We have often discussed that data teams are incredibly diverse — from the choice of their tools to their working styles. So it is no surprise that the same job titles may end up with very different sets of responsibilities. For example, a data analyst in one organization might be only responsible for reporting and analysis, but in another organization, they might be sharing responsibilities with a data scientist.
Even within the same organization, people with the same job titles end up having different workflows. For example, a BI analyst might work solely on creating dashboards from very clear requirements, but another BI analyst in the same organization might have to collaborate with many stakeholders and data engineers to fully understand requirements.
You probably will come across more of these examples as you conduct user surveys and interviews. A key learning will be that individuals need to be treated differently because they have different pain points. In an ideal world, we'd create a solution for every single user problem in the organization, but in reality, our next best shot is to create segments of users who look a lot like each other (i.e. have similar problems), and then find a solution that everyone will benefit from. Your segments are user personas, and problem-solution fit is the use case.
What is a user persona?
A user persona is a data-driven representation of your current users. It's typically a one-page document that depicts a realistic but fictional person who shares similar traits, attitudes, and behaviors with an entire group of users. They are also a great way to present your research findings and communicate "who your users are" to your team and stakeholders.
Create your own user personas
Gather the right data
It is no surprise that the quality of your persona will depend on the quality of your data, so make sure that your foundation is strong. This means, if you haven't already, conduct your user research. You can use tools like user surveys and user interviews to build your insight bible. Grounding your user personas on pure facts rather than assumptions will give you high-value user segments. Make sure that you also record the outcome of all the research in a structured way so that you can go through it later easily.
After conducting user surveys and interviews, you will end up with a wealth of information. This information will be both qualitative (answers to open-ended questions) and quantitative (scores on survey questions). Block time with your team to analyze this information. Try to find common themes and patterns in your data. To begin with, you can segment by:
- Type of consumption: Start by segmenting your users by the nature of their data consumption. Are your end users mostly data consumers — they work with processed data? Or are they data producers — they write queries and perform ETL to get data in a shape that others can use?
- Type of role: Think how similar kinds of roles can be grouped together. For example, can all the roles who do a lot of SQL querying represent one persona, and all the ones who are concerned with ETL represent another persona?
- Type of tool: Try to understand if there are patterns in the type of tools that your end users use. For example, users who use Excel and Tableau (for reporting) might fall in one group, and users who use SQL and create dashboards in another.
Once you've analyzed the data and you have a clear picture of who your users are, it's time to create a one-pager for these personas.
The main points you need to summarize in this document are:
- What are the broad categories of each persona?
- What are their main goals?
- What challenges do they face in achieving these goals?
- What are their current user flows?
- What is your desired user flow for this persona on Atlan?
You can find more examples in the user persona template guide that we have put together for you.