Making research insights actionable with a RACI matrix
A way to assign clear responsibilities and to better act on research conclusions with a simple chart
After a user research is concluded, ideally people will act on the conclusions straight away, but often is not clear who needs to start on what. This short post focusses on applying a project management tool to the user research process to better communicate actions and next steps.
What is a RACI matrix?
A RACI chart is a representation of the responsibilities of a group of people involved in a project, so that it’s clear to everyone what’s their remit and scope within the project.
It consists in assigning one of this four roles for each tasks of the project:
Responsible: the person who is actually completing the task
Accountable: the person who supervises the completion of the task / makes sure the task is completed
Consulted: the person who supports the completion of the tasks
Informed: the person who will be updated on the progress and the completion of the task
The chart shows a matrix where the rows represent the single tasks of the project while the columns stand for the actors/team members involved.
This model can be a nice way to represent the user research insights in a way that is immediately clear and actionable. Ideally, after seeing this, everybody can answer to the question “What can I do tomorrow with this information?”.
There are few tweaks that might be applied to the chart in order to fit this scope.
1. Flexibility with actors involved
The official approach suggests to define for each task a single team member who is responsible or accountable, and one or more actors for the role of the informed and consulted.
This allows to identify clearly the workload for each person and help to better set boundaries between responsibilities.
However, user research insights are rarely tied to individuals, but more often to teams or to a department. This information usually informs and triggers actions in groups of people.
For this reason it might be better to adjust the chart based on the organisation structure and workflow.
In the image below there is an example of how a RACI might look with user research insights instead of tasks, mixing individual and group as actors.
The actors might be picked up in the organisation across disciplines / horizontally (UX, Product, Dev, etc.) , or by seniority/ vertically (Senior UX, UX Lead, Head of UX, etc.).
2. Combining roles and actions/tasks
In the RACI charts they are usually represented the roles of the team members (columns), while the actions are already defined in the tasks (rows).
In our case we’re not talking about a list of defined tasks, but we are dealing with a piece of knowledge, which is vaguer and less actionable by itself as it doesn’t imply any activity.
To avoid confusion, we need to specify the task(s) or action(s) suggested alongside with the responsibilities that we’re defining.
Using this model forces to pick up just the insights that are actually leading to some actions and condense them, which is another useful exercise.
Below an example of tasks and roles in a RACI.
3. Narrowing the scope
When a user research ends, often the next steps and recommendations include further investigations and analysis, with consequential other decisions and actions.
This will complicate the chart as it’s not designed to show dependencies or support decision mapping.
It’s preferable to keep things simple and just represent in the chart just the tasks that can follow immediately the release of the insights.
In the previous chart for example, “New context information around a user type” is represented as a research insight in the second row. The main action is to understand/investigate if the persona is worth exploring and eventually (but just in a second time) if it triggers any changes in priorities and roadmaps.
So the chart just covers the initial action (decision) and the other actors that can contribute or be informed around it, but it doesn’t mention any future evolutions of the decision itself.
What happens next “next” - after it’s clear if this user type is important or not- will find space elsewhere in the report, in a more hypothetical scenario.
It’s better to envision additional future developments in a different manner and keep the matrix clean, so everybody knows what needs to be done immediately with the information.
Conclusions
The RACI matrix is a tool like many other that can be used from researchers to better communicate the new knowledge to other groups.
It’s immediate and it set expectations on what it can be done with the insights right now.
It might not be possible to always apply the original approach, but if this representation helps creating a bridge between research reporting and teams/groups actions, it is definitely worth exploring it.
