What is data exchange?

If data is the lifeblood of modern organizations, data exchange is the circulatory system that keeps it flowing. Sharing data ensures information reaches the right systems and people—fueling operations and enabling informed decisions. Just as the body depends on healthy circulation to function, digital ecosystems rely on governed data flows to break down silos and unlock the value of their data assets.

Data exchange is a fundamental part of data management, the practice of collecting, processing and using data securely and efficiently to drive better business outcomes. It supports various initiatives, from artificial intelligence (AI) development to ecosystem integration with data providers. Data exchanges typically happen through application programming interfaces (APIs), file transfers, streaming pipelines or cloud-based platforms—each tailored to different use cases.

Every day, the world generates approximately 402.74 million terabytes of data. Without effective data exchange, that information (and its value) would be trapped. In the EU alone, cloud data flows generated an estimated EUR 77 billion in economic value in 2024—a figure projected to rise to EUR 328 billion by 2035.

Data exchange is the foundation of any modern, data-driven organization. Those with effective data exchange strategies can unify fragmented internal and external data and unlock deeper insights across departments, partnerships and use cases. 

For instance, through real-time data exchanges, e-commerce platforms are able to dynamically adjust pricing, share data flows among retailers and optimize supply chains. Similarly, these exchanges allow hospital staff to share lab results with external specialists in real time, which can reduce diagnosis times and improve patient outcomes.

Data exchange also plays a crucial role in enabling AI systems to learn and deliver value. By streamlining the flow of data across different systems, data exchange can help certify that AI models are trained on the most current and relevant information. 

Key components of data exchange—such as standardized schemas, secure connectors and governed permissions—help ensure that diverse data sources can be used effectively within AI ecosystems. This allows organizations to integrate third-party data without compromising quality or control.

Data exchange can be categorized along several dimensions—notably timing, architecture and access model. Understanding these distinctions can help organizations design more resilient data-sharing strategies, supporting everything from real-time data flows to secure third-party integrations.

Real-time exchange: Data is transmitted instantly or near-instantly between systems, often in response to a specific event. This is essential in time-sensitive scenarios like fraud detection, Internet of Things (IoT) monitoring or dynamic pricing. Real-time exchange helps streamline decision-making and can be event triggered or continuously streamed depending on system architecture.

Scheduled (batch) exchange: Data is collected and transferred in bulk at predefined intervals such as hourly, nightly or weekly. Common in compliance workflows and extract, transform, load (ETL) pipelines, batch exchange is reliable for moving large datasets. Legacy methods—such as file transfer protocol (FTP) or cloud storage uploads—remain common in these workflows, especially when modern APIs are not yet available.

Streaming exchange: Data flows continuously from source to destination in small, incremental units. Used in high-volume scenarios like telemetry or recommendation engines, streaming supports real-time insights and reduces latency by eliminating the need to wait for full datasets. It's often a core part of data exchange platforms and large-scale analytics pipelines.

API-based exchange: APIs offer structured, programmable access to data, supporting both real-time and batch workflows. They standardize communication across systems, validate payloads and simplify data integration—especially in microservices and cloud-native ecosystems. Many organizations implement API-based exchange through direct integrations, using either custom-built connectors or standardized APIs to automate data flows and reduce manual intervention.

Event-driven exchange: Instead of polling or scheduled jobs, this method triggers data transfer when specific events occur. Common in modern applications and serverless architectures, it helps optimize operational efficiency by sending only relevant information when needed—minimizing network load and improving responsiveness.

Message queues and pub/sub systems: Technologies like Apache Kafka and RabbitMQ use message brokers to decouple data producers and consumers. This pattern enables scalable, asynchronous data flows (when one system sends data, the other processes it later) and underpins many distributed information systems. This allows organizations to support flexible connectors across platforms. Broadcast-style distribution—where messages are published to multiple subscribers simultaneously—can also be implemented via publisher/subscriber (pub/sub) models.

Private exchange: Data is shared within or between trusted parties, typically with strong governance, compliance and audit controls. This model supports secure data sharing for B2B use cases, cloud data-sharing services and internal data fabrics that prioritize sensitive data such as personally identifiable information (PII). 

Public exchange: Data is openly shared via public APIs, data marketplaces or government repositories. These exchanges promote monetization, accessibility and innovation, but require robust validation and usage policies to ensure data quality and integrity. Data exchange platforms such as Microsoft Azure Data Share and IBM Sterling Data Exchange help standardize and secure these processes through built-in governance tools and permission models. 

Peer-to-peer exchange: Systems connect directly—often symmetrically—without relying on a central broker. This model supports federated data systems, decentralized networks and supply chain exchanges, providing resilience and autonomy while maintaining interoperability across external data sources. 

Data formats (sometimes referred to as "data languages") play a key role in data exchanges. Formats can be categorized two ways: text-based and binary-based.

These formats store data in human-readable text and are commonly used for simplicity, compatibility and ease of debugging across systems.

These compact, machine-readable formats are optimized for performance, making them ideal for high-speed data exchange in distributed or limited environments.

Modern data exchange can unlock significant value for organizations. However, realizing this value requires overcoming several technical and operational challenges.

• Interoperability and integration: With standardized schemas, governed permissions and flexible connectors, data exchange helps organizations unify fragmented systems and streamline integration across partners and platforms.
  
  
• Monetization and ecosystem growth: Through marketplaces and structured data-sharing partnerships, organizations can monetize valuable data products—converting once-siloed datasets into revenue-generating assets.
  
  
• AI and automation: Reliable data flows fuel machine learning (ML) systems with up-to-date, relevant information. Well-governed exchanges ensure models are trained on high-quality data, while APIs and real-time streaming enable low-latency inference and feedback loops.
  
  
• Governance and trust at scale: Strong data governance frameworks—including permissions management, validation checks and audit controls—make it possible to scale data exchange securely. By embedding governance into data flows, organizations can reduce compliance risks and build trusted data ecosystems.

• Compatibility gaps: Legacy infrastructure may not support modern formats like JSON or XML, creating friction during integration—especially in hybrid environments.
  
  
• Security and privacy risks: Without strong encryption and validation mechanisms, sensitive data is vulnerable in transit. This is particularly true in high-stakes sectors like healthcare and finance.
  
  
• Data quality inconsistencies: Third-party or poorly governed internal sources can introduce noise, errors or mismatches that cascade through downstream workflows.
  
  
• Governance complexity: As data moves across more platforms and stakeholders, ownership, usage rights and regulatory compliance become harder to manage at scale.
  
  
• Infrastructure costs: Building scalable, real-time pipelines—and maintaining the governance layers around them—requires significant upfront investment, particularly for smaller organizations.