Interoperability

What Is Interoperability?

Interoperability is the ability of two or more systems, components, or services to exchange data and use it without significant modifications. Although efforts to ensure interoperability often focus on information technology (IT), human factors such as communication, collaboration, and a shared understanding of vocabulary are equally important for success.

Table of Contents Table of Contents

1. What Is Interoperability?
2. Key Takeaways
3. The Importance of Interoperability in the Enterprise
4. How Interoperability Works
5. Types of Interoperability
6. Interoperability Use Cases
7. Interoperability Examples
8. Interoperability Frameworks
9. 4 Levels of Interoperability
10. Interoperability and AI
11. Interoperability Pros and Cons
12. The Bottom Line
13. FAQs
14. References

Show Full Guide

Table of Contents

1. What Is Interoperability?
2. Key Takeaways
3. The Importance of Interoperability in the Enterprise
Show Full Guide
4. How Interoperability Works
5. Types of Interoperability
6. Interoperability Use Cases
7. Interoperability Examples
8. Interoperability Frameworks
9. 4 Levels of Interoperability
10. Interoperability and AI
11. Interoperability Pros and Cons
12. The Bottom Line
13. FAQs
14. References

The Importance of Interoperability in the Enterprise

In the enterprise, interoperability is important because it directly impacts an organization’s ability to tear down data silos and meet the needs of diverse stakeholders. For example, if a cloud HR system cannot exchange employee data seamlessly with the organization’s payroll system, employees and contractors may experience delays or errors in payments because their data will need to be re-entered manually. The organization itself may even face compliance issues due to inaccurate or incomplete records.

How Interoperability Works

Interoperability works by enabling different systems, devices, or applications to communicate and share data seamlessly.

This can be achieved through:

Types of Interoperability

Different types of interoperability can be characterized by what they prioritize:

Interoperability Use Cases

Interoperability can be challenging when there is a lack of standardization. Here are some use cases where better interoperability can improve operations and ultimately lead to more efficient and effective outcomes:

• Healthcare
• Smart homes
• Supply chain management (SCM)
• Finance
• Public Safety
• Education
• Transportation
• International trade
• Telecommunications
• Manufacturing

Interoperability Examples

There are stories in the news almost every day about the challenges of interoperability in various industries.

Here are some notable real-life examples that appeared in recent news stories:

• The Connectivity Standards Alliance (CSA) introduced Matter 1.4 in an effort to enhance interoperability across different smart home ecosystems.
• The European Union has taken steps to compel Apple to open its iPhone and iPad operating systems (OSs) to competing technologies under the Digital Markets Act (DMA). This move seeks to ensure interoperability with rival devices and software.
• The U.S. healthcare system continues to grapple with interoperability challenges, particularly concerning the exchange of electronic health records (EHRs). Concerns around data privacy and security are complicating efforts to mandate interoperability.

Interoperability Frameworks

Interoperability frameworks provide guidelines, standards, and protocols to help organizations exchange data and use it meaningfully. Successful framework development needs to strike a delicate balance between standardization and flexibility.

Popular Interoperability Frameworks:

4 Levels of Interoperability

In 2004, the European Union (EU) published the European Interoperability Framework. The framework, which is considered to be a living document, provides guidelines for ensuring digital public services are interoperable across different EU countries.

Interoperability and AI

Artificial intelligence (AI) has the potential to make interoperability frameworks more adaptable than ever before.

By analyzing log files and data flows, AI-driven models can proactively identify potential compatibility and interoperability issues before they become a problem. The predictive modeling that machine learning (ML) models can provide has the potential to help organizations address problems earlier and improve IT operations management.

Natural language processing (NLP) techniques can also enhance interoperability by standardizing the terminology used across different systems and departments. For example, NLP can analyze unstructured text from different sources to identify key concepts and terms and use this information to create a common vocabulary across systems. NLP can also translate data between different formats to help systems exchange information seamlessly.

At the same time, AI solutions can help enforce data governance policies to ensure that sensitive information is shared legally. For example, AI algorithms can analyze large volumes of data to identify personally identifiable information (PII) or protected health information and help ensure that sensitive data is handled appropriately when shared between systems.

Interoperability Pros and Cons

Arguably, the advantages of interoperability can far outweigh the challenges, but it requires stakeholders to prioritize collaboration and standardization:

The Bottom Line

While the definition of interoperability may seem straightforward, its practical implementation can be complex, involving various standards, protocols, and governance mechanisms. To be effective, interoperability initiatives require careful planning, technical expertise, and collaboration among different stakeholders.

FAQs