How Los Angeles Is Building A Data-Driven Culture

LA’s chief data officer on what lessons the city of nearly 4 million has learned from business—and what business might learn from the city.
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Before starting any new project for the city of Los Angeles, Eva Pereira, the city’s chief data officer, begins by helping her colleagues think of data first.

“Data should always come first in program or project design, and that’s really what I’m trying to promote here in the city,” she said. “How are we going to capture and collect it and what kind of analysis do we want to do with it? That’s my raison d’etre and my mission here, hoping to foster a more data-driven culture.”

Pereira spoke with StrategicCIO360 about developing a data-driven culture, the applications of AI to municipal projects and the value of open-source data.

Given the plethora of municipal data, what do you regard as your main role as chief data officer for the city of Los Angeles?

My main role is to work on data projects within the mayor’s office and also departments in City Hall. We’ll work on data projects that generally fall into three categories. Resource allocation projects: just identifying areas of greatest need throughout the city in terms of helping us roll out programs and services. We’ll also do some gap analysis projects: trying to understand eligibility versus applications. Are we seeing pockets of the city where there are lots of folks who would be eligible for this program but they’re just not applying? And the final category that data projects generally fall into is an equity analysis: trying to understand demographic disparities in access. In terms of broadband access, are we seeing any demographic disparity in pockets of the city where access is low?

It sounds like you’re relying on many data sources, not just municipal data.

Absolutely. We pull in data from the state government, from the federal government, census data.

Can you describe how you collaborate with different city departments? How do you decide what projects take priority or will be of most use to your citizens when your scope could run the gamut from looking at everything from crime to, say, water use?

The projects that generally take the most priority are ones that are top of mind or salient for the mayor’s office. If there’s a new program that’s rolling out, that’s obviously going to take precedence over our analysis projects like maybe some legacy programs. So we’ll prioritize things that are new programs and services that are being rolled out just to ensure that there aren’t any gaps in who’s eligible versus who’s applying—and to ensure that we’re doing the appropriate amount of outreach for those new programs and services.

What does that look like right now when we’re still wrestling with Covid both from a public health standpoint, but also certainly there’ve been many pressure points—say food insecurity or even just employment. What is your role there?

This definitely has been an unprecedented time to be working in local government. And I think we’ve really had to shift to becoming a human and social services provider at this time. Data has really been more important and integral to our operations than ever before. We’re just trying to understand where there are disproportionately impacted communities when it comes to Covid and what are the specific needs and characteristics of those communities and what can we do to meet their needs and lighten the burden? Data has absolutely helped us not only understand where the need is greatest, but also helped us understand whether there are any underlying demographic disparities there and where we need to do more outreach—where folks are maybe eligible for some of these programs and services, but not applying or not hearing about them.

There’s a tremendous emphasis on how we keep schools and education operating safely. And certainly this looks very different this year than last, but what role did your office have in crunching those numbers?

Our office played a big role in, first of all, understanding broadband access around the city. There certainly are pockets of the city that have low access to the internet. During Covid, to make sure that students were connected to online classes so they could continue learning, the city rolled out a program called the Angeleno Connectivity Trust. That was a partnership with T-Mobile to distribute 18,000 Wi-Fi hotspot devices. There are programs like that we never before worked on that had to roll out very quickly just to meet the need and to ensure that the students who maybe lived in homes that didn’t have internet connection could still take online classes and participate in school. We did some analysis of broadband access data and then were also working on operations dashboards for that program, the Angeleno Connectivity Trust program, just to see which zip codes in the city are getting these Wi-Fi___33 hotspot devices. And what’s the distribution of grade levels for students who are receiving these devices and what are the organizations that we’re working with primarily?

Can you give me an example of a recent insight or analysis that came about because of data analysis that might not have otherwise happened?

We recently worked on a dashboard that was looking at applications for the emergency broadband benefit. We’re looking at zip code, where are we seeing applications for that federal program? And where are people eligible? This goes back to the gap analysis that I was talking about earlier. We were able to identify zip codes where there were a lot of people eligible based on the criteria—like 150% of the federal poverty rate. There are some neighborhoods where you have a lot of people who are eligible just based on our knowledge of census data, but we’re seeing very low applications. We are sharing that information internally, so we can do maybe some more outreach just to raise awareness about the fact that this broadband benefit exists.

What role do you see AI playing in the future?

AI definitely plays a role in some of the data work happening at the city. For example, our bureau of street services is really interested in using AI to detect road quality and identify potholes. There are devices that you can mount to vehicles that read in road data and identify cracks and the severity of those cracks and the types of cracks that they are. So the street services team is really interested in using AI for that work because it would augment our ability to maintain road quality. The hope is that it’ll assist the department in understanding which segments of street need to be prioritized for repavement. It’s in a pilot phase.

Is there some other project that you think has a natural match for applying AI?

When it comes to any technology, it always starts first and foremost with the mission of that agency or that department. It always starts with, this is our mission, this is our mandate and how can we do this more efficiently, more effectively and also integrate more with other departments?

Looking a little farther ahead, what trends are you watching over the next year ahead and what do you think will be the top priority?

First and foremost, particularly in the public sector, I think CDOs and data teams are increasingly being seen as mission critical functions. And I’m so glad to see this shift happen because I think it is integral to improving internal operations as a city and also improving programs and services that the city provides. I’ve seen a lot of cities hire CDOs and build data teams and I think we’re behind the private sector in that respect. These are teams that you already see in a lot of private sector organizations, but I’m seeing just a real increasing understanding that this is a mission critical function in the public sector as well.

What do you think triggered that change?

A lot of it has to do with the fact that there is this broader cultural shift toward data-driven leadership. You really saw that a lot in the private sector. There’s just so many examples of organizations that are leveraging data science to serve you the movie that you might want to see next or recommend products or services that you might like. And I think it’s just spilled over into the public sector as well. Just this realization that there’s so much data out there. I think there’s a desire on the part of a lot of local state and federal governments to make improvements and really leverage the best tools that are at our disposal to do more.

I lived in New York City when then Mayor Michael Bloomberg brought 311 to life. That was some time ago now, but what has changed in regard to what problems data analysis can help solve?

There are so many public sector challenges that data analysis can help solve. It all comes down to access to quality data and having the organizational capacity to work with that data. Mayor Garcetti has been really great about promoting open data and data sharing at City Hall. There was an executive directive back in 2013 that established open data within the city of Los Angeles. Since then, we’ve just really focused on increasing data sharing within the organization and also publicly externally through our open data portals. And I think that’s proved itself to be really valuable both in first of all, helping us better do our work, but also helping mission-aligned organizations do their work.

What is the biggest challenge? You mentioned it’s critical to have quality data, but are there other obstacles that get in the way of accomplishing what you want to in the future?

The biggest challenge would be just continuing to build a data culture. That’s something that a CDO role is also sort of like a systems change role. You’re an agent of change throughout the organization. It takes time to sort of foster that data-driven culture and to break down the barriers and silos that exist. I think that’s probably something that’s always going to be a work in progress, something that you can improve upon.

What have you found to be effective?

What I found really effective is just holding a standing monthly meeting. We host the city-wide data collaborative once a month and we’ll convene all data stewards and typically start with a data spotlight. We’ll have a presentation of a really cool data-driven project that’s happening at one of the departments. It’s sort of like a show and tell. But I think it’s great—first of all it’s such a large organization it’s very hard, especially in this remote era to understand what’s going on on the other side of the city and what cool projects other teams are working on. I feel like that really inspires people and helps them understand who’s working where and what’s happening at their department.

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