Over the last two years, we have been reviewing our design processes, and in line with our strategic priority to “Provide easy, efficient, and responsive community services to support the needs of our community,” we have been improving the DataCite design processes. This post reflects on these improvements, and we plan to evaluate the impact of these design process improvements on new services/functionality.
Open science infrastructure discussions in working groups, task forces, project meetings, and collaborations can be complex. Generally, participants in these discussions have complex ideas from different perspectives, making it challenging to reach consensus on community needs.
The DataCite product design team is acutely aware of this challenge. We believe that creative problem-solving and transparent decision-making can help us to make progress as a community. That’s why since late 2020, we have been using design thinking and user experience (UX) techniques not only to collect and analyze user data but to efficiently build a shared understanding across different stakeholders.
We started by introducing Design Sprints to validate ideas received from the community and solve significant challenges posed by new services ideas. For those unfamiliar with the term, a Design Sprint is a time-constrained, five-phase process that uses design thinking intending to reduce the risk when bringing a new product, service, or feature to the market. As an example, in the planned Harvester service, we used the “How Might We” (HMW) method in the Design Sprint to create a positive framework for resolving challenges described by the participants. The HMW method is used by design teams worldwide “to prevent individuals from suggesting their pet solutions.” At DataCite, we use this method to capture opportunities during lightning talks and throughout the early phases of idea validation. This method allows our product team to take the insights and pain points we hear and positively reframe them.
When we were involved in the design sprint for the [Datacite’s] harvesting service, the “How Might We” question technique supported creativity while keeping the focus on the problem to solve for the user.Britta Dreyer, Head of PID and Metadata Services, Technische Informationsbibliothek (TIB)
Another place where we introduced these techniques was with projects such as the FAIR Island Project. A project in which DataCite’s overall role is to lead the technical development of a dashboard and further extension of the PID Graph.
During this project, we used Lightning Decision Workshops. A Lightning Decision Workshop replaces a typical meeting with a structured and time-boxed workshop, consisting of problem identification, prioritization, and reframing and brainstorming activities. This method helped DataCite structure open-ended, unstructured and complex discussions with a straightforward process, encouraging creativity and fostering innovation.
During the FAIR Island project….going through this [Lighting Decision Workshop] process allowed us to hear directly from our principal users and learn more about their current workflows and the gaps in the types of information and reporting necessary for their work. …..I think the best part of this process was the systematic approach to information gathering, which facilitated the uncovering of critical gaps in the information available to our users and provided an opportunity for the development team to gather requirements and fully understand the priority level of specific feature sets.Maria Praetzellis,
Product Manager, University of California Office of the President, California Digital Library
Moreover, combining various techniques has helped us move complex topics forward through discovery and design phases in multi-organization projects. For example, within the DataCite Metadata Working Group, we used these techniques in one of the various schema changes. We used dot voting (an established tool used to prioritize items democratically), collective affinity mapping (for organizing research findings or sorting design ideas), and multi-staged brainstorming to achieve agreement and clarify the steps toward one of the new schema change recommendations.
While working on the definition of the … [a] metadata schema change, [these techniques] helped us to structure ideas and have a “roadmap” and defined steps. … Moreover, collaborative brainstorming and [dot voting] are techniques that I would like to see adopted in other meetings I participate in.Jan Ashton, Metadata Standards Analyst, The British Library
These methods have helped the Product design team to be more lean and efficient in gathering feedback from our members and stakeholders while helping them move forward with complex discussions. As of today, around 50 stakeholders have participated in these efforts. At the end of the day, these techniques’ primary beneficiaries are the Datacite members and community stakeholders. If you are a DataCite member or a community stakeholder, we hope that you will see the positive effects of the application of methodologies in the variety of services we will launch in future product releases.