Tips for creating user personas for open data

Use this guide to compile ethnographic observations into a format that allows designers to relate to users at a glance.

Introductory sentence or two? (Could pull from background)


In cities, residents navigate service ecosystems. When considering transparency at the local level, it is important to keep in mind that service ecosystems are complex. The data about these services is dense, and in order to communicate the information to many people, good communication design is essential.

A healthy open data economy means municipal government should be proactively sensitive to demand and access. Creating personas creates a shared language for internal staff, other municipal agencies, and external partners. Throughout the process of creating open data user personas, there are several touch points between designers and users, which remove barriers to empathy.

That’s why we have put together some tips to help you generate effective open data personas. Good open data user personas may take several different forms, and may be relevant to specific agency or citywide objectives. Personas are one among many tools that can bolster open data programs, and that in this context they are a means to an end.

What are the end goals of creating personas?

  • “Improve accessibility and usability of data to engage a wider audience.”
  • Multi-stakeholder collaboration
  • Efficiently communicate residents’ roles and goals
  • Improve user-centered design decisions
  • Foster proactive supply of open data
  • Check design assumptions

Acknowledging time and budget constraints, we offer a suggested short design sprint below.

Step 1: User Discovery

Effective personas begin with research, which means you have a good reason to go out and talk to residents. The point of user discovery is to elicit stories that can emerge in patterns. Then we have to identify those patterns, which are displayed in user personas. In the process of generating personas, scheduling and conducting interviews will take most of the time. Two to four weeks is a good window for user discovery sprints. [EXAMPLE]

1A Team Preparation

  • Identify a 3-4 person team to work on all stages of the process of creating personas for open data. As composite products, personas are best assembled by team members that bring different expertise across technical and social sciences disciplines.
  • Narrow the scope of the design problem before starting user discovery by considering city objectives.
  • Identify key lines of inquiry about a problem and its context. Key lines of inquiry might be motivated by a citywide or agency objective, such as ending homelessness. [EXAMPLE Austin]
  • Make preliminary personas quickly from your team’s present knowledge. As a team, think about preliminary categories of users. This is not meant to cover literally everyone. Who are the open data users? What are people going to get out of open data? What do they need from the open data portal? What is their role in their community with regard to open data?
  • Test assumptions by trying to find information on the portal as if you were one of the personas

1B Conduct Key Informant Interviews

  • Key Informants may include local community leaders, relevant community organizations, civic tech groups, or relevant city staff in other departments. Often there are small groups with loud voices that are disproportionately represented in design thinking. At this stage, cast a wide net to learn who is out there, what their problems are, and what support they are in need of.
  • Allow key informants to tell you about the open data landscape. KIIs should comprise of mostly if not entirely open ended questions.
  • No more than 5 KIIs are necessary.

1C Conduct User Interviews

  • We suggest conducting 20 interviews. More is even better.
  • The snowball method is a good way to find interviewees. KII’s may identify people to interview, and those initial interviews may then lead to further contacts for more interviews.
  • We suggest conducting qualitative ethnographic semi-structured interviews.
    • There are a lot of resources about conducting ethnographic research.
  • Interviewing in teams of two allows one person to focus on asking questions and maintaining eye contact with the interviewee, and one person to take notes. (Taking notes is more efficient than making audio recordings, and also recordings may make the interviewee feel less comfortable.)
  • Ideally, create a template based on your key lines of inquiry with basic fields that you can fill in as you conduct your interview.
    • Have an organized note-taking sheet that is coded by key lines of inquiry you want to cover.
    • DETAIL and SPECIFICITY are essential to record.
  • In the template you can divide the page into two sections: Observations and Interpretations.
    • An observation is something that anyone can verify empirically. In a user discovery interview, an observation may be about body language, tone of voice, reporting a quote.
    • An interpretation is the story we tell in our heads, which we especially tend to do about things we can’t observe.
    • More interpretations can be developed at a later time, but it is harder to collect more observations without conducting more interviews, so recording observations is important.
    • Taking the time to rigorously reflect on distinguishing between observation (what is empirically verifiable) and interpretation (the story we tell ourselves) can help to avoid biased stereotyping.
  • Prepare a question guide.
    • Based on key lines of inquiry, come up with five key questions that you want to know about every interviewee.
    • Prepare five additional questions specific to the context of the interview.
    • Don’t follow the question guide precisely; allow the interviewee to guide the conversation and tell his or her story.
    • Ask open-ended questions that don’t have short answers, i.e. “Why do you use open data?”, “What is your experience with city services in general?”,
    • Follow up with “How?” and “Why?”.
  • Make sure participants know that they are valuable to the research, especially if they are not current users. Think about what questions might acknowledge their relevance to the research, i.e.  ”What are other times that you have solved a problem, and how?”.
  • Keep in mind the goal of the research while conducting user discovery. What would people like to solve? What are the current and potential use cases?
  • Staying aware of Key lines of inquiry may surface goals, values in the community, technical skills, non-technical skills.

Step 2: Synthesis

The most challenging aspect of generating personas is taking information from many observations, and distilling it into one fictional, yet realistic and research-informed person. Synthesizing can be completed in one full day meeting.

2A Meeting Preparation

  • Reconvene as a full team to synthesize interview notes into patterns.
  • Set the agenda for the synthesis meeting before starting. Lay out your initial questions, or design problems as a city or agency. Share and display the key lines of inquiry–what characteristics do you want to compare–to define and narrow the scope. In other words, make sure you explicitly articulate what you want to use the personas for, as a group as you sit down to synthesize.
  • Each team member should prepare for the meeting by reviewing interview notes, making comments, and beginning to look for patterns.

2B Use affinity mapping to identify segmentations

  • Write down observations on blue post its
  • Write down interpretations on yellow post its
  • Write down patterns on green post its
  • Make sure the team is more or less on the same page about observations and interpretations.
  • Mark two axes of interest on the wall. Axes might be, technological ability, proximity to local government, or community impact, each ranging from low to high.
    • Merging themes and patterns is easier when there is a framework for organizing data. This will give the team a sense of what to look for and pull out for the personas.
    • The segmentation should allow you to compare characteristics across different segments of users
  • Experiment with placing the post-it notes on the axes.
  • Allow the details to coalesce into clusters along the axes of interest.
  • Look for patterns and themes within clusters. Which observations resonate together?
    • Common relation to tools
    • Common behavior
    • Common community connections
    • Common knowledge
    • Common motivation
    • Common barrier
    • Common ability
  • It is normal to identify a theme and then throw it out. There are many possible ways to group attributes, and it is important to feel comfortable in exploring different ways.
  • With key lines of inquiry in mind, try to solidify approximately five categories of interest across all personas
  • Individual interviewees should not be recognizable in the process affinity mapping. This allows for abstract patterns to emerge.
  • Personas should not present averages from research.

Step 3: Persona Generation

Good design is inherently creative. These tips are meant to encourage anyone working with open data in a smooth process of turning empirical data and analysis into ideation and fictional representation.  Generating user personas entails a jump from data to imaginary characters. We created this toolkit to instill confidence.

EXAMPLE: WPRDC points out that making personas should be fun.

Generate personas in the same meeting as the synthesis or during another day.  

Distill segmentation information into composite individual persona

  • Keep in mind that characteristics are the useful component.
    • Strip back demographic detail to a minimum, leaving aside occupation titles or group affiliations, while highlighting roles, motivations and needs. Think creatively about how to present the characteristics.
  • Think creatively about titles that describe roles (not just occupation).
    • What do you want to compare across the open data economy?
  • Include capabilities
  • Include Incentives
  • Include personal anecdotes
  • Include a picture
  • Then balance with little details from observations to build out persona as a real relatable person.
  • Once you have a title and some characteristics for each persona, think about their Network and their position in the open data ecosystem/economy
  • Create no more than 6 personas.
  • Look at models of existing templates

Step 4: Persona Use

4A Use Personas to Impact Open Data Economy

  • Reference personas while considering portal redesigned
    • Which datasets will you prioritize to make available?
    • Which city departments will you partner with to update the open data portal?
  • How can you parse and present open data in a way that is easy for most users to find.
  • Roles and priorities change over time, and also vary from one agency’s use cases to the next. Personas should be revised and updated after a couple years to make sure you are in touch with communities.
Get in touch

Are you using user-centered design for open data? Do you want to know if we have any resources to support you? Get in touch!