Why Autonomous Is More Than A Keyword In Data Storage

Paul Speciale, Chief Technology Evangelist at Scality.

Global data volume and storage needs continue to rise at a rapid rate. By 2030, the volume is expected to surpass 700 zetabytes, according to IDC’s Global DataSphere analysis. Fueled by AI workloads, unstructured data and more edge computing data, the storage market will experience similar robust growth. The global software-defined storage (SDS) platforms and applications market is projected to grow from $28 billion in 2024 to more than $107.7 billion by 2030, according to Research and Markets.

The increase in data flow and storage capacity demand has prompted more enterprises to use autonomous data management to better control workflows and preserve staff time for other high-value tasks. Coupled with AI agents, it’s a strategy that can help create a more workable approach to managing data volume.​​

​The Rise Of Autonomous Data Management

Autonomous data management is not an entirely new concept. Database management systems have long automated tasks such as storing, retrieving and updating data. AI and agentic AI now extend that concept, allowing systems to engage in more complex data-management tasks with human direction and oversight.

The exponential increase of data requires taking AI and agentic AI to a higher, more integrated level of support. Viewed holistically, enterprises can improve overall operations. For example, autonomous AI can help keep customer financial data current, retrievable and compliant while flagging anomalous access requests for human review. Multiply this by terabytes of data flowing through organizations daily, and it’s evident the data cannot be handled by humans alone, nor can it be securely organized, stored and retrieved without the help of AI processing.​​

Object Autonomy

Humans And ROI

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