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Finding Common Purpose

In early March, Fast Company published an article that describes how three U.S. cities have essentially ended all chronic homelessness, and another nine have ended veteran homelessness. It’s all part of a national program called Built for Zero that uses a data-based approach to help officials figure out exactly who needs what services. Now it’s accelerating its work in 50 more cities.

The details really struck me—especially the parts about how data are being used to realize population-level change. Also, by pursuing ending all chronic homeless, you also are automatically pursuing eliminating disparities.  

“Ending” is defined as “getting to a place where it’s rare, brief, and it gets solved correctly and quickly when it does happen.” The program employs “real-time, person-specific” data to help agencies track people who are experiencing homelessness and empowers communities to take active measures on a daily basis not only to get people sheltered, but to get them housed for the long term.

Bergen County, New Jersey, for instance uses the approach—the city calls it a “housing first” approach—to move people “into permanent housing as a first step before also getting help with finding a job, mental healthcare, or other issues.”

To extend the metaphor I’ve been using in some previous posts, drawing on the old adage about giving a person a fish versus teaching a person to fish, the program is a powerful example of using data to start with giving and then moving on to support teaching—keeping to what end in mind each step of the way. That’s further reflected in Bergen County’s obvious understanding that housing is just one piece of a pathway to lifelong success.

In an earlier blog post, I wrote about how important it is to have real-time data that can be analyzed so nonprofits, philanthropy, and government can see the progress we’re making toward a desired outcome—and make changes as quickly as possible.

The descriptions in the Fast Company article are a striking reminder of how population-level change is made so much more possible when data and data-related tools can be put to use not as an end in themselves, but to support making decisions that create continuous improvements in the quality of services, lead to real, lasting changes in peoples’ lives and, ultimately, genuine population-level change.

Data, in fact, are “key to the process” the article describes, “and a visual dashboard … lets agencies track people experiencing homelessness in real time.”

For instance, in Abilene, Texas, a city with a population just over 120,000, “the city located every homeless veteran, gathered information about each individual situation, and stored this information in a ‘by-name list’ that was continually updated.”

Access to real-time data has made continuous improvement possible. “‘It basically just forced us to continuously look to change improvements to our system, and how to use real-time data to improve our performance,’ says John Meier, the program manager for supportive services for veteran families for the West Central Texas Regional Foundation.  Every agency in the city began working together and meeting to discuss how to get each veteran—21 people, as of February 2018—into housing. While watching the data, they could test interventions like working with local landlords and the public housing agency to prioritize people on the list.”

The implications for applying it to address other social issues are significant. The Abilene example demonstrates how having real-time data on individuals allows programs to be more deliberate about which services to offer and what other agencies to partner with to support an individual’s progress, because each can see all the same data and results related to that individual. And as that support is provided, prioritizing, testing, and making changes are all empowered, based specifically on data that show how things are going with that individual.

Imagine having the capability to measure that kind of genuine progress along an individual’s entire pathway to lifelong success from healthy birth, to a quality education, to a living wage, to healthy and secure aging, in real time, and then being able to generalize across a population! Imagine having that system to supersede all the disconnected data tracking systems individual agencies and programs maintain!

Unfortunately—and even though the article recounts several examples of data being put to the right use—the norm is for data collection to be wasted.  There’s an example offered that corresponds precisely to what I’ve seen throughout my work in our sector.

“For decades, homelessness organizations would collect data, and they would send it to HUD,” says Neal Myrick, global head of the Tableau Foundation. “Once a year, HUD would produce a massive report that nobody was really reading. And the information wasn’t really usable to the people who needed it on the ground to make active decisions about what to do day-to-day to better solve the problem.”

In many respects, wasting data is structural in our sector. The problems begin with the major compliance requirements that come with receiving funding, whether from the government or foundations. The obligation to collect and report data eats up huge amounts of time. Sometimes, the data aren’t even the right kind from which to make decisions and improvements in the program. Often, the people who require receipt of the data reports don’t even look at them. Meanwhile, the people running the programs often lack the resources, time, and sometimes the skill set needed to analyze and use the data to take action.

All this needs to change if our sector is going to realize population-level change and reduce disparities. Becoming data driven for continuous improvement is key to meeting objectives. It’s not a silver bullet, but from my vantage point it is certainly a key change we need to make.

The right data empower the people working on the front lines to advocate for those they are serving and achieve what they want most in the work they do: to genuinely improve people’s lives. It is a powerful means to an end.

I’ve been thinking about data almost every day since I launched Root Cause nearly 15 years ago. In an upcoming post, I will describe how Root Cause has worked with programs in the past to strengthen their use of data, along with what’s worked and, more often, what has not. In the meanwhile, I invite readers to share their thoughts about how they are collecting, reporting, and using data, and what’s working and what’s not.

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