Zurich Airport faces similar challenges in its data landscape. By transforming the data journey, the airport created a scalable, data ecosystem-based foundation that enables near real-time decision-making, improves collaboration, and enhances operational efficiency. The insights from this transformation are valuable for any business embarking on a data ecosystem transformation, not only in the transport and mobility sectors.

In this blog post, Philipp Morf, Director of Data Science at Zühlke and David Hangartner, Head of the Data and Analytics Competence Centre at Zurich Airport, examine the ongoing transformation of Zurich Airport's data landscape and share the lessons that other organisations can learn from the experience.

• Why data silos are a major barrier to operational efficiency in complex environments.
• Best practices for building a future-proof data ecosystem.
• How to balance data governance, security, and accessibility.
• The role of AI and near real-time analytics in driving better decision-making.
• Lessons from Zurich Airport’s experience that apply to any organisation.

Many organisations rely on data-driven decision-making, yet their data remains fragmented across disconnected systems. Without a strong central data system, decisions are often reactive – with negative effects on both the efficiency and the experience for clients and users. This challenge is particularly evident in large-scale, multi-stakeholder environments like airports, logistics hubs, and multinational corporations. 

Some of the key challenges include:

• Data fragmentation: Critical operational data is stored in disconnected legacy systems, preventing a unified near real-time overview.
• Limited collaboration: Stakeholders manage their data independently, making cross-functional coordination difficult.
• Rising demand for automation and AI: Teams are eager to leverage predictive analytics and benefit from near real-time decision-making. However, the infrastructure to support this isn’t there.
• Regulatory and security constraints: Industries like aviation, finance, or healthcare face strict rules. These rules make data sharing and interoperability more complex.

Zurich Airport experienced many of these challenges firsthand. Their journey to a fully integrated data ecosystem provides valuable insights for any organisation looking to improve its data strategy.
 

2. Build a future-ready data foundation

To scale AI-driven applications and analytics, organisations need a strong data infrastructure. With our support, Zurich Airport developed a modern data platform using lakehouse architecture on Azure and Databricks.

This foundation was built with several key principles in mind:

• Standardised data processing to ensure consistency across all systems and reduce redundancies.
• Automated governance and security frameworks to maintain compliance while making data accessible where needed. Data governance and data ethics can mitigate risks, ensuring that AI systems remain transparent and aligned with ethical standards.
• Interoperability with existing systems, allowing organisations to leverage historical data and legacy applications without major disruptions.

A well-structured data foundation ensures that an organisation is not just collecting data but is also extracting value from it. With this architecture in place, Zurich Airport is equipped to expand data-driven applications, improve near real-time analytics, and enhance decision-making at every level.

3. Deliver near real-time visibility to improve operational efficiency

For organisations managing complex operations, near real-time visibility across multiple stakeholders is crucial. A system that integrates and combines key data sources enables teams to anticipate disruptions and make informed decisions. Effective process observation enhances coordination and ensures teams can proactively manage operational changes.

For Zurich Airport, developing the Airport Operations Plan (AOP) application was a great solution. The AOP is a near real-time application designed to improve operational coordination and situational awareness. Built on Zurich Airport’s existing data platform, it integrates multiple data sources such as flight schedules, weather conditions, and ground operations to enable proactive decision-making. Close collaboration between IT and business teams ensures alignment on goals, while early user involvement drives adoption and usability.

A user-centric approach – where stakeholders are actively involved in defining requirements – ensures the system meets operational needs. Continuous feedback loops help refine solutions iteratively, keeping them aligned with evolving operational needs. By integrating multiple data sources, applications like AOP provide a comprehensive view of operations, improving decision-making and collaboration.
 

Any organisation aiming to enhance its data-driven decision-making can benefit from a similar approach, ensuring they not only collect data but also use it effectively to drive tangible improvements.

Zurich Airport’s journey highlights key best practices for any organisation looking to unlock the power of data:

• Start with governance: Establish clear ownership, access rules, and compliance standards early on.
• Focus on business value: Align data initiatives with real operational needs, not just technical possibilities.
• Adopt an agile approach: Iterative development and continuous learning ensure faster, more impactful innovation.
• Balance security and accessibility: Automated governance frameworks can manage privacy and boost usability.

Zurich Airport’s journey demonstrates how a structured approach to data transformation can drive efficiency, innovation, and scalability. By focusing on governance, interoperability, and AI-driven insights, the airport has set a benchmark for intelligent, connected aviation.

For businesses embarking on their own data journey, the key takeaway is clear: start with business impact, build a strong foundation, and adopt an agile mindset to drive meaningful transformation.