• December 17, 2020
• Civic Analytics Network

One runs out of adjectives to describe 2020: unprecedented, extraordinary, nightmarish, unlucky, devastating. Racial tensions reached a long-coming boiling point, the unemployment rate skyrocketed, democracy was under attack, and more than 301,000 Americans died of COVID-19 (at the time of publication). As the year finally winds down, it’s difficult to look back at twelve long months that many would rather forget.

However, there are important lessons buried in the past year, and some important successes that should be highlighted. The city and county Chief Data Officers (CDOs) of Harvard University’s Civic Analytics Network (CAN) graciously contributed to this retrospective by discussing how they addressed racial inequality, furthered data-driven policies, and contributed to COVID-19 insights — all while managing a new remote work situation on top of usual responsibilities. As 2020 finally moves into the rearview mirror, it’s crucial to build on what worked, discuss new best practices, and share lessons learned.

“The appetite for data has tremendously increased and data insights are becoming the norm,” said Mike Sarasti, the Chief Innovation Officer (CIO) and Director of Innovation and Technology in Miami. This sentiment was echoed in every response to Data-Smart; while data has always been important in government decisions, this year’s fast-moving infections, shifting regulations, and budget reductions had every department calling on the CDOs and their teams. None of them think this will slow down, with many of them anticipating an even greater demand for data in 2021.

The Coronavirus

One obvious need for data was in developing COVID-19 dashboards. Many of the CAN member cities had their novel coronavirus dashboards up and running by April 2020, which helped inform policies on stay-at-home orders and kept residents up to date on infection rates. In addition, data was useful for many other COVID-related projects. For example, in Philadelphia, the move to remote learning brought new urgency to closing the digital divide, which left many low-income students unable to participate in online education. The Office of Innovation and Technology (OIT) partnered with the Mayor’s Office of Education to create and launch PHLConnectED, which provides free internet for kindergarteners through 12th graders in need. If the student’s family has secure housing, they are eligible for a free in-home high-speed connection, and if they are housing insecure, they are eligible for a free high-speed mobile hotspot. This program, the first step in a broader digital divide initiative, uses city demographic data, American Community Survey data, maps of public wifi, and surveys of Community-Based Organizations (CBOs) to identify where help is needed and provide connectivity so that students don’t fall behind.

In Arlington County, Virginia, Chief Data Officer Jaime Lees partnered with the local Emergency Management Team to track the county’s supply and usage of personal protective equipment (PPE). Lees’ team also used data to project future supply and demand, an incredibly useful predictive system that protects frontline and healthcare workers by revealing where supplies are running low. According to Lees, this required the creation of “a simple, broadly accessible data entry mechanism” so that all stakeholders could access and report on the PPE levels.

In Syracuse, the Innovation Team had to get creative in order to understand and track COVID hotspots. Since the city had limited access to COVID-19 tracing data (there is no Syracuse health department; the city relies on the greater Onondaga County), the team mined emergency calls, mobility data, and even weather patterns in order to visualize infections. By doing this over most of the year, they not only had a reliable map of day-to-day infection rates, but were able to trace the impact of COVID-19 restrictions. Newly helmed by CDO and CIO Nicolas Diaz Amigo, the Innovation Team was able to see how lifting the initial shutdowns and allowing small gatherings impacted — and potentially increased — COVID-19 cases. Data-driven decisions on lockdowns and gatherings wouldn’t have been possible without Diaz Amigo and team's work.

Racial Justice

Another area ripe for data-driven policies and statistical insights is racial equity. After the fatal shooting of Breonna Taylor and homicide of George Floyd, people across the country (and the world) took to the streets to demand change. The history of structural racism in the United States has resulted in unequal treatment, health outcomes, homeownership, rates of COVID-19, and unemployment for Black Americans. Faced with this information, many cities are now seeing how data can guide actions against the entrenched legacy of racism.

The city of Denver has a dedicated Office of Social Equity and Innovation (SEI) that works “to increase systems, policies, and practices that sustain social equity, race, and social justice.” According to Denver CDO Paul Kresser, the city’s data team works closely with the SEI team to collect and analyze data in support of these goals. Data-driven practices are important for the city to make progress; not only does data help city government identify areas of improvement, but it provides a measurable scale for the city to track what does and doesn’t increase equity.

Philadelphia is similarly reviewing its practices, and is visualizing capital budget spending data to determine where the city has historically allocated assets. In a pilot study with the Parks and Recreation Department, the OIT’s CityGeo Team mapped out all the city’s physical recreation assets, then layered over the last 20 years of budget investments. Analyzing this information with demographic data, the CityGeo Team helped identify which communities were historically invested in. They also used the map to find the walking distance from neighborhoods to recreational spaces; coupling this information with data on Philadelphia’s land banks, the OIT team has developed a dashboard to guide equitable investment and development in traditionally underserved areas.

In Syracuse, Diaz Amigo’s team helped gather data on COVID-related furloughs to make sure that the city wasn’t disproportionately laying off certain groups or types of people. According to Diaz Amigo, “race, gender, age, and type of employee (salaried vs. hourly) were considered” to make sure that the necessary furloughs weren’t biased. The city is also collecting data through the new Inclusion, Diversity, Equity, and Accessibility (IDEA) Office, with surveys that measure employee equity and possible biases, and qualitative data on any concerns or ideas about increasing diversity.

Carrying On

Despite the new responsibilities outlined above, city data offices still have their original goals and mandates; admirably, they have managed to keep calm and carry on with their usual data work, despite the extreme circumstances. Kat Hartman, director of Detroit’s new Data Strategy and Analytics team, onboarded 75 percent of her employees during Michigan’s Stay Home, Stay Safe order. Despite being 100 percent remote since its inception, the team has managed to drive large-scale data governance, including a Base Unit integrated address schema.

And Allegheny County launched their Hello Baby initiative, which “provides tiered resources to all new parents in the county.” With information from the county’s data warehouse, Hello Baby relies on a predictive risk model to better identify new parents who may be in need of additional support, flexible service delivery, and early intervention. The program is voluntary, but aims to “reduce child maltreatment and improve overall family outcomes” in the county.

“When we work together, we thrive together,” said Sarasti, speaking about Miami’s economic indicator report and data sharing partnerships, “The road to recovery might be long but it presents a myriad of opportunities that can be harnessed through data.” While 2020 was a year of uncertainty, protests, and mourning, the CDOs in this article found ways to help support and serve all residents with data.