A Smart City Starts with Smart Data

What Does “Smart City” Really Mean?
In an era where almost every city aspires to call itself “smart,” it’s easy to confuse appearance with essence. Many equate a smart city with flashy apps, intelligent traffic lights, or centralized control systems. a smart city is first and foremost a city that understands its data and acts upon it. This is where the real challenge begins — municipalities often hold vast amounts of information, yet suffer from a shortage of actionable insights. Data is scattered across dozens of databases, hundreds of reports, and systems like CRM, BI, and GIS, each speaking a different language, using different structures, and rarely connecting meaningfully to decision-making processes.

From Data Overload to Clarity – Structure as the Foundation for Insights
The first step in turning information into insight is creating an urban ontology — a standardized, agreed-upon mapping of all existing data: schools, public facilities, budgets, events, citizen inquiries, and the relationships between them. Once there’s a clear, consistent data structure, it becomes possible to ask the right questions and receive answers with genuine value.

Starting from the Pain Points, Not the Technology
The temptation to start with an impressive new tech platform is strong, but a truly smart city begins by identifying its pain points: recurring bottlenecks in citizen requests, repeated operational failures, or a lack of transparency in service information. This is where the potential for immediate value lies. It’s not about searching for something entirely new to implement — it’s about improving what already exists using the data at hand.

Examples of Early-Stage Applications
In education, combining data on enrollment, performance, complaints, and resource allocation makes it possible to forecast school and kindergarten capacity, identify geographic service gaps, and plan support staff, transportation, and budgets with precision. In waste management, integrating data on collection routes, frequency, and citizen feedback enables route optimization, identification of inefficiencies, cost reduction, and improved satisfaction. In public safety, analyzing calls to the municipal hotline about noise, lighting, vandalism, and recurring incidents helps identify “hot spots” and provides a foundation for enforcement, infrastructure planning, and service delivery.

Algorithm Transparency – The Basis for Trust and Control
When incorporating AI and machine learning systems, it’s essential that they not only deliver results but also explain the reasoning behind them. This prevents “model hallucinations” — false conclusions drawn from random patterns — builds trust among managers and staff, and enables ongoing improvement. Systems should include a human-readable explanation layer, whether through dashboards, business rules, or simplified decision logic displays.