How to Build a Collaborative Approach to Data Sharing
Download the full report here: How to Build a Collaborative Approach to Data Sharing (PDF)
In This Report
Authors
Emilie Bassi, Research & Policy Associate
Kayla Blackadar, Research & Policy Associate
Rachel Carr, Manager, Strategic Initiatives
Kiran Gurm, Senior Research & Policy Associate
Introduction
Background
Youth homelessness is a complex and urgent issue in Canada. It affects thousands of young people every day. Many lack the resources and support they need to successfully move into adulthood. In 2022, the Government of Canada estimated that 46 percent of homeless adults had their first experience of homelessness before the age of 25.1
For many young people, homelessness is a cycle. They are constantly shuffling between systems and supports. Breaking this process requires coordinated, youth-centred solutions that address immediate housing needs. Any changes must also address the long-term social and economic factors that contribute to homelessness.
The social sector often struggles to collaborate and share information in ways that directly support youth. This is because many social service agencies, shelters, and government programs operate in silos. These divisions make it hard to follow a young person’s journey through the system. It also makes it difficult to ensure they receive consistent, coordinated care.
Organizations that do engage in data sharing often take different approaches. It all depends on their capacity, policies, and comfort with information-sharing practices. These various methods can hamper connecting and coordinating data to improve prevention outcomes.
The consequences of not sharing and connecting information can be significant. Youth may need to repeat their stories to many different providers. This can be retraumatizing and exhausting for them. Lack of data sharing may also result in service gaps, repeated assessments, and uneven care for youth. These barriers make it harder for them to access help and break the cycle of homelessness.
A connected, collaborative system can reduce the risk of youth homelessness. Data collaboration enables responsible information sharing that can lead to informed referrals, streamlined access to resources, and reduced duplication. It can also help identify at-risk youth earlier, improving support and prevention efforts.
This report is for anyone in the social sector interested in improving collaboration and access to services through better data and information sharing. Whether you are beginning to explore this topic or are already working within a collaborative framework, this report can help you spark meaningful conversations. We hope it encourages innovation, helps organizations to work together more effectively, and improves outcomes for young people.
Project Approach
We wrote this report to guide the development of a pilot project we are working on. It identifies important considerations, approaches, and practices required for successful data collaborations.
Through the project, we will build and pilot a simple data collaboration initiative to support coordinated service access for youth who are homeless or at risk of homelessness in Calgary. We are basing this project on emerging practices and real-world community experience. At the same time, we will be engaging with community partners and youth. We are hoping that using this community-based solution will help inform other initiatives across Canada.
Methods
We started by exploring effective collaborations for information sharing that improve service delivery. We conducted an international environmental scan to identify the best examples of collaborative information sharing initiatives addressing homelessness. Our search criteria included organizations meeting the following conditions:
- Focus on homelessness or wraparound services.
- Discussion of collaboration using data or information sharing in their documents or websites.
- Active engagement with data or information sharing within the past five years.
We prioritized our local context of Alberta and Canada. However, we also examined examples from Australia, New Zealand, the United Kingdom, and the United States. Using specific search terms, we identified the best ways to share data across sectors that improved coordination and client support. While we mainly focused on data collaboration related to youth homelessness, we also looked at examples of other supports and social services for vulnerable groups. For more details, see Appendix A.
Our search generated 37 examples of organizations that matched our criteria. We then invited 13 of them to participate in an interview. We conducted nine semi-structured 60-minute interviews with 13 representatives from data-sharing collaboratives. The interviews were transcribed verbatim and coded in NVivo 12 software. Through our analysis, we identified key areas to consider when approaching data collaboration.
Scope
While our project’s focus is on youth homelessness prevention, the interviews captured approaches to data sharing and collaboration in other programs and services. We learned that there are principles and considerations around data sharing that apply across the social serving sector. These considerations can be used in a variety of programs and settings. Due to the limited sample size and the fact that some organizations were still working to put in place their data-sharing processes, we framed our findings as key considerations in data sharing.
Five Key Considerations for Data Collaboration
This section highlights five key considerations for developing a data collaboration to support enhanced service delivery and coordination. By reflecting on these key considerations and using them to guide discussions, you can establish a strong foundation for governance, one that is essential for fostering a sustainable and responsible data collaboration.
A governance structure is the framework of policies, roles, responsibilities, and processes that guide decision making, accountability, and operations within a collaborative effort. While governance is often viewed as the starting point for building a data collaboration, those involved in creating an effective governance model require a deeper understanding of their needs, priorities, and capacities. By first exploring these considerations, you can develop a governance structure that is not only aligned with your organization’s goals but is also responsive to the evolving nature of collaborative data work.
For each consideration, we explain its importance, the actions that support effective collaboration, and provide reflective questions. These considerations highlight emerging ideas and approaches rather than universally tested solutions.
Meaningful data collaboration is a long-term process, and organizations enter collaborations from different starting points. The way organizations share data varies depending on the type of services provided, the nature of the partnership, the organizational capacity, and the broader systemic factors. There’s no single pathway, definitive answer, or standardized approach to this work. The five key considerations below are a starting point for building an approach to data collaboration that aligns with your organization’s unique needs.
The key considerations are:
- Establishing Purpose and Intention
- Identifying and Mitigating Risk
- Promoting Client-Centred Design
- Understanding and Navigating Capacity
- Prioritizing Sustainability
Establishing Purpose and Intention
Establishing and defining the purpose and intention for data sharing is often a good starting point to explore in a data collaboration. Purpose refers to the overall reason for data sharing. It captures the “why.” Intention is the specific actions or plans to achieve the goal. It is more focused on the “how.”
Our interview participants described how effective collaboration begins with a clear understanding of why partners are working together and what they aim to achieve. Purpose and intention are foundational aspects to revisit when planning, addressing challenges, and making decisions. Participants emphasized that defining a shared purpose and common objectives is essential for guiding collaboration efforts and ensuring data is shared intentionally and appropriately. Below we highlight actions that participants identified as important for establishing purpose and intention.
Creating a clear purpose statement and guiding principles. Purpose and intent serve distinct roles in supporting data collaboration. Organizations can formalize these through a clear purpose statement and a set of guiding principles. The purpose statement helps define the overarching goal of collaboration. The guiding principles provide clarity and consistency when making decisions and taking actions, which shape how information is used and shared. These can be developed and used between partners and linked to organizational or program-specific values and mission statements. For example, one interview participant shared:
It’s such a great, like a North Star for this, if you start with your purpose and then you understand your roles in it…
– Participant
Agreeing on the minimum information that organizations will share. Determining the essential information needed to support a client is a critical decision when collaborating. The level of detail shared should be aligned with and informed by the purpose of the collaboration. For example, providing holistic support may require more comprehensive information, whereas a basic needs assessment for referrals might only need minimal data sharing.
The goal of minimum information sharing is to ensure that organizations respect their clients’ privacy as much as possible while gaining enough understanding to effectively support them. As highlighted by one participant:
…information sharing starts with purpose, right? If the three of us are here to do great things for Sally and Frank, what’s the purpose behind what we’re doing? We’re here to make sure that they’re fed and clothed, right?
So, when you think about this in terms of, are you sharing information, if we’re talking about Frank’s broken finger, and it doesn’t relate to being fed or clothed, the purpose of why we’re here together, then we shouldn’t be talking about it, right?
– Participant
Clarifying roles, expectations, and levels of commitment. Establishing a shared purpose helps define roles and expectations, ensuring that each partner’s level of commitment is clear and aligned with the collaboration’s goals. When intention is explicit, partners can make informed decisions about their participation and contributions. One participant noted:
…being very clear about what it is that they’re trying to achieve, and that they are committed to actually doing the work necessary to achieve those goals or the purpose or the objectives.
– Participant
Maintaining ongoing communication about the shared purpose. Linking communications back to the purpose can help support informed decision making, address challenges, and adapt data-sharing processes accordingly. One participant emphasized the importance of asking the following questions:
…where do we stand? Why do we stand there? What do we need to do to take on more risk, right? What are the things that would get us there.
– Participant
Reflective Question
- What are we trying to achieve and address with this data collaboration?
- How does each partner’s role align with the collective goal?
- What is the minimum necessary information we need to share to meet our purpose?
- What is the level of commitment among partners?
- What are each partner’s needs?
- What are the collective needs among partners?
Identifying and Mitigating Risk
Starting a data collaboration often raises concerns about potential risks, leading to hesitancy among staff and organizations. Common risks include privacy and security breaches, data misuse or misinterpretation, legal and compliance challenges, and loss of control over shared data. Other staff concerns include fear of negative consequences if something goes wrong, uncertainty about data-sharing processes, and resistance to change.
Organizations can help manage risk by reframing it. They can shift the focus from risk avoidance to thoughtful and proactive risk management. This shift includes recognizing the risks that not sharing data may affect their ability to support clients holistically.
When organizations reframe risk, they can create an environment where collaboration feels safer and more achievable. This encourages interest holders to share data, knowing that measures are in place to protect sensitive information and address potential challenges. By adopting intentional and proactive approaches, organizations can build systems that mitigate risks, foster trust, and empower effective information sharing. Below, we highlight actions that interview participants identified as important to reframing risk and encouraging responsible data sharing.
Identifying and understanding risks early in the process. When collaborating, it’s helpful to openly assess risks upfront and to create a safe environment for sharing concerns. This ensures that everyone is aware of the risks specific to your data and its potential uses. It also helps address any hesitations by focusing on ways to reduce or manage risks together. This can also mean talking about the risks of not sharing, as one participant noted:
If we share something and we break privacy, we could get sued, right? What are the risks if we don’t share information? And sometimes those risks are death, right?
– Participant
Embedding privacy protections and a clear purpose in the collaboration’s design. When addressing risks related to data sharing, participants emphasized the importance of embedding privacy measures early in the design process to proactively mitigate them. Starting with a clear discussion about purpose, collaborators can focus on how purpose determines what information they should share, which can then help them embed privacy measures early in the process. As one participant explained:
…be intentional about all these pieces right up front. You can mitigate cyber security risk. You can mitigate a lot of these. Then it allows you to take more risk because you have the knowledge.
– Participant
Leveraging technology to support responsible data-sharing. The right technology can enhance data collaboration by streamlining processes and enabling secure information exchange across organizations. Well-designed data-sharing systems can reduce administrative burdens, facilitate real-time collaboration, and support decision making. However, technology also introduces risks, making it essential to prioritize security, privacy, and proper training. By thoughtfully implementing and managing technology, organizations can maximize its benefits while mitigating potential challenges, ensuring that data collaboration is both effective and responsible.
Reflective Question
- What are the potential risks of this data collaboration, and how can we proactively address them?
- Have we considered both the risks of sharing data and the risks of not sharing data?
- Is there a shared understanding of risk among all partners?
- How can we embed privacy protections into the collaboration?
Promoting Client-Centred Design
Using a client-centred design in data collaboration prioritizes the needs, choices, and well-being of individuals in all decisions related to information sharing. The design should consider how data is collected, shared, and informed, and who will be impacted. A client-centred design acknowledges the diversity of clients and follows culturally responsive and trauma-informed practices. The design process should include the perspectives and values of those with lived experience.
In terms of collecting and sharing information, participants also emphasized the importance of flexibility and nuance. They recognize that each client’s circumstances are unique, and that information sharing should not follow a one-size-fits-all model. This means carefully considering what data is shared, with whom, and under what circumstances. Client-centred design can build trust with clients and help create processes that are ethical, transparent, and aligned with lived experiences, thereby minimizing harm. Below, we highlight key actions that participants identified as important to client-centred design
Sharing data on a case-by-case basis. Participants described the importance of approaching privacy and information sharing on a flexible, case-by-case basis. Determining what is appropriate to share depends on the specific client, situation, and purpose. As one participant noted:
…it still is very difficult to always predict what information you are going to require for this person that you’re supporting under whatever the circumstances they’re facing. You can outline it in broad stroke terms, but not necessarily down to the data element.
– Participant
Integrating informed consent practices. Participants emphasized the importance of thoughtful and transparent informed consent practices when seeking permission to share a client’s information. A tailored approach that walks clients through the consent process helps ensure they understand what they are agreeing to, feel empowered to make informed decisions, and can ask questions.
Rather than relying on a rigid script, consent discussions should focus on key points, clearly explaining the purpose of sharing information, addressing potential risks, and providing clients with the option to withhold information from certain partners. As one participant shared:
When they are asking for consent… it’s a good idea to circle back and ensure that, once they are stabilized, you actually sit down with them… just to make sure that they understand what we talked about.
– Participant
Embedding trauma-informed practices into data collection and sharing. A trauma-informed lens is necessary when collecting and sharing information. Organizations can minimize harm or negative experiences for clients by carefully considering how questions are asked and what data is truly needed to best support the individual. One participant described how housing strategists are modifying the data collection process to be more culturally sensitive. They also highlighted the importance of training by noting:
…the way questions are laid out sometimes some of them are problematic… we try to coach them in training to maybe ask this question in a different way that is more trauma informed.
– Participant
Applying equity, diversity, and inclusion (EDI) principles. Embedding EDI principles in information sharing and data collaboration is essential for building trust and fostering ethical engagement. Participants emphasized the importance of approaching data collaboration thoughtfully, considering the systemic barriers and power dynamics that affect diverse communities. This includes providing opportunities for lived experiences, perspectives, and values to inform the design of data collaboration.
Actively engaging these communities to understand their unique cultural contexts and ensuring data practices align with their needs ensures a meaningful contribution to the design process. Highlighting the importance of EDI, one participant shared:
…it’s multiple voices, right? So, what does the Vietnamese community versus the Nepalese community think of when we’re talking about consent, and this thing and their journey?
We want to be like this idea of health equity is really important to us, right? We do not want to see any of those communities fall through the cracks.
– Participant
A Note About OCAP
An important framework that supports EDI in data collaboration is Ownership, Control, Access, and Possession (OCAP). OCAP provides ethical guidelines for working with Indigenous data, ensuring that communities have authority over how their information is collected, analyzed, and shared. It promotes ethical considerations at each step of the data collaboration process, helping to uphold cultural integrity and ensure that data is interpreted in the appropriate context.
Reflective Question
- Do we understand the diversity of our clients?
- How can we incorporate the lived experiences of our clients in the data collaboration design?
- How will we build trust and support informed decision making with clients?
- What is our approach to informed consent in information sharing?
- Does our process of collecting and sharing information align with trauma-informed practices?
Understanding and Navigating Capacity
Engaging in data collaboration with other organizations often happens “off the side of the desk” for staff with already full workloads. This creates challenges related to administrative burden, staff burnout, and competing priorities, all of which participants cited as barriers to effective collaboration. Before pursuing data collaboration, it is helpful to assess current organizational and staff capacities. Navigating capacity constraints require creative problem-solving, resource sharing, and strategic partnerships.
These approaches enable organizations to maximize limited resources while still driving system-level changes, even with funding and workload challenges. Using current staff capacity, data collaborations that start with simple and small changes can be effective. They can evolve and grow as capacity shifts. Below, we highlight actions that participants identified as important to supporting organizational and staff capacity for data collaboration.
Dedicating job roles to keep collaboration moving. All participants described data collaboration challenges related to job capacity and funding constraints. As one noted:
Collaborations start to fall from the wayside when people have a collaboration that’s on the side of their desk… if it’s not part of their day-to-day role, it can easily get pushed and bumped.
– Participant
Participants who had a funded position or team dedicated to collaboration felt it was a major contributor to the collaboration’s success. Dedicated roles help keep efforts aligned and sustained over time by supporting logistics and planning, maintaining relationships within the collaboration, and ensuring the sustainability of collaborative efforts.
Identifying a lead organization to strengthen accountability and reduce burden. Participants highlighted the benefits of identifying a lead organization or a shared governance structure to take on key responsibilities and risks. This approach promotes stability, ensures accountability, and allows other partners to focus on their strengths. As one participant explained:
Having a lead there that takes on most of the tasks, that it’s not spread evenly… it is a collaboration, but there’s a lead that’s making sure things are in place.
– Participant
A lead organization can help coordinate efforts and improve communication, making the collaboration more organized and efficient. Centralizing key responsibilities helps reduce the workload for individual organizations.
Building off existing work and learning from other collaborations. Drawing inspiration from existing models rather than starting from scratch allows organizations to adapt proven strategies to their own needs. Participants highlighted the value of learning from established collaborations and leveraging pre-existing relationships to foster trust and enhance capacity, ultimately strengthening their own collaboration.
Aligning each organization’s needs with the collaboration’s shared goals. Participants described creatively navigating resource constraints and competing priorities by aligning their individual organizational needs with broader collective goals. Success depends on maintaining a shared purpose and commitment.
To navigate current capacity constraints, organizations can also offer in-kind services, such as space, expertise, or staff time, to support shared initiatives. This alignment enables partners to contribute meaningfully while advancing collective outcomes. As one participant reflected:
How does everybody balance their individual agency needs and progress, versus how that fits within the collaborative, and sometimes those strategies align and sometimes they don’t.
– Participant
Reflective Question
- What is the current workload of staff members who would be involved in the collaboration?
- Are there dedicated roles or positions to support data collaboration?
- How can we creatively share resources or leverage partnerships to enhance capacity?
- What successful data collaborations or models can we learn from or build upon?
Prioritizing Sustainability
Sustainability is a key aspect to consider and embed into a data collaboration. Building a sustainable data collaboration requires fostering strong, trusting relationships with partners, continuously developing staff competencies and capacity, and implementing evaluation strategies to measure and demonstrate impact. These elements strengthen organizational foundations for data collaboration, contributing to long-term success. Below, we highlight actions that participants identified as important to building a foundation for sustainability in data collaboration.
Creating a foundation of trust and relationship building. Participants highlighted that trust and relationship building form the foundation of successful data collaborations, providing collaborators with a sense of security in their roles and commitments. One participant explained:
I think what’s been helpful is just establishing solid relationships from the very beginning of the partnership. So that means, you know, constantly checking in and just ensuring they can see the benefit of becoming a partner right from the beginning, so that we’re not constantly kind of trying to sell this to them.
– Participant
Providing ongoing staff training and support. Participants emphasized the need for staff training in areas such as consent practices, data collection, and technology usage. Training can equip staff to effectively guide clients through consent forms, answer questions transparently, and promote ethical information sharing. A tailored approach helps staff clearly explain the purpose of data sharing, address risks openly, and ensure clients understand their right to opt out. As one participant shared:
…what we do is really train that person, who that young person might see coming through the front door… to really be able to speak to young people about why this information is collected, who’s going to have access to it, what does it tell us and why do we do this.
– Participant
Measuring success through ongoing evaluation and implementing improvements. Structured evaluation frameworks and data collection processes are crucial tools for demonstrating success and gaining buy-in from potential partners and funders. Participants emphasized the importance of using data to validate their impact and strengthen collaborations. For example, a key metric they highlighted was appointment data. It provided insights into service accessibility and support availability. One participant explained:
…the importance of data in all of this, like just showing how much we contribute to the mental health sector, and using that data to validate current partners, but also potential partners, like this is what we’ve done so far, this is a need in the community. I think it’s been significant to share the progression of this, and the success of this collaboration and initiative.
– Participant
Reflective Question
- What strategies can be used to maintain strong relationships over time, especially through staff turnover?
- What skills and knowledge do staff need to participate effectively in data collaboration?
- How can we demonstrate the impact of the data collaboration?
- What evaluation strategies can be used to measure progress?
- How can we share success stories in ways that resonate with different audiences, such as, funders, community members, and staff?
Conclusion
The five key considerations we shared in this report are not a one-size-fits-all solution. They are a starting point for deeper conversations about how to build a collaborative approach that works for your organization and the clients you serve. Exploring these areas as a collaboration or even as a single organization takes time and intention. As you move forward, consider how these practices can inform your governance structure, strengthen partnerships, and create a foundation for more effective and ethical data sharing.
References
- Government of Canada. (2022). Everyone Counts 2020-2022: Preliminary Highlights Report.
Appendix
The key considerations are based on our interviews with participants identified in an international environmental scan. We first conducted an international environmental scan of existing examples of information sharing collaboration to address homelessness. While we prioritized the local context of Alberta and Canada, we also included Australia, New Zealand, the United Kingdom, and the United States of America. Search terms were used in Google to retrieve examples of leading applications of information sharing collaborations who seek to improve service delivery for youth experiencing homelessness.
Search Terms
- Data sharing or agency collaboration or data collaborative (AND)
- Homelessness(youth) or mental health or child welfare (AND)
- Alberta, Saskatchewan, British Columbia, Australia, New Zealand, UK, or United States (limited)
Combinations of above
- Specific: Action (AND) Population (AND) Place (AND) Support (AND) Canada
- Broad: Population (AND) Support (AND) Place
Inclusion Criteria
- Recent timelines (2019/2020 or later)
- Homelessness services (with preference to youth services)
- The document/website discusses collaboration using data or information sharing
- The document/website is available in English
- The document/website is from Canada, Commonwealth countries (i.e., UK, Australia, or New Zealand), or the United States
The search generated 37 examples that matched the criteria in the search strategy. Of the 37 examples, 13 were identified and invited to participate in an interview. Nine interviews were conducted with 13 representatives from data sharing collaboratives. The remaining four declined or did not respond to the request. Interviews were approximately 60 minutes. The interviews were transcribed verbatim and coded in Nvivo 12 software. Through our analysis, we identified themes that help inform promising practices for information sharing collaboration.
Acknowledgments
We would like to thank the organizations and individuals that offered their time to participate in interviews that informed this report. We appreciate their commitment to openly sharing knowledge, experiences, and perspectives.
Project Funding
This project was funded in part by the Government of Canada.
