Cross-Departmental Collaboration: Turning Siloed Data into Shared Insight

One pivotal moment in my career as a GIS analyst came during a proposed redesign of a major arterial roadway. Initially scoped as a routine resurfacing project, the initiative took a significant turn after we aggregated data from multiple departments, including transportation, public health, and emergency services. A shared dataset revealed a pattern of pedestrian injuries concentrated around intersections near schools and senior housing facilities. This insight prompted a reevaluation of the project’s goals, shifting from mere pavement replacement to a comprehensive redesign that included traffic calming measures, upgraded crosswalks, and bicycle lanes.

The key to this shift was not just the availability of data, but the willingness of departments to share and interpret it together. Transportation planners brought in collision reports, while public health staff contributed data on injury severity and hospitalization rates. Emergency services provided response time analytics, which were layered spatially in our GIS platform. This collaborative approach, enabled by interoperable data systems and shared dashboards, resulted in a safer and more inclusive design. The project ultimately received additional funding through a state pedestrian safety grant, which likely would not have been pursued without this integrated planning approach.

Data Integration Challenges and the Importance of Context

Integrating data from multiple sources often reveals inconsistencies that require both technical skill and contextual understanding to resolve. For example, parcel data from the planning department may use different geocoding conventions than utility infrastructure maps maintained by the public works department. Without standardization, overlaying these datasets can result in misalignments that skew analysis. In one project involving stormwater infrastructure upgrades, we discovered that address mismatches between permitting records and asset management databases were causing planners to overlook properties at risk of flooding.

Addressing these discrepancies required not only data cleaning but also active engagement with the domain experts who manage each dataset. A regular working group with representatives from planning, utilities, and IT allowed us to define shared data standards and implement consistent update protocols. These conversations also uncovered valuable institutional knowledge not captured in digital formats, such as historical infrastructure modifications or undocumented easements. By embedding this qualitative context into our spatial analysis, we improved both the reliability of our models and the trust stakeholders had in our recommendations.

Engaging Non-Technical Partners Through Visualization

One of the most effective ways to bridge the gap between technical analysis and policy de