Data sharing has become a cornerstone of modern business strategy, driving innovation, efficiency, and new revenue streams. According to Gartner, organizations that share data externally with their partners generate three times more measurable economic benefit than those that don’t. Their clients report various forms of increased positive business impacts and value generation from proactive data sharing. These include driving competitive advantage, revenue growth, and superior Data & Analytics team performance.

But despite these clear benefits, a new wave of hesitation has emerged. Concerns about AI-related risks, including exposure of sensitive data, intellectual property leakage, and potential generative AI (GenAI) data misuse, are leading some to pause data sharing initiatives. In industries like healthcare, this hesitation is compounded by recent high-profile security breaches.

The Real Risk is Keeping Data Siloed

While the natural reaction to avoid these new AI-related risks is to put data sharing initiatives on hold, doing so comes at a cost. Going back to siloed environments might slightly reduce the potential risk of IP and GenAI data misuse, but it also results in lower decision quality, inconsistencies and decreased trust in responses.

It is Gartner’s position that returning to data siloed environments — while seemingly an easy fix for confronting AI IP risk or GenAI data misuse — is not the appropriate response. Instead, organizations should be accelerating data sharing initiatives, including with competitors, to improve their risk mitigation and business value creation from D&A and AI investments.

Gartner - Toolkit: 500 Use Cases to Drive Business Value From Data Sharing

Continuing and even accelerating data sharing initiatives supports the delivery of AI-ready data across a variety of AI use cases by ensuring that data and metadata is shareable to expand the scope of data that can be used. It also enables organizations to better identify, manage and mitigate enterprise-wide risks, including those related to AI initiatives.

Below, we explore high-impact use cases across four key industries where real-time data sharing (enabled by platforms like Apache Kafka) delivers transformative outcomes.

Where Real-Time Data Sharing Is Making an Impact

Financial Services

Two examples of real-time data collaboration in finance.

1. Open banking connections with real-time data access
Fiserv works with Akoya to enable real-time financial data sharing between banks and fintech apps, improving customer experiences while maintaining strong security controls and reducing risk related to account opening.

2. Real-time fraud prevention across payment networks
Capital One, Adyen, and Stripe exchange transactional data in real time to detect and block fraud as it happens. This improves trust for merchants and reduces false declines for users.

Deutsche Bank is one of multiple financial institutions sharing anonymized data with financial messaging service provider Swift to test the use of AI to fight fraud. 

Transportation and Logistics

Moving data as fast as the goods.

1. Predictive maintenance through vessel telemetry
Shell, Maersk, and Lloyd’s share real-time vessel and port data to track mechanical issues and prevent failures during voyages.

2. Automotive quality and sustainability through supply chain data sharing
Heathrow Airport has developed CargoCloud to share real-time load data across shippers, freight forwarders, and trucking firms. The system matches shipments with available capacity, cutting transport costs and lowering emissions. 

Manufacturing

Data sharing for faster development and smarter factories.

1. Automotive quality and sustainability through supply chain data sharing
BMW, Mercedes-Benz, and suppliers like Siemens and Bosch are partnering through Catena-X, a real-time data-sharing network. It connects the entire automotive value chain, enabling live data exchange for quality control, supply chain resilience, and carbon footprint tracking.

2. Live equipment data for energy-efficient production
BASF streams real-time data from its air separation units to a shared platform used by the equipment supplier. The vendor builds optimization models and sends back live control commands that reduce energy use and lower costs.

Healthcare

Better decisions and faster response through shared medical data.

1. Unified patient records across providers
Anthem and Epic expanded their partnership to support bidirectional data exchange, to streamline administrative processes like prior authorizations, and give providers more real-time data on patient behaviors like medication adherence.

2. Streaming patient vitals across hospital networks
mphrX and Mount Sinai Health System partnered to share patient monitoring data across locations in real time, enabling faster intervention and reduced ICU transfer rates.

Where Kafka and Conduktor Fit in Data Sharing

Many of these use cases depend on one thing: data that moves in real time. Batch pipelines and static files can't support fraud detection, personalized services, or supply chain optimization.

That’s why streaming platforms like Kafka are at the core of modern data sharing. Kafka handles the scale and speed, but it doesn’t provide the controls needed to safely expose that data outside the organization. That’s where Conduktor comes in.

Conduktor provides the guardrails to securely share Kafka data with external partners, in a cost-efficient way and without the data replication and overhead of other sharing methods. 

Some of the world’s most advanced organizations, like BMW, Fiserv, and Sainsbury’s, already use Conduktor to:

• Accelerate data sharing: Create dedicated, isolated environments for partners in minutes, reducing management overhead and complexity.

• Prioritize security and compliance: Control exactly who accesses your data, protecting sensitive information and ensuring regulatory compliance.

• Cut costs: Eliminate the need for duplicate clusters and replication, saving on infrastructure and operational expenses.

• Enforce governance: Enforce traffic control policies and easily filter, mask, transform, or encrypt shared data to meet business and compliance needs.

TL;DR? You can now share real-time data without losing control. Learn more on how it works.