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Insights from successful collaborative maintenance projects

Tue Jul 9, 2019
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Designing a successful collaborative data maintenance project

User Researcher Sonia Duarte explores the process of setting up and running a collaborative data maintenance project, and discusses the barriers and tips to developing a successful project

What makes a successful collaborative maintenance project? What are the key elements for success and most importantly, what challenges should be taken into account when designing them? As part of our collaborative data maintenance project we’ve carried out some user research to help us learn from the community.

In the discovery phase of our collaborative data maintenance project, we interviewed people working on collaborative digital projects including Wikipedia, MusicBrainz, OpenStreetMap, Zooniverse, Open Food facts and Colouring London. They told us about the challenges faced and lessons learned through developing successful projects, and gave us some tips and recommendations for people designing similar projects. In this blogpost we briefly summarise some of the insights from the interviews.

Our key learnings are: understand your aims and your users; invest in your community; and plan to iterate.

Understand your aims and your users

Three common aims of collaborative projects emerged from our research:

  • Establishing and nurturing a community that collaborates continuously to solve a problem.
  • Producing a meaningful and quality database. 
  • Making data more accessible in a way that can be useful to as many people as possible.

By having a sense of what drives successful projects, and what motivates people to contribute to them, we are hoping to provide some useful guidance for service designers building new projects.

Keeping the community engaged is crucial for the ongoing success of the project, and it is important to bear in mind that not all users are the same. The level of engagement will depend on several things:

  1. Their motivations to contribute.
  2. Any incentives to encourage contribution.
  3. Their specific needs based on their different levels of experience with the project (eg contribution, moderation or data use).
  4. Their experience of interacting with the project.

When talking to the interviewees, we noticed how particular users engage with different community projects. To reflect this, we drafted some typical user profiles:

Typical user profiles of those engaged in collaborative data maintenance projects

Type of userMotivation/driveEngagementNeeds
User that comes to look for information about specific topic or dataInformation, research. Medium/low – drop off might happen once they find the data they’re looking for.Accessible and well-designed features to find the information they need, and up-to-date database.
User contributing because they believe in the value/purpose of the projectBe part of and promote the value of the collaborative initiative.Highly engaged users. They have the potential to become collaborator.Have confidence that they’re contributing in an accurate and useful way.
User contributing because of their expertise in a topicShare knowledge about the topic and get meaningful database.High - they may become validators of the data contributions.Access to discussion channels, access to historical changes and regular events to interact with the community
Casual user that interacts as an entertainmentEntertainment.Might be inconsistent if the task is repetitive or could be more engaged if contributing offers a challenge.Good design, rewarding experience, easy to use.

Invest in your community

In collaboratively maintained data projects coordinating the community of contributors is very important. They are key to growing the database; improving the quality of the input data; and helping to guide and improve the overall project by providing  feedback and review. 

Interview participants mentioned the following challenges:

  • Guiding the community through the collaboration journey.
  • Keeping the community engaged, so they continue to contribute over time.
  • Managing the communication channels and the discussions.
  • Having a trusted community to build a valid database.

Plan to iterate

‘Start small’. One of the most common pieces of advice from experts was to anticipate that the dataset won’t be perfect from the beginning. Starting small and designing to adapt are listed in our principles for strengthening our data infrastructure.

Our experts provided additional tips to help manage expectations as a project evolves:

  • Not every project will be maintained in the same way. It will be necessary to define the project goal and identify the required dataset. Once that is established, you can determine the best way to maintain it.
  • Iterate and learn: Continuous iteration and feedback from the community will improve the database. It will also help teams develop new features that facilitate the successful ongoing contribution and maintenance.
  • Identify, create and work with the appropriate community, engaging with them and address their needs from the start.

Collaborate with us!

Based on our findings we are now hard at work developing a ‘patterns catalogue’ that will capture some of the solutions to common problems encountered by designers of collaborative data projects.

We are interested in hearing from you if you are involved in the design of these types of services – whether you are creating one or iterating on an existing project to make it more collaborative.

We are keen to help by testing the patterns with you during our next round of user research. Get in touch!