The Gard Department is a local authority that employs nearly 3,000 agents. Every day, they serve the public interest in various areas: public health and social action, departmental roadworks, middle school, high-speed broadband, etc.
Through these activities, the Gard Department generates and processes large amounts of data daily, which the Department, through its Innovation and Information Systems Directorate, aims to leverage. To this end, a study was conducted with fellow local authorities and more broadly within the data ecosystem to identify the state of the art and define a way forward.
This study revealed that the Modern Data Stack is widely adopted by both large corporations and smaller companies. Dedicated websites, podcasts, communities, and conferences extol its virtues. However, despite this widespread enthusiasm, implementations in local authorities are scarce: is the Modern Data Stack unsuitable for the challenges faced by public local authorities ?
A local authority is primarily defined by its territory. Consequently, the data we handle is largely geolocated:
Are there accident-prone areas that would require changes to road layouts? Where are the populations most in need of assistance, and how should we distribute our resources across the territory? What is the most relevant location to build the future college considering the students' locations ? Therefore, the stack must be geographical! With this constraint established, which tools should be chosen to build our Modern Data Stack?
This talk will present the stack implemented by the Gard Department and the reasons behind these choices. We will see that while some reference components, such as DBT, handle geographical data well, others struggle, leading us to rely on lesser-known data science tools but widely used by geomaticians. We will also explore some use cases illustrating how geography enhances data analysis and decision-making. Finally, we will conclude with the prospects for evolving our stack and organization.
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