03/25/2024 
2 minutes
2 minutes
03/25/2024 
As artificial intelligence (AI) and machine learning (ML) workloads begin to permeate and drive business processes and decision-making at all levels, effective data management is becoming imperative. Data needs to be stored for a long time and it needs to be available to be actively accessed during its lifespan.
Active archive solutions are ideal for managing information for AI and ML frameworks as they can provide customized optimization for storage, security, and performance. In addition, AI tools can be applied in an active archive to automate and streamline data management processes in various ways. For example, AI can cleanse, normalize, and categorize long-term data for AI workloads, automate metadata tagging and indexing for inactive data, and identify and archive sensitive information.
A new eBook from ESG Research, sponsored by the Active Archive Alliance, explores the general state of data archiving and the benefits and challenges shaping modern environments and strategies. It also examines how active archiving integrates into modern data archiving practices and the role it plays in determining the success of AI/ML initiatives.
Key findings include:
Ultimately, AI depends on well-organized data for success, which underscores why effective data management through an active archive is crucial for an AI future. Organizations without intelligent data management processes that feed into business intelligence workloads risk being left behind by competitors who do.
For more information, access the eBook here: Impact of AI/ML on Active Archiving.
