Data for Improvement: Large Scale Change
Leadership performs data analysis with a race and LGBTQ+ equity lens to understand priorities and where improvement is needed
Data reviews are created (in dashboards, handouts, or slides) that are clear and accessible to the people who will use them to make decisions
Each piece of data reviewed is essential to helping make a decision, with minimal clutter; data that you regularly review prompts you to ask, So what?
Each of your team’s major decisions is supported by data, ideally dis-aggregated by race/ethnicity, gender diverse and sexual orientation
You keep data on resources (such as emergency shelter, outreach, and housing providers), which shows performance measures like length of time and exit rates to permanent housing; it allows you to identify next areas for improvement
You keep a quantified inventory of resources in your community, including housing subsidies and units, affordable housing units, available market-rate rental units, and support services
Qualitative data eg. YYA experiences within your system are collected regularly and addressed
Change Ideas
Types of data you might review: current progress to your high-level goal of ending homelessness for YYA; race/ethnicity, gender diverse and LGBQ data, qualitative data collected from YYA in your system, current data from improvement projects or PDSAs; or reviewing and revising milestones.
Before a meeting consider the stakeholders that will be in attendance, the scope of the conversation and which data will be most relevant for that setting.
Show the data over time using the ACI tools or other tools to visualize data for the select audience.
Use systems-level data that you’re already reporting to ACI (actively homeless, inflow, housing placements, and length of time, dis-aggregated by race/ethnicity, gender diverse and LGBQ) to figure out the areas on which you should focus your next improvements.
Make process maps to identify bottlenecks and places for improvement. Consider how these may contribute to or are affected by race/LGBTQ+ equity. Through the dis-aggregated systems data, identify the sub-population most affected and consider what changes can be made to remedy any disproportionate outcomes.
Use program/agency-level data (length of time, positive exit rates to permanent housing) to point to on-the-ground processes where improvement will move the needle towards your shared aim.
Inventory resources to forecast gaps that need to be filled for to reach your aim. These might include inventory of available housing, projections of available housing, and quantified needs like outreach staff, bridge housing, or other human or material resources.
When higher level outcomes data is indicating an improvement is needed [i.e. needed increases in housing placements] drill down to data looking at process measures like length of time and/or program level data to clarify where the most opportunity exists to make improvements.
Kinds of data you might review…
*Data to Measure Current Progress i.e. % reduction against your 20% goal.
*Data from Recent PDSAs/Tests Run
*Reviewing and Revising Milestones i.e. Based on where we are we need to house X more people by X date to stay on track
Consider which data is most appropriate to lead with in each setting:
*Leadership team meetings
*Case conferencing or front line staff meetings
*Community or stakeholder meetings
*Mayoral briefings