Top data management predictions for 2024
Posted: Sun Jan 19, 2025 5:42 am
The data storage and unstructured data management industry has changed a lot over the past year, and businesses are turning their attention to cloud storage strategies because they are increasing in price and putting more pressure on IT budgets.
The market is in turmoil. Generative AI requires new demands for data governance and storage , data migrations are increasingly complex, but also more necessary in an era of data center consolidation. How will organizations overcome all these challenges, reducing costs and providing more value to data?
1.- AI will enrich unstructured data for better results
With the advancement of more accessible AI tools , the search for value in unstructured data has increased. Despite current challenges, increased demand is expected for solutions that simplify the efficient search, enrichment, and verification of this data. These solutions not only save time but also improve accuracy, boosting the effectiveness and success of AI projects across a variety of applications and industries.
2.- From a cloud-first to data-first approach
Cloud-centric strategies popularized during the pandemic are coming under scrutiny. Organizations have embraced flexible hybrid and multi-cloud approaches, adapting to diverse workloads, but the perception is that the savings the cloud was supposed to offer are no longer as cost-effective. Instead, organizations have found that keeping data in-house can be more co el salvador mobile phone number list st-effective. As a result, the flexibility of being able to move data from one storage to another as requirements change and technologies advance is now more highly valued.
3.- Unstructured data migrations become smarter and more 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 oversight by IT administrators.
Advanced migration planning tools, powered by AI, provide optimal storage recommendations and dynamically respond to customer environment challenges. This evolution aims to meet growing business demand for faster migrations, long-lasting results, and a 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 become part of the jargon of storage architects, highlighting the importance of managing vendors, contracts, and providing efficient and secure data services.
With an increasing focus on software and services, hardware management becomes less relevant. The transition to multi-vendor environments requires storage professionals to expand their knowledge in areas such as networking, security, cloud architecture, and data analytics. Storage-specific roles are also expected to evolve into other titles such as “data skills engineer” or “data management architect,” highlighting the convergence of storage and AI in IT infrastructure.
The market is in turmoil. Generative AI requires new demands for data governance and storage , data migrations are increasingly complex, but also more necessary in an era of data center consolidation. How will organizations overcome all these challenges, reducing costs and providing more value to data?
1.- AI will enrich unstructured data for better results
With the advancement of more accessible AI tools , the search for value in unstructured data has increased. Despite current challenges, increased demand is expected for solutions that simplify the efficient search, enrichment, and verification of this data. These solutions not only save time but also improve accuracy, boosting the effectiveness and success of AI projects across a variety of applications and industries.
2.- From a cloud-first to data-first approach
Cloud-centric strategies popularized during the pandemic are coming under scrutiny. Organizations have embraced flexible hybrid and multi-cloud approaches, adapting to diverse workloads, but the perception is that the savings the cloud was supposed to offer are no longer as cost-effective. Instead, organizations have found that keeping data in-house can be more co el salvador mobile phone number list st-effective. As a result, the flexibility of being able to move data from one storage to another as requirements change and technologies advance is now more highly valued.
3.- Unstructured data migrations become smarter and more 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 oversight by IT administrators.
Advanced migration planning tools, powered by AI, provide optimal storage recommendations and dynamically respond to customer environment challenges. This evolution aims to meet growing business demand for faster migrations, long-lasting results, and a 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 become part of the jargon of storage architects, highlighting the importance of managing vendors, contracts, and providing efficient and secure data services.
With an increasing focus on software and services, hardware management becomes less relevant. The transition to multi-vendor environments requires storage professionals to expand their knowledge in areas such as networking, security, cloud architecture, and data analytics. Storage-specific roles are also expected to evolve into other titles such as “data skills engineer” or “data management architect,” highlighting the convergence of storage and AI in IT infrastructure.