The data storage and unstructured data management sector has undergone significant changes in the past year, with companies focusing on cloud storage strategies due to rising prices and increased pressure on IT budgets.
The market is tumultuous. Generative AI requires new demands for data governance and storage, data migrations are becoming more complex yet more necessary in an era of data center consolidation. How will organizations overcome these challenges, reducing costs, and providing more value to data?
1.- AI will enrich unstructured data for better outcomes
With the advancement of more accessible artificial intelligence tools, the quest for value in unstructured data has increased. Despite current challenges, there is an expected surge in demand for solutions that simplify the search, enrichment, and efficient verification of this data. These solutions not only save time but also enhance accuracy, driving the effectiveness and success of AI projects across various applications and sectors.
2.- Shifting from a Cloud-First to Data-First Approach
Cloud-centric strategies, popularized during the pandemic, are being questioned. Organizations have embraced flexible hybrid and multi-cloud approaches, catering to diverse workloads, but the perception is that the cost-saving purportedly offered by the cloud is now not as significant. Instead, organizations have found that retaining data internally can be more cost-effective. Consequently, there is now greater value placed on the flexibility to move data from one storage to another as requirements change and technologies advance.
3.- Unstructured Data Migrations Become Smarter and Automated
Traditional enterprise data migrations, historically complex and reliant on extensive professional services, are undergoing a transformation thanks to automation and artificial intelligence. These innovations enable smarter, more efficient, and adaptive migrations, eliminating the need for constant supervision by IT administrators.
Advanced tools for planning migrations, driven by AI, offer optimal storage recommendations and dynamically respond to customer environment challenges. This evolution aims to meet the growing business demand for faster migrations, lasting results, and significant reduction in data loss, errors, and security risks.
4.- New Employment Opportunities
In the data storage landscape, IT teams will need to acquire additional skills to adapt to emerging trends. The term FinOps will integrate into the storage architects' jargon, emphasizing the importance of managing providers, contracts, and delivering efficient and secure data services.
With a growing focus on software and services, hardware management becomes less relevant.
The transition to multi-vendor environments requires storage professionals to broaden their knowledge in areas such as networking, security, cloud architecture, and data analytics. Additionally, specific storage roles are expected to evolve into titles like "data insights engineer" or "data management architect," highlighting the convergence between storage and artificial intelligence in IT infrastructure.
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