WWC Certification Data Insights
By: What Works Cities
Tags: Cities, Data, Innovation, Data Driven, Local Government

WWC Certification Data Insights

Go behind the scenes in this first installment of ‘The Data and the Details’ series

By Jennifer Park & Lisa Mae Fiedler

It’s not a revolutionary idea that mayors and city leaders want to make good choices for their cities and residents — and a cross-departmental commitment to data and evidence can be the game-changer in many city halls. For city leaders, starting with the question “what is our data telling us?” can be critical in navigating the challenges of ensuring that all residents have access to quality services and live in thriving communities. As straightforward as it may sound, however, the act of using data to inform decisions is not an easy task and requires not only bold leadership but also resources and a roadmap.

That’s why in 2017 we launched What Works Cities (WWC) Certification, the national standard of excellence for well-managed, data-driven cities. Now, cities across the country can assess themselves against this standard and aspire towards WWC Certification. By implementing the Certification program’s leading practices, cities are building the foundation necessary to not only ask “what is our data telling us?” but also understand the answers to inform key decisions.

[Learn more about the methodology behind WWC’s Certification program.]

Over the past three years, 203 cities* have taken the first step towards a more data-driven government by submitting a WWC Assessment to benchmark their practices against the national standard. The number of cities that have achieved Certification has nearly tripled since launching the program in 2017, from nine to 24. Plus, six cities have since leveled-up to achieve Gold Certification, and two cities have advanced to Platinum Certification.

Familiarize yourself with the “basics” of the Certification data.

Through the WWC Assessment process, we have learned from cities what’s working well (and what’s not!), what challenges they face as they build a culture of data and evidence, and in many cases, interesting tidbits that can be instructive for other cities. Our Certification experts have combed through hundreds of municipal contracts, reviewed open data policies, spent hours on the phone with city teams, and dug into the results of quasi-experimental evaluations. The data validated through this rigorous assessment process has been invaluable as we’ve worked with cities to help determine the immediate and long-term steps they can take to become leaders in data-driven government.

We want to start sharing the insights we’ve gathered with you through a new series called, The Data and the Details. In this series, we plan to publicly answer questions we’ve personally had about the program’s data such as:

Are there differences in criteria achievement based on a city’s form of government or city population size?

What are the common characteristics of the cities that succeed in mastering foundational data practices?

These questions are important not only for cities that are just starting their data journey but also for cities that are testing and piloting new data-driven solutions to citywide challenges. Analyzing and understanding the data cities collect is critical for decision-making, and WWC embraces that as best practice as well.

So, to start us off, we want to share insights into how cities have improved practices in our eight foundational practice areas.

Within a year, we saw achievement improve in all foundational practice areas, except for in Repurposing. 74 percent of city assessments demonstrated publishing open data to a central public online location, the most commonly achieved criterion, while less than 1 percent of city assessments confirmed a documented policy or practice aimed at harnessing the benefits of artificial intelligence (AI) while reducing associated risks. This shows that while many cities collect and publish data online, they still need to build policies and practices to reduce the impact of demographic and geographic biases that may exist in that data.