lurking might help you feel close to the people who are speaking, but they have no way of even knowing that you’re there. Make sure that you’re actively participating in shared spaces to make it easier for others to reach out. 
  • Build relationships first. Sometimes you find the perfect person just as you start up a new project, but most collaborations happen between people who are already at least acquainted, if not friends. Collaboration is a lot of commitment, and it’s easier to work with someone you know well and can trust to keep up their side of the bargain. Keep this in mind, and try to connect and establish relationships with people before you ask to collaborate with them.
  • Focus on similar interests and approaches, or the unique aspects of your work. There are lots of personalities and styles out there; you want someone who matches your mindset and commitment level, or whose work complements yours (either in style or skillset). Never underestimate how important basic compatibility will be for ensuring that your collaboration makes it to completion. 
  • Take advantage of formal structures. There are tons of mentoring, collaboration and networking opportunities, and many are organized specifically around the idea of finding collaborators. Conferences, in-person meetups and online communities are all good ways of finding people to work with. Joining a committee or volunteering for a project are also great ways to meet folks with a common goal. Challenges, competitions and awards events are a great way to get your name out there and show people what you can do.
  • Think about how you present yourself. Whatever your method of searching, it’s important to pay attention to how you show up. Other people may not know you or your work; what can you do or say to help them see that you’re a good match? Is there a quick and memorable way to help them connect with your work?
  • Expect it to take time. Good collaborations very rarely spring up overnight. You should work on building (and maintaining) relationships constantly, because you never know when something will come up. If you wait until you want to start a project to think about meeting people, you might find that it takes quite a while to find just the right partner. 
  • You’ll probably have to ask first. Multiple times. The world is a busy place, and there are always a million opportunities competing for our attention. Even when you find someone who’s a great match, it may not be a good time or the right project for them. If you treat “no” as a failure or a reason to get discouraged, you probably won’t keep at it long enough to find the right person. Don’t play cool and detached on this one; put yourself out there and show that you’re open to collaborate. Making it easier for other people to find you will increase your chances for success. 

Asking for a collaboration

Let’s say you’ve identified a collaborator and you’re super excited, but you’re not sure how to approach them about it. What should you do? 

  • Be honest about your expectations, and your limits. A clear ask is often a critical ingredient in a good collaboration. Be up front about what you do and do not want from this person, and be thoughtful about outlining your own contributions and time. Honesty at the beginning helps you both to decide if this is likely to work out. 
  • Identify a reason that you want to work together. Remember that most people have a lot of opportunities for projects and collaboration. What’s interesting about this one, specifically? Help them to understand what they would bring that you need, and why you are interested in working with them
  • Agree to specific goals or outcomes for your project. It’s always a good idea to set some specific objectives at the beginning, to make your expectations more concrete. Approaching someone in the focus stage of the project means that you have a lot more clarity around what you’re trying to do, and makes it easier for someone to tell if they can (and want to) help. 

Pitfalls to avoid

There are also many ways that a collaboration can go wrong. Most of these come down to mismatched expectations, whether that’s around work ethic, project goals and timelines, degree of involvement, or the specific tasks that people take on. Before you embark on a collaborative venture, it is a good idea to pause and talk with your collaborator(s) explicitly about what your collaboration is not. It’s easy to focus on what you will do in a new relationship, but it’s equally important to set expectations for what you’re looking to avoid.

You don’t need to overdo it with rigid rules, but even a brief conversation can help to set the stage and provide a starting point for clear communication later on. Here are a few things that might trip you up:

  • Don’t make others responsible for your project. A lot of people look for collaboration buddies to help them “stay accountable” or to “keep them motivated.” This is usually a huge warning sign that you are not taking ownership for the success of your own project and management of your own needs. Shared projects are great for motivation, but finding a collaborator does not mean outsourcing the hard stuff to someone else, or counting on them to carry you through. 
  • Collaboration is not a way to get free help or work from a professional. Lots of people know how to do what you want to get done, and they probably get paid to do it. Unless there is a clear professional benefit to working with you, don’t expect someone to sign on for something where they’d usually get paid.
  • This is not a fun-only zone. Most collaboration involves real work, and there are going to be times when you don’t feel like doing it. In some cases, a project might even involve disagreement and conflict, especially when you’re getting things set up. It won’t always be easy and it won’t always be fun, but a good collaborator shows up—even when it’s hard. If you or your collaborator are only conditionally committed (“I’ll do it if I feel like it or if it works for me”), make sure that you are clear about that up front and that you’re both ok with it before you proceed. 

Focusing on the survey project

After going through the focusing exercises for my survey data project, it became really clear that I could benefit from a collaborator who knew something about R. (Pro tip: any “focus” list that begins with “learn a new piece of software from scratch” is more than a bit suspect.) Fortunately, working on a project for the Data Visualization Society gave me an opportunity to find a collaborator who had exactly the skills that I lacked, and who was interested in working on a project that would help to move the organization along. When I started publishing articles and talking about the work that we were doing, Jenn Schilling followed up about joining my committee to help out. Jenn came into the project with extensive R knowledge, and was interested in exploring the survey data and getting more involved with DVS.

What we each brought to the project:

  • Erica:
    • Early data exploration, experience working with data and familiarity with the DVS survey, connection to other DVS initiatives.
    • Strategic guidance on which analyses to pursue, curation of final project outcomes and goals. Focus, brainstorming, and general process support.
    • Advanced design software skills for cleaning up and presenting final work
  • Jenn:
    • Advanced R knowledge and quick data shaping skills.
    • Deep knowledge of data analysis and handling methods, which opened up new opportunities for how we think about the analysis and goals.
    • Advanced visualization skills in R for creating complex visualizations quickly.

Benefits of collaboration:

  • To the project:
    • Faster analysis, deeper insights. Because we had more advanced R skills on the team, we were able to go a lot farther in extracting insights from the data than I ever would have been on my own. 
    • Second opinion on tough calls. There were several times where we had to make tough decisions about what to cut and what to keep, or how to visualize the data. Having a second set of eyes and another expert to discuss with made it easier to choose the right approach. 
    • Tighter focus. It’s easy to convince yourself to go off on a crazy tangent, but harder to spend someone else’s time on something that might not pan out. That extra accountability helped us to keep things tightly focused and on track, even when there were lots of interesting side paths that we could have explored. 
  • To Erica: 
    • Learning R by example. It is always helpful to learn code by example. Watching the analysis develop live (and spending hours taking it apart and trying to replicate and understand it myself) taught me a lot about how to approach problems in this language. I learned far more and much faster than if I’d relied on tutorials alone. This also meant that the project progress wasn’t handicapped by my coding speed or ability to understand the language.
    • Fun to work with someone. It’s always nice when you are in a collaboration that just works. It’s really fun to balance the workload with someone else, and to see things develop faster as a result. We developed a really good cadence for handing work back and forth, and it kept us focused on delivery and getting things done. 
  • To Jenn:
    • Learning design by example. I really enjoy collaboration because everyone thinks and approaches data in slightly different ways, and I learned a lot from the way Erica created prototypes of visualizations and iterated through the design of the report. I have more experience generating dashboards and one-off visualizations than with design and comprehensive reports. So, I benefited from experiencing Erica’s design skills as we worked together.
    • Fun to work with and learn from someone. Our complementary skillsets taught me a lot—I learned more about working with design software and the design process overall from Erica. As Erica mentions above, we had a great rhythm for our work as we passed ideas and work back and forth. It was also a great opportunity to get to know each other and develop a friendship through working together. 
    • Increased involvement in DVS. I wrote my first Nightingale article as a result of our collaboration, and our collaboration will result in the first Career Portraits report! I also got to meet and interact with other members of the DVS leadership and community through our collaboration. It’s been great to get more involved with the organization!

Hopefully we’ve convinced you to give collaboration a try. Check out upcoming DVS events for opportunities to connect…you might make a new friend! 

Previous articles in this series:

Embrace the Challenge to Beat Imposter Syndrome
Step 1 in the Data Exploration Journey: Getting to Know Your Data
Step 2 in the Data Exploration Journey: Going Deeper into the Analysis
Step 3 in the Data Exploration Journey: Productive Tangents
Step 4 in the Data Exploration Journey: Knowing When to Stop

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Erica Gunn is a data visualization designer at one of the largest clinical trial data companies in the world. She creates information ecosystems that help clients to understand their data better and to access it in more intuitive and useful ways. She received her MFA in information design from Northeastern University in 2017. In a previous life, Erica was a research scientist and college chemistry professor. You can connect with her on Twitter @EricaGunn.

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