Replicating data is not multiple sources of truth in the age of AI and Cloud
Strategic Data Replication is not Many Sources of Truth, and can be complimentary to a Single Source of Truth architectural design. Even in a highly regulated Healthcare data ecosystem, strategic replication can maintain a compliant source of truth as part of a larger strategy to scale and control costs.
The concepts of single source of truth, data replication, data redundancy, and data duplication are often convoluted and can lead to misunderstandings that result in poorly performing and unnecessarily expensive architectures. I've published a new video that attempts to unwind the problem. No matter what data tools or cloud platforms you work with, hopefully you can find value in the content. I've added some fun analogies to data replication for cryptocurrency (such as Bitcoin) and Biology (DNA) to help explain the benefits of data replication in a multi-cloud world of analytics and AI.
How can you plan for a highly scalable, cost optimized analytics and AI architecture with high query concurrency, global users, and rapidly evolving technology having an AI Agentic future? How did Kimball vs Inmon influence the single source of truth discussion? How do you have a multi-tool environment with both corporate reporting and self-service tools that provide consistent metrics? How can you prevent the self-service spreadmarts and shadow IT nightmares of the past without stifling innovation and progress? Hopefully the content in the video below helps explain the history and frames up the future:
Microsoft