[CITY] will put user-centred design practices at the heart of its approach to data, ensuring that teams are able to design data tools and platforms that meet users’ needs.
User needs
Managing data in line with data users’ needs, whether they are residents, organisational partners, or [CITY] officials, will ensure that public data generates a positive policy and social outcomes. Understanding and applying user-centred design practices across the [CITY]’s data work will ensure that product teams are able to design data tools and platforms that meet users’ needs. Additionally, understanding how users make use of data, for example by collecting metrics on data usage or conducting user research to understand data stories, can help tell the story of how public data generates positive social impact for communities.
Users are commonly broken down into the following categories:
- Primary User: The general description of a primary user is one who will be directly using the tool or service that we provide. They will often co-create or ‘product own’ the tool and are often also responsible for its content and maintenance. This user is theoretically our specialist in their field or job. We ideally want detailed knowledge of how they carry out their tasks and what barriers they face in doing so. We aim to deliver a product that is a contextual response to the challenges faced by our user with the end beneficiary in mind as the proof of a successful delivery.
- Secondary User: A secondary user in our context is someone who receives outputs from the primary user. IE: we may be working with a data steward from a unit as our primary user and the reports and outputs they create go to decision makers who would be our secondary user. Where possible we want to work with secondary users to better understand what they require of the primary user. This increases the chance of expectations being met in terms of desired outputs. Outputs created by the primary user should be specific to the secondary users ideal situation meeting them at both their technical and focus levels.
- Beneficiaries: Beneficiaries are people like residents and community members. In a successful system they receive the benefits of the work we do. Ideally the engagements should always be as non extractive as possible and should be in a relationship where we are clear about sharing results or progress of the work we carry out. We should endeavour to share results and progress in a non cryptic way that doesn’t require technology or access to paid mobile data etc to access that knowledge.
In summary, user-centred data management can both improve efficiency by ensuring that [CITY] departmental units use data-driven practices to address challenges that matter to residents, organisational partners, and municipal officials; and can also improve outcomes for communities by ensuring that public data is shared and applied in ways that meet community needs.
User research as a practice
User-centred design involves implementing design practices before the launch of a new data-driven program, tool, or platform based on users’ needs. Design processes focused on users’ needs must start with an exploration of potential challenges that data users face that can be solved with data. These explorations can be categorised as user research, and should be used to inform ideation around the implementation of data-driven solutions. Specific insights that emerge from user research can be attached to specific proposed solutions. Then user researchers can work with data owners to understand which proposed solutions are both feasible and impactful based on the results of the research.
For data users who are municipal officials, user research may involve conducting interviews with government data users as well as doing background research to understand administrative and policy environments. Insights emerging from user interviews can then be compared with or cross-referenced with background research to understand how the challenges that data users face are connected to institutional barriers to data use. For example, background research may reveal certain policies that restrict the sharing of important data across units or disagreements about data-sharing priorities. These insights may then be validated in interviews with municipal officials who are data users and should be taken into account when developing potential solutions and understanding their true feasibility.
For data users who work for organisational partners outside of government or who are municipal residents, user research may involve broader surveys of community needs beyond just what users need from data. All residents have a right to access and use public information, and may benefit from better information about specific policy issues so that they can make more informed decisions in their day-to-day lives. With this in mind, interviews with potential data users can also be supplemented by exploration of social challenges that are widely and deeply felt. Insights emerging from user interviews can be compared with these broader social analyses to identify which potential data-driven solutions will resonate with community needs and therefore meaningfully address hot button issues. This can ensure that public data is perceived by the general public as helpful and useful in addressing current community challenges.
Building feedback loops
Responsiveness is an important component of building trust inside and outside of government. When governments are responsive to communities, even if the news is not good news, residents can begin to trust that they are part of the process of governing. Publishing high-quality data, or communicating the gaps in available data proactively, can also be part of trust-building measures around government data programs. These practices are part of building responsiveness into government data practices.
To build a trustworthy design process, [CITY] must balance collecting feedback from users with communicating results back to these users. After completing user research and identifying data-driven solutions, the Product Team should share their findings with users. This communication should include a documented plan with specific goals and milestones that clearly demonstrates how user feedback shaped the proposed data solutions.
Product Teams should also make sure to effectively communicate the plan, for example by coordinating with agency communications staff to help disseminate the plan to relevant stakeholders or presenting the plan to city leaders in open forums with opportunities for public feedback. Implementing responsiveness as a principle in these ways is also known as building feedback loops, to ensure that feedback continues to be collected, incorporated, and communicated again with residents and potential data users. Data ecosystems with a high level of government responsiveness and strong feedback loops tend to have higher uptake of public data tools and offerings.
Transparency and accountability practices
Part of building strong feedback loops is ensuring that government agencies see the value of participating in municipal and regional open data programs. Publishing open data by default can ensure that residents always have access to relevant data without having to face administrative burdens via public information requests. Open data best practices are reflected in the overall data standards set forth within this data strategy, and can also incorporate user-centred design practices to help data owners understand which datasets community members most want to see. User research for open data offerings can use past public information requests to identify high-demand datasets and pinpoint data requests that consume significant time and resources. This helps data stewards streamline and make clear policies on accessing this data.
Open data is an important part of establishing government transparency. In addition to publishing data, it is also important for governments to take accountability for policies or programs that don’t meet established performance metrics for success. Accountability requires open feedback loops where residents can participate in determining which policies are or aren’t meeting the mark. Accountability must also involve taking community feedback and integrating it into future decision-making. Establishing feedback loops via data programs and platforms can give community members a way to provide feedback in the form of data so that policymakers can more easily integrate community input into future decision-making. For example, a new service showing electricity outages on a neighbourhood level can also include a comment form or an interactive voting function where community members can share whether data about their neighbourhood is accurately reflecting their lived reality. If data is inaccurate, community members can be a part of correcting the information and helping policy-makers make more informed decisions about energy policy moving forward. Functions like the ones in this example help to build accountability by creating feedback loops in data platforms, thereby building trust with community members and potential data users.
Sharing data stories
Storytelling is an important component of engaging with potential data users. Because data users may come from many levels of experience or exposure to data practices, making information accessible through storytelling can make a measurable difference in engagement with municipal data tools or public data offerings. For internal municipal data tools like performance dashboards, this can take the form of qualitative data metrics alongside quantitative ones to help leaders and decision-makers more robustly understand the stories beyond the quantifiable data. For open data programs, this can take the form of publishing “Data Stories of the Month” or other creative explorations of available data to help community members engage with open data.
Data Stories are a way of expressing a complex idea using a relatable narrative.
Considerations about how to communicate data stories often come at the end of a data project. However, keeping an eye on the balance between qualitative and quantitative data from the beginning of a data project can ensure that data owners and team members are designing data tools that will be accessible and usable by a wide audience of potential data users. Data owners may also want to collaborate directly with communications staff to gain professional input into the best ways to plan for the publication of a new data tool or platform.
Data teams that plan to incorporate storytelling into their data projects may also benefit from data management practices that address privacy concerns to ensure that data that is sensitive is protected, and that non-sensitive data is shared as widely as needed. By taking storytelling into account in the early stages of data management life cycles, data owners can also be proactive in planning to release data that tells a clear story about municipal policy goals and activities. Data owners focused on citywide performance management may especially benefit from these practices, including proactive coordination with communications staff, to ensure that data about city performance metrics is communicated clearly and with sufficient context.
Certain visualisation techniques may also help tell data stories. For example, combining quantitative information with visual storytelling and geographic maps can help people contextualise information that they are seeing and therefore gain a deeper understanding to make more informed decisions.
Action Items
- Publish a ‘data standard’ that puts user-centred approaches at the heart of the [CITY]’s data work
- Publish and maintain a ‘user-centred data management manual’ that includes guidance and best practices for teams across [CITY]
- Expand the publishing of data stories on the [CITY] website
- Create a programme of user-centred data management training for key staff