Our dashboards provide valuable insights into the performance of agencies and key metrics related to homelessness services across Connecticut. These dashboards are designed to support decision-making by offering real-time data on program outcomes, service utilization, and client demographics. Below is an overview of the dashboards currently available:

• CT HMIS Support Ticket Dashboard: Tracks support tickets submitted by users, helping identify common issues and areas for system improvement.
• CT HMIS User Activity Dashboard: Monitors user engagement and activity within the HMIS, allowing administrators to oversee system usage and compliance.
• CT HMIS Programs Dashboard: Displays performance data for various programs, providing insights into program effectiveness and areas needing attention.
• CT HMIS Known Issues Dashboard: Lists current known issues within the system, keeping users informed about ongoing challenges and resolutions.
• Enhancement Request Dashboard: Summarizes requests for system enhancements, enabling stakeholders to see what improvements are being considered or implemented.
• CT HMIS LMS Dashboard: Tracks learning management system usage, including course completion rates and user progress.
• CT HMIS ILC Dashboard: (To be retired) Provides data on instructor-led course participation and outcomes.

 

Data Source and Interpretation:

The data in these dashboards is primarily sourced from the information entered by service providers and is updated regularly. It’s important to note that this data is self-reported and may be subject to errors or changes over time. While every effort is made to ensure accuracy, comprehensive data quality checks are not universally applied. Users should interpret the data with this in mind and consider it a reflection of current reports rather than absolute figures.

Qualifiers for Dashboards:

To maintain transparency, each dashboard includes qualifiers that outline the limitations of the data. These qualifiers are essential for understanding the context of the information and should be reviewed alongside the data to avoid misinterpretations.