This is part three of a six-part series dedicated to sharing cross-functional ideas for design thinkers, data practitioners, business intelligence analysts, researchers, policymakers, and subject matter experts to better collaborate. In that spirit, we want to hear from you! We’d love to hear your thoughts via the three questions we’re added to the bottom of this article!
In our last installment, we suggested that organizational data visualization work can be light on methodology and we proposed a high-level framework to break the silos between practices, roles, and the people that inhabit them. Here we examine data literacy as foundational to achieving Information Empowerment.
Community research is a means of gauging data literacy–which we think may be an overlooked concept. Design assets like user personas and journey maps are tools that designers can use to link data collected from subject matter experts to development team activity. In this way, designers act as translators and even project managers. While it’s certainly possible to develop websites and digital applications without design and without community/user data collection, these efforts often miss their mark because they are not informed by context. Organizing around a specific initiative affords a literacy opportunity for both the community AND the practitioners. Roles like design and UX research can provide translation across the team. In the case study below, the designer, the UX researcher, the users, and the developers worked together to successfully deliver the creative brief.
What does it mean to organize around an initiative? As a case study, consider a municipal effort whose goal was to improve access to early childhood education and care for low-income residents. Pre-pandemic, one of Mary’s client projects was to develop a comprehensive resource for parents and caregivers in Detroit. The project involved streamlining the online financial eligibility inquiry and application processes. The visualization, in this case, was both a web interface and a mobile application. Early childhood education and care resemble a “marketplace” model in that stakeholders include parents and caregivers, service providers, and government funding sources. The project was conceived in response to nearly 60 percent of Detroit’s three- and four-year-olds who were not in preschool (source). A lack of awareness, complex eligibility requirements, and a burdensome application process were all contributing factors.
In an effort to improve our team’s data literacy, we sourced subject matter expertise throughout this process. We conducted three listening labs with Detroit parents and caregivers to understand what motivated or deterred them from seeking early childhood care and learning options, paying special attention to how they became aware of their childcare choices. In an effort to make it easier for our stakeholders to attend, these sessions were hosted at one of our client’s community care locations and we provided a child-friendly setting that included coloring books, crafts, toys, and refreshments. The sessions felt like a mix between a parent information meeting and a playgroup.
The listening lab findings were used to revise stakeholder digital journey maps, refine the tech stack, and identify data and operational infrastructure needs. By utilizing community-based participatory research practices, we were able to approach parent and caregiver participants with transparency and we revised our assumptions based on their input.
In the case study above, the data and development team had ongoing assumptions and knowledge that differed from the actual needs of the community. By finding a way to incorporate their users into the working methodology, the team was able to make a better product that spoke more directly to their needs.
Just as our data and development teams need to collaborate with subject matter experts – in this case, the caregivers directly – optimizing the impact of the communication also needs the expertise of designers to make the information accessible and meaningful. These are, in many ways, the most crucial steps in understanding, as it is the realization of the meaning of data and the relevance that allows for generating action. According to MIT, partnering with design and data visualization can help the 82 percent of organizations that struggle to harness their unstructured data sourced from text, audio, social media, customer reviews, etc. The project’s designer was able to source collected data to create the journey map, which was the basis for the eligibility model, and directed the backend development team’s activity. As such, we fulfilled the creative brief and streamlined the eligibility and pre-application process in a way that was inclusive, easy to navigate, and accessible.
Here are three steps to help break down functional silos:
- Invite involvement from your subject matter experts on the collection methods, iteration, and interpretation of your data. Participants and community members can use this experience beyond the initiative and when consuming other visualizations or reports in the future.
- Explain and educate about what you are trying to do and how you are trying to do it. Invite conversations on what you collected (and didn’t collect), how you analyzed it, and how folks can make decisions based on your findings or your visualization. This stance is essential to building trust.
- Be flexible in your mindset so that you can incorporate feedback and iterate quickly.
- Infuse patience and fun whenever possible – it helps us remember that we’re all trying to be respectful, mindful, authentically good humans.
Collaboration across your extended team, which can include the community you’re serving, helps to establish and reinforce project equity. Even the most skilled data practitioners can advance their own knowledge of data seeing it in context and by taking a more initiative-based approach to their work. In part one of this series, we asked: “If data is a currency, how do you spend it wisely?” In this example, the answer is by letting it compound.
What do you think?
- Although this is a community-based example, how could you apply these same ideas in your work?
- What are other mechanisms for developing and reinforcing in-context data literacy in your practice?
- What are some of the barriers to this type of approach in your organization and in your daily work?
Share your thoughts with us at email@example.com.