The transformative impact of artificial intelligence (AI) on industries worldwide is undeniable. From enhancing productivity and efficiency to revolutionizing customer experiences, AI has quickly become a cornerstone of modern business strategies. However, the unprecedented growth of AI technologies has brought about a corresponding surge in data volumes and complexities, posing significant challenges for data management infrastructure. As AI tools continue to evolve, so will data storage requirements, and AI pipelines will continue to change. To leverage AI, organizations must have the right supporting data management infrastructure to support AI initiatives and unlock the most value in their data now and in the future.
What AI Needs
For AI to be effective, requires massive amounts of data to be trained on and to learn. Stored data worldwide is expected to exceed 180 zettabytes by next year, and in one survey, 80% of respondents said they already have between 51 terabytes and 3 petabytes stored. Whether that data is used for training machine learning models, analyzing real-time interactions, or powering predictive algorithms, AI relies on access to diverse and extensive datasets. As organizations strive to harness the full potential of AI, they have no choice but to confront the daunting task of storing, managing, and accessing this ever-expanding pool of data. From structured databases to unstructured text, images, and sensor data, the data management infrastructure must accommodate and deliver high performance while ensuring scalability and performance, all at a reasonable cost.
The lifecycle of AI data also extends beyond initial creation and storage. As AI models evolve and adapt, they generate additional data through ongoing training, feedback loops, and iterative improvements. This continuous data generation presents a unique challenge for storage systems, as they must seamlessly and continuously accommodate new data while maintaining accessibility and integrity to all data for analysis and repurposing. Without a data management infrastructure solution capable of handling this dynamic movement of data across the lifecycle, organizations risk bottlenecks, inconsistencies, and missed opportunities for insights.
Storage Challenges
Managing data across the AI continuum is inherently complex, requiring organizations to navigate myriad technical and logistical challenges. Traditional data management infrastructure solutions may struggle to keep pace with the dynamic nature of AI pipelines. Configuring and optimizing data infrastructure systems for AI applications can be time-consuming and resource-intensive, requiring specialized expertise and constant monitoring.
Moreover, the scalability and performance requirements of AI workloads further compound the challenge as organizations grapple with balancing cost-effectiveness with performance optimization. The same survey found that almost half of respondents have deleted data that they should’ve held onto for AI—because they didn’t have the proper insights into the data. Organizations must have a data management infrastructure that not only delivers high performance but also the ability to archive all their unique data for extended periods of time. By retaining their own unique data, organizations can build AI models that are differentiated from their competition.
The Solution? Simple.
In response to the complexities of AI data management, there is a growing emphasis on simplicity and intelligence in data management design. By intelligently—and simply— managing data throughout the AI lifecycle, a straightforward solution empowers organizations to extract maximum value from their data assets while minimizing complexity, cost, and operational overhead. From data ingestion and preprocessing to model training, inference, and feedback loops, a straightforward solution offers seamless integration and orchestration of AI-driven processes.
Modern data management solutions also should prioritize flexibility and scalability, enabling organizations to adapt to evolving AI demands. Hybrid cloud strategies, rather than strictly adhering to one type of storage, offer scalability and allow organizations to seamlessly expand storage capacity as needed, while still getting the benefits of on-prem. These hybrid architectures provide organizations with the flexibility to leverage edge, core, cloud, and data movement resources, while retaining the ability to build a private cloud to optimize performance, cost, and protect against ever-increasing data sovereignty considerations.
A Data Management Solution for Tomorrow
The evolution of AI has ushered in a new era of data-driven innovation, revolutionizing industries and reshaping the way organizations compete and do business. However, the success of AI hinges on having an agile and scalable data management infrastructure that’s capable of supporting the diverse and dynamic requirements of AI pipelines. By embracing simplicity and flexibility in storage design, organizations can unlock the full potential of AI, driving innovation and gaining a competitive edge in an increasingly AI-driven world. By proactively investing in a modern solution, organizations can future-proof their infrastructure and position themselves for success in the AI-powered world of tomorrow.
About the Author
Jordan Winkelman has more than 25 years of experience in a wide range of technical roles supporting some of the largest global advertising agencies, retail marketing and branding firms with enterprise solutions across data management, software, networking, and platform infrastructure. As Quantum’s Field CTO, Jordan works directly with customers and field teams to deliver scalable storage infrastructure and advanced data management solutions to some of the industry’s most vexing challenges.
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