Human-Centered Data and the Quest for Equity
In her speech at the 2019 Code for America Summit, Results for America’s Simone Brody, the Executive Director of What Works Cities, directly took on the often-cited critique that being data-driven is the opposite of being human-centered. Instead, she says, the best use of data spurs innovation that changes people’s lives. Check out her full remarks below.
Good afternoon and hi, everyone. I’m Simone Brody, Executive Director of What Works Cities. I’m really excited to talk with all of you today about how data can help improve services for the people who need them the most — and I want to push back on the oft-cited criticism that being data-driven is the opposite of being human-centered. In fact, the best use of data contributes to more meaningful engagement and partnership in communities — it uncovers real needs, starts conversations, and spurs innovation that changes people’s lives. More on this later.
Here, among the true believers, we know that digital technology is capable of radically transforming how we design and deliver vital government services.
We know that data and evidence are helping us understand the nature of the problems we face and the real conditions of people’s lives.
We know it — because we see it every day. I certainly do.
Before I joined What Works Cities, I worked in the New York City Department of Education during the Bloomberg Administration.
Mayor Bloomberg took office in 2001, if not at the dawn of the digital age, then very early in the morning. Cities were just beginning to think about how to use the internet at all, let alone how to use data to improve people’s lives.
But Mike Bloomberg had already led a data revolution in the financial sector. And he was determined to change the way New York City approached big problems.
In a city as big and diverse — and, frankly, unequal — as New York, fixing education was one of the biggest problems of all. There aren’t many things that impact a city’s future more than providing access and opportunity to the 1.1 million children who are its future.
Yes, every year, New York City educates 1.1 million young people.
So we launched a massive change operation to fix low-performing schools, empower accountable educators, and engage students and families.
It was a really exciting time. We were working to improve outcomes for kids in the nation’s largest public school system. And we saw real results.
More kids on track in elementary school.
More kids graduating from high school. More kids going to college.
What we did not see enough of was equity.
We were making progress generally but somehow still missing some of the hardest-to-reach and most vulnerable kids. Achievement gaps — between students of different races and different levels of privilege — were not closing quickly enough. But we didn’t know why.
To find out, we turned to data — not for the first time, but in a new way.
For the most part, we had been looking at educational data across the city, by district, and sometimes by race.
Test scores. Graduation rates. College readiness and enrollment.
We saw the broad strokes — but we weren’t getting the full, nuanced picture.
We needed to go deeper. And to do so, we needed to disaggregate the data — to break down our large system and look at individual students and small groups of similar students to understand what was happening.
What we saw in all our work with data didn’t always illuminate answers. Sometimes it just illuminated the right place to start a conversation: with quality, nuanced information that we could use to help chart an informed path to improvement.
That made some people nervous. On the one hand, what if disaggregating data revealed inequities that contradicted our claims of progress? Our answer: Why wouldn’t we want to see which kids we were missing? Progress that leaves kids out is not enough progress.
On the other hand, some people worried that showing poor performance among already disadvantaged groups of children would create the false impression that those kids just couldn’t keep up.
Our answer to that: We will prove them wrong.
So we started digging deeper into the data. And sure enough, we did see real and painful disparities.
We could see pockets of exclusion that led to poor performance.
We could see which kids were falling behind and when. And we could even begin to understand some of the reasons why they were more likely to fall behind.
Let me give you one example.
As we began looking at citywide test scores, broken down by where children lived, we saw that students living in temporary housing — and that’s 1 in 10 students in NYC, 100,000 young people — were half as likely as other students to be on track academically in elementary and middle school.
That’s a sad statistic in itself. But to make it worse, you have to understand that kids in temporary housing switch schools more than kids living in more long-term situations. Sometimes a lot more.
And what we saw when we went deeper was that vital information about what kind of support these students needed to succeed was not being systematically shared with their new schools.
Sometimes this was academic information, and sometimes it was information about what students needed personally to make them feel safe and ready to learn. That could be a meal or an adult they could trust, or to communicate with a child’s caregivers about school work.
Even schools that were tracking information to help students succeed weren’t always passing that information on to other schools. And especially for vulnerable kids, that matters a lot. Because a child who falls behind in a core subject like math or reading may never catch up. And it should come as no surprise that fewer than 1 in 2 of these students were graduating from high school.
That’s how vulnerable kids fall through the cracks. That’s how futures are lost. And that’s how intergenerational cycles of disadvantage are perpetuated.
Imagine how different outcomes could be if such crucial information was part of the enrollment data every school must collect and use to connect the dots so that every child gets the support they need to stay on track.
What we saw in all our work with data didn’t always illuminate answers. Sometimes it just illuminated the right place to start a conversation: with quality, nuanced information that we could use to help chart an informed path to improvement.
Disaggregated data also gave us information with which to approach educators and administrators — many of whom were not aware of the widely disparate outcomes among different segments of their student populations and relied solely on instinct in decision-making.
To be clear, data are never a substitute for the powerful judgment of educators based on experience and observation. But they are vital to informing and enhancing our judgment, especially when it may be clouded unknowingly by the inherent biases of our systems.
By the same token, data also helped us to dispel some false — and very harmful — beliefs. For example, that the challenges schools faced in disadvantaged communities were just too entrenched for schools to tackle.
In fact, better data helped us to see and learn from the schools that were defying that narrative. It helped us prove that of course schools with predominantly disadvantaged or marginalized students not only could excel but were, in fact, already excelling.
It showed us that smaller schools produced dramatically better results than the massive schools that previously dominated low-income communities. That school choice had the potential to reverse the terrifying reality that zip code could determine a child’s outcomes, and also that just offering school choice alone was not enough. That smaller class size wasn’t the magic bullet many believed it would be, but maybe teacher quality was.
And contrary to the common belief that data dehumanizes such deeply human work, it actually does the opposite. It enables us to see into the real lives and situations of individual city residents — so many of whom are overlooked and obscured by focusing on the big picture alone.
Lessons like these informed our work developing and delivering services across the entire school system we served.
We identified risk factors for dropping out of high school or not applying to college, and we used them as flags across the city so that teachers and administrators could immediately work with students at risk before they were disengaged.
The data- and evidence-driven approach wasn’t restricted to the Department of Education. Similar strategies were improving outcomes across the city.
When Mayor Bloomberg left office, we saw so much interest from cities that wanted to adopt a data-driven approach to all their service delivery — education, urban blight, public safety, affordable housing, transportation, even climate change.
And not just the big cities. All cities.
That is the mission of What Works Cities. We empower and enable cities to use evidence and data to deepen their understanding of real needs and gaps in service, to use that understanding to design and deliver services more equitably, and to track their progress so they can direct precious resources more efficiently.
And we are doing it all over the country.
Not just in cities like Boston or Los Angeles, but also in cities like South Bend, Indiana (population 100,000), Bellevue, Washington (population 144,000), and Arlington, Texas (population around 400,000).
Let me give you two examples of the kind of programs cities are working on.
Consider 911 and ambulance response time.
EMS personnel respond to roughly 37 million 911 calls around the country every year. Nationally, patients wait an average of 7 to 8 minutes for an ambulance after a 911 call is placed.
But that’s the average. Recent studies show that ambulance response times in low-income communities can be nearly 4 minutes longer than in high-income communities. When every minute counts, that can be a matter of life or death. In fact, studies cite this gap in response time as a potential driver of income-mortality disparities across the country.
Virtually every city in the country struggles with the challenge of reducing ambulance response times without incurring unsustainable expense. How do you do it without buying more emergency vehicles and equipment? Or without hiring more first responders?
A deeper look at the data is helping some cities change their approach to this problem.
Memphis, Tennessee, is one such city.
As the City started examining how it could improve ambulance response times, it began to see evidence that too many ambulances were being called in non-emergency situations.
As staff dug deeper into the data, they began to understand that many residents simply lacked information about how to solve their pressing health problems. But they did know how to reach 911.
So calls were made and ambulances were dispatched, when what callers really needed was someone to drive them to the doctor’s office, or to hear a nurse tell them how to handle a health situation at home.
As one city official put it, these callers weren’t abusing the system — they just didn’t know where else to turn.
As a result, up to 20% of all ambulance calls could have been solved in another, less resource-intensive way. Armed with this data, the City of Memphis piloted a new program to dispatch a paramedic in a cherry red SUV to conduct an on-site medical exam in non-emergency cases, instead of sending a costly ambulance and full response team to every call.
Tracking data shows that this program is working: Response times are shortening, and people are getting the right level of care no matter where they live, without driving costs up.
This is a great example of how data can help define a problem — and innovation can help design a solution that works for everyone.
Tulsa, Oklahoma, is another great example.
In 2018, the City’s Mayor commissioned an Equality Indicators Report to begin what he called a community-wide conversation and city-wide movement to make services and outcome more equitable, informed by standardized data.
One of the most glaring examples of inequity that the report revealed was an 11-year disparity in the average lifespan of residents in the wealthiest and poorest zip codes. As the City dug deeper into the numbers, they began to wonder whether improving housing options for residents might help close this outrageous gap.
So, equipped with its own disaggregated data and other studies, the city began to re-think the way it allocated federal Community Development Block Grant (CDBG) funding for local affordable housing initiatives.
Many cities struggle with the best use of this limited discretionary funding to really advance stable housing in their communities. Tulsa decided to shift from an “equal” approach to allocation — with some funding going to most neighborhoods, regardless of need — to an “equitable” approach, with funding going to the neighborhood with the greatest need, and the highest level of poverty.
Now the City is working to establish partnerships with community organizations and academic institutions that can help to evaluate the effect of the increased funding on life expectancy outcomes for the residents of vulnerable neighborhoods.
This is how progress reaches everyone.
If you start with a big question and use information to engage your community and really drill down to the core of it — and then test and measure potential answers to that question, hand in hand with your community — data is one of the most powerful, efficient tools we have to solve our nation’s biggest challenges.
The cities we work with — and the partners who support our work — share our conviction that we cannot solve the biggest problems our cities face unless we understand the underlying causes of those problems and the people whose lives they affect in measurable, concrete ways.
And contrary to the common belief that data dehumanizes such deeply human work, it actually does the opposite.
It enables us to see into the real lives and situations of individual city residents — so many of whom are overlooked and obscured by focusing on the big picture alone.
It enables us to make our efforts laser-focused. Right-sized. Truly able to meet the needs of the people we intend to serve.
To be very clear, data should never be used to distance government from residents or to replace the invaluable role of listening to people’s needs and wants. It is, however, an indispensable tool that can be used to start and inform those conversations — and to create a solid foundation to drive innovation and, ultimately, positive change.
It’s true that data can be narrowing — but only if you are only looking at a narrow set of data.
If you start with a big question and use information to engage your community and really drill down to the core of it — and then test and measure potential answers to that question, hand in hand with your community — data is one of the most powerful, efficient tools we have to solve our nation’s biggest challenges.
So, what do I want you to take away from all of this?
First, this is an incredibly exciting time. New tools and ways of using data are changing the way we dissect problems so we can take bold action.
But second, that data alone are not enough: Data need to be coupled with a deep understanding of the real needs of cities and their residents — and used to drive innovative policies and programs to meet those needs.
And third, we need to be honest about what the data show us. Sometimes, they reveal failure — and we have to use what we learn from those shortcomings together and change course when we have to, until we get it right.
Finally, what can you do to help?
Cities: so many of you are doing this work already — working hard to use data and tech to address inequity in your community. If you’re already a part of What Works Cities, great! If not, let’s talk. I know there is great work we can do together, and we have a range of free expert support to offer as you improve how you’re using data to drive results for your residents.
Tech community: Government is the scale play here. You know this already, but it’s worth repeating: The best opportunity for your tools and strategies to reach those who need it most is by truly partnering with local governments in the communities where you work. They’re already on the front lines of solving pressing social challenges, and you have a role to play in strengthening their efforts.
Leverage your expertise to help cities do a better job reaching their most vulnerable residents and their most underserved communities. Share your data, and then go a step further: Help cities understand it and use it to meaningfully address the complex issues their residents face.
Where you lead, others will follow. And the future of our cities — our country — is riding on it.
Thank you.
Learn more here about What Works Cities and how your city can get access to free support from our expert partners to build a data-driven local government.
