Artificial intelligence (AI) is driving a shift in data strategies for organizations. Initially, many companies turn to the cloud for AI projects due to easy access to computing resources and cost savings. However, as AI initiatives move from testing to production, the dynamics change.
AI’s Impact on Data Strategies
AI drives organizations to rethink their data strategies. While cloud environments offer flexibility for launching AI projects, long-term resource requirements can make on-premises solutions more cost-effective. Additionally, performance considerations, such as low latency needs, often favor on-premises setups for critical AI workloads.
Concerns around security and governance, especially in regulated industries, further push organizations towards on-premises solutions for better control and compliance.
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Advantages of On-Prem Hardware
Many organizations are looking to repatriate their data due to the benefits of on-premises data governance. Keeping sensitive data on-site enhances visibility, control, and security. However, this shift comes with risks that need to be carefully managed.
Public cloud platforms offer built-in protections that may not transfer seamlessly back on-premises, requiring organizations to reevaluate their backup, recovery, and cyber resilience strategies. Advanced approaches like immutable backups and orchestrated recovery models are gaining traction to support critical AI workloads.
