Cloud-Based Healthcare Services Returning Onshore: The Argument for Reversing Digital Health Data Migration
In the rapidly evolving world of technology, healthcare IT leaders are faced with a pressing challenge: reducing cloud spending while investing in artificial intelligence (AI). Caitlin Gordon, Vice President of Product Management at Dell Technologies, highlights a common solution - a hybrid or multicloud setup, which involves using multiple vendors in a data center or multiple public clouds for different workloads.
This approach, however, comes with its own set of considerations. Compliance requirements, particularly in healthcare, could impact the design of a company's architecture. For instance, genomic sequencing, a resource-intensive workload, might leverage data on-premises due to stringent compliance or data residency regulations.
Rob Tiffany, Research Director for Cloud and Edge Infrastructure at IDC, sheds light on another challenge. Companies are hesitant to share their Large Language Models (LLMs) with AI vendors, preferring to keep sensitive data in private cloud infrastructure, as recommended by Caitlin Gordon.
To address these challenges, Tiffany suggests a legitimate hybrid cloud infrastructure between public and private clouds, enabling free movement of workloads. In such a setup, traditional Virtual Machines (VMs) or containers like Kubernetes can ensure workload portability. For example, an Microsoft database workload in SQL Server running on-premises could sync data with an Azure SQL Server.
CDW can assist teams in modernizing their on-premises infrastructure using hyperconverged infrastructure and software-defined data centers. Meanwhile, organizations using "private AI" applications are likely turning to on-premises infrastructure.
Tiffany also points towards the future with the "disaggregated model," offering teams more flexibility as they restructure workloads. This model allows for the optimization of workloads by combining secure private and public cloud infrastructures, as seen in the new university hospital in Cottbus, where T-Systems (Deutsche Telekom) utilizes hybrid cloud solutions.
The trend of re-evaluating "cloud first" strategies and moving workloads from the public cloud back on-premises, known as cloud repatriation, is gaining traction. The goal is to reallocate the 21% of cloud infrastructure spending that is typically wasted on underused resources. However, only 8%-9% of organizations intend to implement full workload repatriation, according to IDC's recent Server and Storage Workloads Survey.
Gordon advises scaling compute and storage separately and not locking in to any single vendor. High-performance storage options for AI workloads are available from providers like NetApp and PureStorage. For high-performance storage tailored to AI workloads, T-Systems plans to launch the Industrial AI Cloud in partnership with NVIDIA by 2026.
IBM also offers hybrid cloud consulting and AI-enhanced cloud platforms suited for healthcare workloads. In the realm of AI-focused cloud solutions, T-Systems' Industrial AI Cloud stands out, aiming to provide secure, fast infrastructure designed for AI applications.
In conclusion, navigating the hybrid cloud landscape in healthcare requires careful consideration of workload placement, compliance, and cost-effectiveness. With the right strategies and partnerships, healthcare IT teams can strike a balance between reducing costs, ensuring compliance, and harnessing the power of AI.
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