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Navigating AI and Big Data’s Impact
01 Nov., 2024
AI and big data are transforming businesses with strategic and data-driven insights. Explore their impacts and learn about storage solutions for data management at scale.
Widespread adoption of artificial intelligence (AI) and big data technologies has driven transformative change across virtually every industry, changing how organizations generate strategic insights and drive data-driven decision-making.
This rapid rate of adoption isn’t expected to slow down anytime soon. According to Grand View Research, the global market for AI technology is projected to grow at an annual rate of 36.6%, reaching a total global market value of more than $1.8 trillion by 2030.
From small businesses to enterprise corporations, AI and big data are bringing new efficiencies and capabilities to strategy development and day-to-day operations. As a global storage leader, Seagate plays a central role in providing the scalable, reliable, and high-performance data storage systems that businesses need to support AI and big data in their workflows.
Artificial intelligence is an innovative technology capable of performing reasoning, learning, and problem-solving with logical capabilities that simulate human cognition and intelligence.
AI is an umbrella term encompassing many forms of virtual intelligence, including machine learning, generative AI, and natural language processing. Through advanced algorithms, big data and prompt engineering, AI is a revolutionary tool powering new capabilities regarding personalization and automation.
Big data refers to high-volume datasets that are so large and complex they require specialized systems to process, manage, and analyze its information. Big data typically represents datasets coming from a wide range of sources, and the data is unified by its ownership by, or relevance to, a specific organization or entity.
AI, machine learning, and advanced analytics are typically required to effectively process and analyze big data to generate valuable insights from this information.
AI can use several types of data to generate valuable insights that offer strategic value to businesses seeking to elevate performance, optimize productivity, and enable continuous improvement across its teams and operations.
Here are the three types of data everyone should know when using AI for data analytics:
Structured data is processed, organized data that’s easily searchable within a database. Common sources of structured data include customer information, inventory data, transactions, and maintenance logs.
This type of data is the most suitable to deliver strategic insights that guide optimizations and other changes within an organization.
Unstructured data is information that needs to be processed before meaning and insights can be derived from it. Images, video, and certain kinds of text files are common forms of unstructured data.
AI technologies have become an effective tool for analyzing this data at scale, allowing businesses to extract powerful insights from unstructured datasets. For example, AI can monitor security footage and identify anomalies indicating specific behavior. It can also help evaluate customer feedback to automatically contextualize and categorize each individual response.
Big data typically includes both structured and unstructured datasets, all of which must be processed and managed at scale. AI can move across structured and unstructured data to identify patterns in customer behavior. It can also coordinate relationships between structured data trends and unstructured data events to help contextualize information about changes in operations, shopping patterns, supply chain logistics, and many other applications.
As AI technologies are used to process and analyze big data, these two separate entities enjoy mutual benefits from this ongoing relationship.
Big data analysis gives AI models more information to learn and refine their models, improving AI performance over time. Meanwhile, better AI analytic capabilities increase the potential business impact of insights gleaned from big data, delivering more value to your organization.
This symbiotic relationship allows many industries to achieve rapid insight processing that supports data-driven interventions and personalized AI solutions.
Through the consumption and analysis of data, AI itself has become a significant source of data growth through machine learning, automation, and content automation. IoT devices and other AI- enhanced technology all contribute to unprecedented volumes of real-time data that must be managed, analyzed, and stored.
Managing data on this scale requires robust, high-performance storage infrastructure that can meet your current and future storage needs.
AI data analysis relies on a continuous lifecycle that supports ongoing learning and refinement for your AI models. Here’s how AI and big data work together to support ongoing creation and iteration:
AI must have access to diverse, high-quality data sources, including IoT sensors, business software integrations, direct interactions with customers, and a proprietary knowledge base. This information and its access must be effectively managed to ensure data is complete and accurate, and that the data collection process is adhering to privacy and other compliance requirements.
Data preparation and cleaning eliminates anomalies and inaccuracies that undercut the value of those datasets. Common techniques used in this phase of the data flow include deduplication and normalization, among other tactics.
Annotation and labeling are critical when training AI models. Annotation tags data with relevant contextualizing information, such as labeling objects in videos and images, or applying sentiment labels to help the AI better comprehend customer feedback. Over time, consistent annotation and labeling will support better training and more effective AI outcomes.
Efficient, highly scalable storage is required to support efficient data management and data storage for AI workflows. Businesses must build supportive infrastructure using solutions like Seagate Mozaic 3+™, which is designed specifically to serve the unique storage challenges of AI implementations.
AI’s long-term value is based on iterative improvements. A sustainable AI data feedback loop involves a continuous cycle of data consumption, content generation, and performance improvement that all contribute to these iterative improvements. Organizations must have seamless data access for AI technologies to facilitate this virtuous cycle of development.
Demanding AI workflows require a storage infrastructure that’s designed to balance the following requirements:
● High performance for rapid data processing. AI workflows need fast storage performance that offers low latency at scale, especially when using AI to generate real-time insights.
● Scalability to accommodate growing datasets. As data volume increases and AI implementations increase, storage infrastructure must grow seamlessly alongside these services.
● Reliability to secure uninterrupted workflows. Storage performance must be maintained even during peak periods of use and under the strain of growing datasets.
To meet these multifaceted needs, businesses need a diverse ecosystem of memory and storage solutions, utilizing local and network solid-state drives (SSDs), high bandwidth memory (HBM), dynamic random-access memory (DRAM), and network hard drives.
In an AI workflow, storage and compute clusters do not exist as separate entities. They have a synergistic role in the overall performance and optimization of AI workflows. GPUs, CPUs, HBM, DRAM, enterprise SSDs, and enterprise hard drives each serve specific capabilities regarding processing power and data management. Seamless integration of these solutions is critical to maximizing AI performance.
Seagate offers a suite of enterprise storage solutions that optimize capacity and efficiency in your data center, supporting buildout while future-proofing your infrastructure for evolving AI demands and a growing volume of AI workflows. With Mozaic 3+, your business can equip its data center with mass-capacity storage at unprecedented areal densities.
The Seagate Mozaic 3+ solution effortlessly supports the complementary technologies comprising your AI workflow, raising the ceiling for performance, scalability, and reliability.
A synergistic approach to managing computing resources and storage ultimately optimizes the speed, efficiency, energy consumption, and availability of your AI capabilities. When properly implemented, these solutions span the spectrum between performance and scale to maximize the long-term value realization of your AI investments.
The transformative power of AI requires storage infrastructure that breaks barriers and raises storage density and performance to new heights.
Seagate Mozaic 3+ solutions, including Exos® Mozaic 3+ hard drives, achieve this via heat-assisted magnetic recording (HAMR), which enables significant areal density gains that pack data tighter into a smaller, more efficient space—while keeping that data magnetically and thermally steady.
With HAMR, Mozaic 3+ expands the limits of storage density without compromising reliability for that storage—and while still fitting that convenient, familiar 3.5-inch form factor.
Conventional storage infrastructures aren’t equipped to support the rapid growth of AI workflows and big data initiatives. Businesses that want to take advantage of these innovative opportunities must first ensure that they’ve built a storage foundation capable of supporting these initiatives at scale.
Explore Seagate storage solutions for yourself—and discover how Mozaic 3+ can help address your emerging AI and big data needs.
Mozaic 3+™ can help you do exactly that. As a global leader in data storage, Seagate’s leadership brings the expertise and agility needed to navigate and overcome the challenges of exponential data growth.
