What is the Service Delivery Data Hub project?
By providing its participating agencies with a one-of-a-kind data pooling platform – the Newcomer Insight Collaborative (NIC) – the SDDH project engages and motivates local agencies to pool their service delivery data for collaborative analysis. The outcomes of analysis are then presented in bi-annual Scarborough Newcomer Needs and Trends (SNNT) reports aimed at identifying patterns and trends to help organizations make informed, evidence-based decisions on service delivery. Beyond reporting, the SDDH project also delivers data analysis and capacity trainings, building a data-skilled community of newcomer agencies.
With Catholic Crosscultural Services (CCS) as the lead agency, the SDDH project is funded by the Immigration, Refugees and Citizenship Canada (IRCC), and is implemented in partnership with the University of Toronto Scarborough Campus and the Toronto East Quadrant Local Immigration Partnership.
Check out our one-pager and our Research Collaboration Toolkit!
What does the Service Delivery Data Hub’s research process look like?
There are four main components to the SDDH project’s research and reporting cycle:
- Engagement: Prospective agencies are engaged by SDDH project staff to gauge their eligibility to participate. Once mutual interest is confirmed, a Data Transfer Agreement is then signed to formalize participation.
- Onboarding: The agency and SDDH project staff work together to configure the NIC platform to read and accept the agency’s unique data report.
- Data Collection: Organizations share their research needs and interests and identify data for collection. Agreed upon data is then uploaded into the NIC platform, encrypted, and stored securely offline.
- Research & Reporting: Project researchers perform detailed statistical analyses on the data in aggregate. Organizations are consulted and updated with analysis findings in an iterative process through meetings, leading to the final SNNT report.
To read more about the SDDH project’s processes, check out our Participation and Newcomer Insight Collaborative (NIC) Guidebook.
What does a Scarborough Newcomer Needs & Trends report contain?
Our bi-annual SNNT reports are confidential and only shared with our participating agencies. Here are some examples of analyses that we would typically conduct and feature in our reports, made entirely of mock data.
MOCK FIGURE 1:
Mock Figure 1 exemplifies how the Data Hub project would track the number of clients who have accessed analyzed services over the years.
MOCK FIGURE 2:
Mock Figure 2 provides an example of the Data Hub’s breakdown of frequent service users (i.e. clients who accessed services six or more times within a year) by gender and by service type.
We conduct a number of different analyses focusing on other variables as well, such as…
- Clients’ country of origin,
- Clients’ postal code (breakdown of client locations in Scarborough and GTA),
- Modality of service access,
- Service access forecasting,
- And more!
With service delivery data as far back as 2016, we look to our participating agencies to help us decide on which analyses and visualizations would be the most meaningful and interesting to them!
We also do capacity-building training!
The Data Hub project is proud of its ongoing facilitation of data-focused capacity-building workshops, open to all interested service providers. Here are some workshops we have hosted in the past:
- The Importance of Data Cleaning
- Crafting Data-Driven Research Questions
- Intro to Data Analysis: Case Studies Program
- Research Principles and Tips for Real People
- Using Microsoft Excel for Data Analysis
- Basic Survey Design & Analysis in the Newcomer Service Sector
- Navigating Newcomer & Immigration Data Resources
- Introduction to Microsoft Power BI (Desktop)
*Please note that we do not record/post our workshop sessions publicly, but share all workshop materials with registered participants only.
To be added to our capacity-building workshop mailing list for future training opportunities, please complete the contact form below.