A blog post by Stelia’s CEO & Founder, Tobias Hooton
As we enter the era of AI and large-scale data management becomes a competitive differentiator for all businesses, it is essential for technology leaders to understand the difference between data movement and data mobility. You could say the chess board has been kicked over and new games with new rules are being created.
“We believe data represents the strongest long-term competitive moat in the AI arms race,” Fred Havemeyer, Macquarie Senior Enterprise Software analyst, wrote in a July 20 client note.
Data movement refers to the simple act of transferring data from one location to another, often in a point-to-point manner. This is how the Internet currently operates. While data movement is necessary, it does not address the broader challenges of managing and accessing massive datasets across diverse environments and systems and in near real-time.
Data mobility, on the other hand, encompasses the ability to move, access, and utilize huge data flows across different platforms, environments, and storage tiers seamlessly and efficiently. It involves the intelligent orchestration of data, ensuring that the right data is available at the right time and in the right place, for example – to support emerging AI workloads. This is how private AI networks of the future will operate, led by forward-thinking ecosystems of builders and developers.
AI and ML, Storage Technologies, VFX/Media, Frequency Trading, Edge Computing, Healthcare, Robotics, IoT, Digital Twinning, and Research and Life Sciences are just some examples of specific use cases for data mobility.
| AI & ML: Real-time model training and inference: Data mobility enables AI models to be trained on massive, distributed datasets and deployed instantly across multiple environments for real-time inference. | Collaborative research and development: Scientists can seamlessly share and access large AI datasets across institutions, accelerating the pace of innovation and discovery. |
| STORAGE TECHNOLOGIES: Intelligent data tiering: Data mobility allows for the automatic movement of data between hot, warm, and cold storage tiers based on usage patterns and business requirements. | Seamless data migration: Organizations can effortlessly migrate massive datasets between on-premises storage and cloud platforms without disrupting ongoing operations. |
| VFX/MEDIA: Collaborative video editing: Data mobility enables video editors to work on the same high-resolution footage simultaneously from different locations, streamlining the post-production process. | Rendering and animation: Studios can leverage data mobility to distribute rendering and animation workloads across multiple environments, significantly reducing processing times. |
| FREQUENCY TRADING: Low-latency data access: Data mobility ensures that trading algorithms have near-instantaneous access to market data across multiple exchanges, enabling split-second trading decisions. | Real-time risk analysis: Financial institutions can harness data mobility to perform real-time risk analysis on massive, distributed datasets, mitigating potential losses. |
| EDGE COMPUTING: Autonomous vehicle data processing: Data mobility allows autonomous vehicles to process and analyze massive sensor data across edge nodes and cloud platforms in real-time, enabling safe and efficient navigation. | Smart city optimization: Cities can leverage data mobility to collect, process, and analyze data from millions of IoT de vices in real-time, optimizing services like traffic management and energy distribution. |
| HEALTHCARE: Personalized medicine: Data mobility enables healthcare providers to access and analyze patient data from multiple sources, facilitating the development of personalized treatment plans. | Real-time patient monitoring: Hospitals can leverage data mobility to monitor patients’ vital signs and medical device data across multiple environments, enabling proactive care and early intervention. |
| ROBOTICS: Autonomous robot navigation: Data mobility allows robots to process and analyze sensor data from multiple sources in real-time, enabling precise navigation and obstacle avoidance. | Collaborative robotics: Manufacturers can harness data mobility to enable seamless communication and coordination between robots and human workers, optimizing production processes. |
| IoT: Predictive maintenance: Data mobility enables manufacturers to collect and analyze sensor data from thousands of machines in real-time, predicting and preventing equipment failures. | Supply chain optimization: Retailers can leverage data mobility to track and optimize inventory levels and shipments across multiple locations, improving efficiency and reducing costs. |
| DIGITAL TWINNING: Real-time asset monitoring: Data mobility allows organizations to create and update digital twins of physical assets in real-time, enabling proactive maintenance and optimization. | Simulation and scenario planning: Enterprises can harness data mobility to run complex simulations and scenario planning on digital twins, optimizing processes and reducing risks. |
| RESEARCH AND LIFE SCIENCES: Drug discovery and development: Data mobility enables pharmaceutical companies to analyze massive datasets from multiple sources, accelerating the identification of new drug candidates. | Genomics and precision medicine: Researchers can leverage data mobility to process and analyze vast amounts of genomic data across multiple platforms, advancing the development of precision medicine. |
The AI Data Mobility Challenge: Limitations and Opportunities
Although barely begun, already the era of AI has transformed the business landscape, offering organisations unprecedented opportunities to innovate, optimise, and grow. However, as AI applications become more sophisticated and data-intensive, companies now face a novel set of challenges:
1 – Data Deluge: AI workloads require moving, processing and analysing massive volumes of data, increasingly in real-time. Legacy network topologies struggle to keep pace, creating bottlenecks and hindering performance. As Krishna Subramanian, co-founder of Komprise, states, “Unstructured data is expensive to store, protect and manage due to its sheer volume and pace of growth.”
2 – Distributed Data: AI data is often generated and stored across geographically dispersed locations, from edge devices to cloud data centres and specialised GPUaaS providers. Efficiently moving and processing this data is critical for AI success. According to IDC, the global datasphere is expected to grow from 33 zettabytes in 2018 to 175 zettabytes by 2025, emphasising the need for scalable data mobility solutions.
3 – Integration Complexity: Deploying AI solutions requires seamless integration with existing IT systems and infrastructure. This can be a daunting task, especially for organizations with complex, heterogeneous environments.
4 – Scalability and Flexibility: As AI initiatives evolve, businesses need the ability to quickly scale their data mobility capabilities up or down based on changing requirements. Traditional solutions lack the agility to adapt to these dynamic needs.
Aging infrastructures are ill-equipped to address these challenges as they lack the technology to deliver AI-centric capability. As datasets trend towards petabytes, the demands of realtime processing for IoT and 6G, and endpoints and processing platforms proliferate, the need for an entirely new data mobility topology comes rapidly into focus.
To overcome limitations and seize the opportunities presented by AI, organisations will demand advanced code-driven data orchestration platforms that can handle the complexities of distributed data management and provide seamless integration with AI technologies. As Subramanian notes, “Unstructured data is increasingly in motion through its lifecycle to less expensive storage and backup options and to data lakes and analytics applications. You need a strategy to manage that ongoing mobility.”
A future article will discuss how Stelia addresses the data mobility challenge in the era of AI.
