Skip to main content

Digital Data Exchange to Set Media Standards

According to the Hollywood Reporter, major record labels, technology companies and publisher groups have formed a membership-based organization to create standards for communicating information necessary to license and sell digital entertainment globally.

The U.S.-based Digital Data Exchange (DDEX) was founded by 11 charter members: EMI Music, Sony BMG Music Entertainment, Warner Music Group, Universal Music Group, Apple Computer, Microsoft, RealNetworks, ASCAP, the Harry Fox Agency, U.K. collecting society MCPS-PRS Alliance and Spanish society SGAE. French society SACEM is reportedly finalizing its charter membership.

The group hopes to create voluntary standards to ensure the speedy and accurate transfer of digital music, videos, artwork and information that identifies the music, creators, copyright holders, purchases and other sales-related data.

With more than 100 digital service providers, an increasing number of mobile service providers, dozens of collecting societies and thousands of music publishers and record labels -- most using different software and set up to identify music in different ways -- the back-office systems are creating a log jam and increasing costs for the transition to digital distribution.

Popular posts from this blog

AI Agents Have Memory. Where is The Security?

Enterprise AI adoption keeps running into the same wall: the moment sensitive data, model weights, or agent memory leave a tightly controlled environment, security teams lose visibility into what happens to them while the workload is actually running. Confidential computing exists to close that gap, and new research from ABI Research shows the technology has matured enough, across CPUs, GPUs, containers, and now agentic AI, to become a foundational requirement for enterprises building sovereign AI infrastructure: computing environments they control end to end rather than simply rent. Protection is Moving From CPUs to GPUs ABI Research finds that CPU-based confidential computing is closest to mass-market adoption, the product of years spent hardening chip-level isolation for general workloads. GPU-based confidential computing is now generating the strongest momentum, and for good reason. It addresses one of the largest unresolved gaps in AI security: protecting data and models while inf...