According to a recent, global Recon Analytics survey commissioned by Seagate Technology, business leaders from across 15 industry sectors and 10 countries expect that adoption of artificial intelligence (AI) applications will generate unprecedented volumes of data (AI-Driven Data) driving a boom in demand for data storage, in particular cloud-based storage.
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With hard drives delivering scalability relative to terabyte-per-dollar cost efficiencies, cloud service providers rely on hard drives to store mass quantities of data. Recently, analyst firm IDC estimated that 89% of data stored by leading cloud service providers is stored on hard drives. Now, according to this Recon Analytics study, nearly two-thirds of respondents (61%) from companies that use cloud as their leading storage medium expect their cloud-based storage to grow by more than 100% over the next 3 years.
“The survey results generally point to a coming surge in demand for data storage, with hard drives emerging as the clear winner,” remarked Roger Entner, founder and lead analyst of Recon Analytics. “When you consider that the business leaders we surveyed intend to store more and more of this AI-driven data in the cloud, it appears that cloud services are well-positioned to ride a second growth wave.”
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Key Findings:
- 72% surveyed say they use AI today.
- 61% of respondents who predominately use cloud storage say their cloud-based storage will increase by over 100% over the next 3 years.
- Storage ranks as the second most important component of AI infrastructure, with Security ranking #1.
o In order of importance: Security, Storage, Data Management, Network Capacity, Compute, Regulations, LLM Viability, Energy
- Of those businesses who’ve adopted AI technology, 90% believe longer data retention improves the quality of AI outcomes.
- 88% of respondents who use AI today believe adoption of Trustworthy AI requires an increased need to store more data for longer periods of time.
Survey results substantiate a trend toward organizations retaining data for longer periods of time to ensure data integrity needed to optimally train AI models. In order for AI innovation and adoption to continue, AI results and outcomes need to become increasingly accurate.
