How to build resilience through smart data strategy

It didn’t happen overnight, but Philadelphia CIO Mark Wheeler built a digital transformation model others can follow

CIOs in every industry today are looking to build more resilient organizations during a global pandemic. Tech leaders at government agencies, who are accountable for continuity of so many critical services, feel that pressure perhaps more than anyone.

For Mark Wheeler, CIO of the City of Philadelphia, resilience is emerging not from dramatic new strategic moves or investments, but from foundational pieces of a digital enterprise he’s built over the last six years.

The core of that foundation is a digital platform that solved a major legacy challenge Wheeler began working on in 2014—huge volumes of incompatible or inaccessible data, stuck in silos created by hundreds of disparate city services.

Through unified data management, Wheeler’s team has made big strides in recent years: The city now counts more than 250 publicly available datasets for tasks like parking violations, building footprints, and commodities contracts.

When the COVID-19 pandemic arrived in early 2020 and sent the majority of city employees home, Wheeler and his IT teams found ways to keep both citizen services and digital initiatives moving, without major hiccups.

Both public- and private-sector CIOs, in fact, might take a lesson from Wheeler’s focus on the basics. According to a new executive survey by ESI ThoughtLab and ServiceNow, data management ranks as the third-highest investment area of organizations considered leaders in digital transformation: 89% of these digital leaders have invested heavily in data management, while just 64% of “follower” organizations have done the same.

“There has to be a very well thought out data schema, execution, and set of skills that we need on board,” Wheeler says. “Much of what we want to accomplish in government relies on that seamless set of transactions.”

Streamline data collection

Collecting and organizing data efficiently is the common thread of any successful digital transformation project, Wheeler says. In Philadelphia, he began by identifying the most important use cases for data analytics across dozens of city departments. The initial goal, he says, was to pull data from many different sources. Once those were organized, he then shifted to automation tools to streamline the collection process. Ultimately, Wheeler says, it helped create “a common data model for better integration and better sharing,” he says.

There, too, Wheeler showed how some public agencies are ahead of private industry, according to the survey. Compared to surveyed companies in four major sectors—financial services, telecom, healthcare, and manufacturing—public-sector agencies do the best job of ensuring processes are well designed for automation before pulling the trigger. (Even the most intelligent automation tools, after all, can’t fix a broken process.)

Centralize data ops

Once Wheeler’s team digitized collection workflows, the next step was to build a data warehouse for centralized key administrative databases—including those for HR, licenses, and building inspections. “There was a very outdated model of siloed applications with their own structures and schemas,” Wheeler recalls. This made data sharing extremely difficult. That, he adds, “created a cost burden on the city to modify schemas so they could communicate.”

The warehouse strategy then allowed the city to develop its own business applications. Those now include Atlas—a citizen app for researching everything from neighborhood crime to real estate trends—and, a portal that manages access to dozens of city services, from trash collection to appealing a zoning decision.

Centralized data has also changed how Wheeler and other city IT leaders work with partners. Today, the city has an internal set of APIs that have become standard elements of faster and more efficient RFP and engagement processes with vendors. “We make it very clear that this is how integrations are to happen,” says Wheeler. “These are the data structures and data standards that we have.”

Use data-viz for better decision-making

The next challenge for Wheeler was more of a cultural one. Leaders across dozens of city agencies had limited levels of accessibility and familiarity with the new data tools. They had data that showed what was happening inside their own departments, but had little visibility beyond that.

With centralized data and APIs, however, his team built new analytics dashboards packed with visualization features to give city workers easy access to more data. The new data-viz features “helped decision-makers,” says Wheeler, including leaders from the Philadelphia Police Department to the city’s office of homeless services. “They all needed views on that information outside of their own scope of knowledge,” he says.

Maintain the innovation pace

With this solid foundation and early wins, Wheeler’s team began rolling out pilots to show how Philly’s new data capabilities would pay off in different ways. Then came COVID-19. The pandemic delayed the timeline of some projects, such as creating new efficiencies in waste management using data and logistics, but it hasn’t changed the overall strategy.

The key to success, Wheeler says, remains the same—getting his hands (and his APIs) on more data. One application with huge potential, he says, is using optical sensors to monitor collision patterns on city streets to reduce fatalities and injuries without compromising citizen privacy.

“There’s an incredible amount of value to put ethical AI and machine learning to work with imaging technology,” he says. “We’re pushing some of our existing pilots in that direction.”