The data economy refers to business activities based on collecting, processing, sharing and using data to create value. In the field of location data, global actors such as Google, OpenStreetMap and Overture Maps Foundation have demonstrated how powerful widely available geospatial data can be — and how ecosystems can be built around shared data.

From a European perspective, we already have a strong foundation. The INSPIRE Directive required public authorities to produce and share spatial data through common standards and APIs for years. Even though INSPIRE requirements are expected to be significantly reduced, we still have a clear advantage: Europe has long experience in interoperability, public data governance, and cross-border data use.

With European data spaces, AI and new regulation emerging, now is the time for organisations to position themselves — not only as data users, but as active participants in the European data economy.

1. Data quality, interoperability and sovereignty are the foundation

The data economy only works when data can be trusted, understood and combined. For location data, this means standardisation, high-quality metadata and interoperable APIs.

But increasingly, data sovereignty is just as important. Organisations need to understand:

• where their data is stored
• who controls access
• how it can be shared securely
• how value is created and retained

Poorly documented or incompatible data creates costs, errors and missed opportunities. At the same time, lack of control over data can limit business potential or create dependency on external platforms.

Geospatial-specific approaches such as Geospatial Reference Data Standards (GERS) help ensure that core datasets (addresses, transport networks, administrative units) are consistent, reliable and reusable across borders.

To build a solid foundation:

• Explore interoperability via the Location Innovation Academy
• Understand high-value datasets through the EU High-Value Datasets framework
• Apply national quality frameworks such as Statistics Finland’s Quality Framework for Data
• Use GERS principles to ensure consistent and reusable geospatial reference data

2. Value comes from integrating data across silos

Location data becomes powerful when combined with other datasets: buildings, transport networks, climate data, mobility flows, or business data.

However, integration must be done carefully. Coordinate systems, identifiers, semantics and data models must align. Otherwise, even advanced analytics will produce unreliable results.

At the same time, integration should respect data sovereignty principles — enabling collaboration without losing control over data assets.

To get started:

• Learn integration practices at the Location Innovation Academy
• Use the Geospatial API Business Guide to structure your data-driven business
• Explore integrated datasets and APIs via Location Finland and Location Europe and test your own use case

3. European data spaces enable trusted data sharing and growth

Europe is building data spaces — federated ecosystems where organisations can share and access data in a controlled, secure and interoperable way.

These data spaces (e.g. in mobility, energy, agriculture, health and the Green Deal) are designed around key principles:

• data sovereignty (you stay in control of your data)
• trust and security
• interoperability and standards
• fair value creation

Location data plays a central role in all of them, acting as the “glue” that connects datasets across domains.

To explore opportunities:

• Discover pan-European geospatial data and APIs on Location Europe
• Explore how your services can plug into emerging European data spaces, for example through national data spaces

4. Using European data is a strategic advantage

European organisations have access to a unique combination of:

• high-quality public sector data
• open and high-value datasets
• trusted governance frameworks

This creates a strong foundation for building scalable and trustworthy services — especially compared to fragmented or proprietary data ecosystems.

Leveraging European data means better interoperability across countries, easier access to public data, alignment with regulation and funding programmes, and stronger positioning in EU markets.

To move forward:

• Identify relevant datasets via European data portal
• Use standards (OGC, ISO, CEN)
• Build services that can scale across borders

5. AI and GeoAI are unlocking the next level of value

Artificial intelligence is rapidly transforming how data is used — and location data is one of its most powerful inputs. GeoAI combines AI methods with geospatial data to generate insights, predictions and automated decision support.

Examples include:

• Satellite and aerial image interpretation
• Real-time situational awareness using sensor and mobility data
• Predictive models for climate, forestry, agriculture and urban development
• Digital twins combining spatial data, simulation and AI

In the European context, combining GeoAI with data spaces and sovereign data sharing enables federated learning across organisations, privacy-preserving analytics, and cross-border AI services. However, AI is only as good as the data behind it, which reinforces the importance of data quality, interoperability, and trusted, sovereign data access.

To start leveraging GeoAI:

• Build on quality data
• Utilize distributed architectures to access data to ensure data quality.
• Build skills through our Location & AI online courses.

In short

Success in the modern data economy is about:

• quality and interoperability
• integration across domains
• data sovereignty and control
• participation in European data spaces
• leveraging AI and GeoAI

Location data sits at the centre of this transformation. Europe has a unique opportunity to lead with trusted, interoperable and sovereign data ecosystems