We have previously argued for the strategic necessity of digital control, sovereignty, and data ownership, and emphasized the role of procurement as the starting point for setting requirements for open standards, interoperability (think EIF!), and open source software. Formulating these requirements is a critical initial stage, but the real test – and the opportunity for business value and real gain – lies in practical implementation.

Specifying that systems should “be interoperable” or “enable data sharing” is necessary, but it doesn’t guarantee that it actually works in practice. The reality in most organizations is characterized by a complex system landscape with applications that have grown over time, often creating data silos that neither “talk” to each other nor easily share information. The technical debt is palpable, and the organizational silos between departments can be as effective barriers as technical ones.

Transforming this landscape into a interconnected, intelligent ecosystem where data flows freely and securely, and where systems can collaborate to deliver better services, requires a determined and practical strategy. It’s the journey from paper requirements to functioning information management.

How do you navigate this complex terrain? Here are some practical steps to move from intention to implementation when it comes to interoperability and data sharing:

1. Start with business value: Technology is a means, not an end. Identify concrete use cases where improved data sharing or system interoperability creates clear value. This might involve streamlining a process, improving decision-making with more complete data, or offering a new digital service to citizens/customers. Start small with a pilot project that has clear goals and measurable impact.

2. Map Your digital landscape: You need to understand what you have. Which systems exist? Where is critical data stored? What do the current (perhaps manual) data flows look like? Which technical interfaces already exist? A thorough baseline analysis is necessary to identify the main data silos and the most critical dependencies.

3. Establish robust data governance: Who owns the data? What is its quality? What rules apply to sharing internally and externally (legal, ethical, security)? Achieving semantic interoperability requires common definitions and data models. Without clear data governance, data sharing risks creating more problems than it solves. This is an organizational and processual cornerstone.

4. Design the architecture for sharing: Move from point-to-point integrations (which create spaghetti architecture) to a strategy based on open APIs (Application Programming Interfaces) and message buses or integration platforms. A modern architecture, often inspired by microservices, is designed to share data and functionality in a controlled and scalable manner, in line with the principles of the EIF.

5. Implement incrementally and agily: Don’t try to solve all integration and data sharing needs at once. Prioritize the use cases that yield the highest and quickest business value. Use agile methods to deliver functioning integrations in iterations, gather experience, and adjust the strategy along the way.

6. Build competence and culture: Working with APIs, integration platforms, data modeling, and data governance requires the right competence. These are areas where many organizations need to develop. Equally important is cultivating a culture of data sharing and collaboration across organizational boundaries.

Succeeding with interoperability and data sharing means transforming your digital assets – your systems and above all your data – from isolated islands into an interconnected network that can generate real business value. It leads to more efficient internal processes, better foundations for strategic decisions, an improved experience for citizens/customers, and not least, increased digital control by allowing you to fully utilize your data and systems, independent of individual vendor limitations.

It is a complex journey that requires a combination of strategic insight, technical expertise, and the ability to drive organizational change. Many organizations need support to analyze the current state, define the target state, design the architecture, establish governance, and project manage the implementation of these critical capabilities.

If your organization struggles with data silos and lack of interoperability, but sees the potential in data-driven innovation and efficiency – then it’s time to take those practical steps. The journey towards true interoperability and data sharing is crucial for converting digital ambitions into tangible results and securing your digital future.

Are you ready to begin the journey to break down your silos and unlock the potential of your data?