Data Walk Directions: Get a “Data Walk” handout, form a trio, review data, and answer questions on the handout Data Sources: Washington Department of Commerce Census US Department of Housing and Urban Development

Not Your High School Math: Using Data To Improve Homelessness Services 2017 Conference on Ending Homelessness May 10, 2017

Welcome Presenters: Sarah Cotton Rajski, Building Changes Kathie Barkow, Aspire Consulting LLC Ian Kinder-Pyle, Department of Commerce Data Support: Talia Scott, Department of Commerce Welcome: Kathie Each introduce selves

Workshop Results At the end of the workshop participants will: Understand how to use data to inform program design Understand the value of prioritizing people from place not meant for habitation (unsheltered) Have a framework for developing strategies to increase the number of unsheltered served in their community Kathie Results Call to action: What we know about making improvements is about knowing where you are headed. The context for today is: All unsheltered people in Washington State are served and get access to housing. WHY this call to action to increase understanding value of prioritizing people from place not meant for habitation

Call to Action All unsheltered people in Washington State are served and get access to housing

Ideas About How To Use Data Data walk = tool to engage Data informs program design and strategies Disaggregation of data reveals disparities “BAD” = best available data Role of data quality Sarah A few other comments about data… - data quality - HUD priority

Checking In How familiar are you with data walks? How useful is the data set? How familiar is this? (show with thumbs) (pivot, few examples possibly) How useful is this data set? L/F: What would be more useful is county specific data, program specific data

Credit Work comes from the Annie E Casey Foundation, Results-Based Leadership Programs http://www.aecf.org/work/leadership- development/leadership-development/ Data tables extracted by Commerce

Adopting Baseline Whole Population History Current WHAT IF NOTHING CHANGES? This is the progression of the trend line if we don’t do anything. It’s what we expect to see with no intervention. Would we be satisfied with the result? Whole Population History Current

Setting Target Whole Population History Current WHERE DO WE WANT TO BE BY WHEN? This question supports leaders to identify targets for the results work. The degree of desired change. The criteria for choosing the target. The time period. Whole Population Where are we? History Current

Adopting Baselines and Setting Targets WHERE ARE WE NOW? Establishes the baseline to guide the work and measurement of progress. Trend line to the current moment Questions to consider What does the available data say about what is happening relative to the result we want to achieve? How extensive are the problems we aim to solve? What populations are affected most? Whole Population Where are we? History Current

Unsheltered Data 17% 2014 22% 2015 24% 2016 Target 35% 24%

Unsheltered Data by Race

Shift to County-Level Discussion Conversation is … How is your county doing? Above or below 35% for 2016? Where you think you are now midway through 2017? (project) Considering you have to increase by 10 percentage points next year … what will you do?

Unsheltered Data, by County Adams 43% Klickitat 33% Asotin 31% Lewis 22% Benton 15% Lincoln 8% Chelan 13% Mason 18% Clallam 17% Okanogan Columbia 16% Pacific 24% Cowlitz Pend Oreille 9% Ferry San Juan Franklin Skagit 27% Garfield Skamania Grant 34% Stevens Grays Harbor Thurston 21% Island Wahkiakum 2% Jefferson Walla Walla King 26% Whatcom 29% Kitsap Whitman Kittitas Yakima 35%

Map your County Trend Line TARGET History Projection Whole Population Current History Current Projection

Story Behind the Curve Story behind the curve and story behind the gap To analyze the FACTORS that affect the trendline To inform the change ideas To inform selection of STRATEGIES (based on evidence & best practice)

The Importance of Factors If we conduct specific actions, then we expect specific changes will happen. Factors inform decision making We decide what to do “more of” and what to do “less of” or “do differently” in our actions. Digging Deep: Ask yourself WHY It’s important to get under “automatic explanation” and get to underlying factor, below the surface.

Getting to the Story Behind the Data Through Factor Analysis Factors decreasing trend (restricting) Whole Population Factors increasing trend (contributing) History Projection Current

What you can identify as factors? What is driving down? (restricting) What is driving up? (contributing)

Using Targets to Develop Strategies Considering you have to increase by 10% next year … what to do? Take a minute and write two strategies and be clear about which factor(s) they address

Targeted Universalism: Universal Goals/Targeted Strategies Mapping and Closing the Gap Whole Population Targeted Sub-Population History Projection Current

Role of Targeted Strategies Is it Better? Same? Worse? Story behind data? Factors decreasing trend (restricting) Factors decreasing trend (restricting) Factors decreasing trend (restricting) Whole Population Target Population Factors increasing trend (contributing) Factors increasing trend (contributing) Factors increasing trend (contributing) History Current Projection Maybe Call out a system example and a race example?? History Current Projection History Current Projection Whole Population Target Population Combined Population

Unsheltered Data by Race

Targeted Strategy Brainstorm Revisit strategies and consider if there is an appropriate targeted strategy

A Few Ideas for Targeted Strategies Hire staff/peers that reflect population served Go to encampments more frequently that are comprised of overly represented populations Ensure language access Ensure cultural competence Assess use of peers in program or outreach Examine neighborhoods where housing is being offered from the lens of its racial composition Build on multigenerational or extended family housing strategies where appropriate

Recap of Process Pick the data point you want to look at Collect multiple years of data in order to create a trend line Consider the direction of the trend line and where you want it to go; set a target Brainstorm the factors that move the trend line in both directions – ideal to do this with partners Brainstorm the factors that move the trend line in both directions for disproportionately affected groups Pick one priority factor Develop strategies to address the factor Repeat to track progress

Call to Action All unsheltered people in Washington State are served and get access to housing

Sarah Cotton Rajski Building Changes | Senior Manager 206. 805 Sarah Cotton Rajski Building Changes | Senior Manager 206.805.6123 o| 206-660-6719 c sarah.rajski@buildingchanges.org Kathie Barkow Aspire Consulting LLC | Principal 510.967.5161 c kathiebarkow@earthlink.net For data, reach out… CHG lead agency And/or HMIS licensees.