A difficult set of choices must be made by CIOs and IT infrastructure directors over which workloads should move from data centers to the cloud and which should remain on-premise. Contextual intelligence from professionals combined with data-driven insights from generative AI and machine learning (ML) can be beneficial.
The most dependable outcomes are obtained by creating cloud migration plans that incorporate the recommendations made by Gen AI with contextual information from seasoned IT professionals. The quality of the infrastructure enabling generation AI and machine learning (ML) will determine how well it can improve data center performance. VentureBeat speaks with CIOs who express heightened pressure to complete more data center consolidation on a smaller team and on a lower budget while maintaining high performance.
Nvidia seizes the opportunity in datacenters.
Nvidia seized on the data center market early because CIOs and their teams are short-handed and require technology that can safely expand with varying demand levels while offering performance increases and lowering costs. The company’s data center strategy is focused on reducing operational costs while maintaining performance and sustainability.
With 56% of revenue in FY23 coming from data centers, NVIDIA’s largest business today. Nvidia announced $7.19 billion in quarterly sales for the first quarter of Fiscal 2024, a 19% increase over the previous quarter. With sales from data centers reaching a record $4.28 billion in the first quarter, up 18% from the previous quarter and 14% from a year ago, Nvidia is clearly seeing a strong demand from businesses looking to use AI and ML-based solutions to boost performance.
The objective is to maximize the value of data centers.
For IT executives, figuring out novel approaches to lower data center expenses without sacrificing performance is crucial. CIOs claim that their boards of directors are moving toward a more operational expense (OPEX) oriented approach, which is typical of a cloud-centric infrastructure, and are holding back on capital expense (CAPEX) investment for new data center expansions.
Financial sector CIOs agree that workloads involving the most sensitive financial data, such as those involving local federal banks, have to remain on-site. Compared to transferring the workloads to the cloud, it is frequently less costly. For their AI workloads and projects, 44% of financial services companies use a hybrid infrastructure, according to an Nvidia analysis.
Finding ways to increase sustainability through better data center operations is one such tactic. CIOs tell VentureBeat that it helps to tie data center modernization to corporation-wide goals, as the CEO and other senior management team members see their whole incentive schemes linked to environmental, social, and corporate governance (ESG) strategies.
Pursuing projects related to sustainability.
Acquiring funding for sustainability projects swiftly emerges as a crucial component of every CIO’s data center cost-cutting plan as they adapt to growing energy expenses, supplier shortages, and unstable economic times. Reducing surplus electricity, investing in renewable energy and postponing replacement cycles are key for reaching this aim.
CIOs might shut unneeded buildings and combine data centers with the use of cloud or colocation services. In order to draw new data center enterprises, public cloud and colocation providers give priority to clean energy and sustainable computing.
According to a recent study by Gartner, companies may save up to 60% on costs by extending the life of servers from three to five years through sustainability-based efforts. Businesses may increase server usage, storage capacity, visibility, and control over operational expenses by using AI, gen AI, and ML approaches to examine real-time server data.
It’s challenging to migrate to the cloud correctly.
It’s difficult to create the ideal business case and technological roadmaps for cloud migration. According to CIOs who spoke with VentureBeat, it’s frequently an iterative process. They also suggest considering it in the context of a business’s digital transformation rather than just as a way to save costs.
More than 95% of new digital workloads, up from 30% in 2021, would be delivered on cloud-native platforms by 2025, according to Gartner’s prediction.
“There is no business strategy without a cloud strategy,” stated Milind Govekar, Gartner distinguished VP analyst. Anything that is not cloud-native will be viewed as legacy, and new workloads delivered in cloud-native environments will be ubiquitous, not simply popular.
The process of organizing and carrying out the transfer of workloads or applications from on-premises infrastructure to external cloud services, or between several external cloud services, is known as cloud migration, according to Gartner. Applications are rehosted, at the very least (mostly transferred exactly as-is to public cloud infrastructure). Nevertheless, they should preferably be updated by rewriting or reworking, or they may even be replaced by software as a service (SaaS).
IT teams may get assistance with the migration process and continuous management of cloud workloads by utilizing AWS Migration Hub, Google Cloud Migration, and Microsoft Azure Migrate. In order to identify servers and workloads that are ideally suited for Azure, Azure Migrate offers a business case builder tool with step-by-step instructions that provide comparisons between on-premises and Azure total cost of ownership, year-over-year cash flow analysis, and resource utilization-based insights.
To be successful, cloud migration roadmaps must have a clear goal.
According to C-level executives who have spearheaded effective cloud migration initiatives, VentureBeat should have a long-term outlook and factor in the possibility that the process may take up to twice as long as first projected.
Strong aversion to change is the cause. According to CIOs speaking with VentureBeat, it’s critical to be absolutely clear about the objectives of each individual roadmap, the new business activities it supports, and whether or not cost reduction is one of those objectives.
For cloud migration roadmaps to effectively offload workloads that impede data center performance, they must address five critical areas. Prior to migration, they include evaluating processes and the effects they will have on performance, cost, and security. Second, in order to optimize workloads, IT, operations, and the CIO’s office must determine which cloud platform provider or providers make the most sense for the workloads at hand, as well as migration strategies (ranging from rehosting to replacing), implementation plans that minimize disruptions, and ways for IT to continuously monitor and modify cloud resources.
The success or failure of a cloud migration may be attributed to five prevalent migration methodologies. Rehost, often known as “lift and shift,” is the first, according to Gartner. It entails transferring an application from one platform or IT environment to another.
Replatforming, sometimes known as “lift and reshape,” is the process of changing an application’s architecture without sacrificing its essential features. Rebuild is the process of completely rewriting or rebuilding an application, whereas rearchitect refers to restructuring or reengineering an application’s architecture. Lastly, the term “replace” describes “dropping” or “shopping” for a new solution in place of repurchasing the old one.
Assessing outcomes.
The business model that each CIO and their team are attempting to improve, legacy system integration workloads, restrictions on moving particular systems, budgets, and teams available to work on the project will all influence the metrics and KPIs that each team decides to track as part of a cloud migration strategy.
Most cloud migrations share a basic set of indicators, according to the CIOs VentureBeat has talked with. Enhancing the user experience through increased system reactivity, dependability, and scalability to accommodate erratic resource demands is the most crucial component.
Application performance comes in second, and maintaining accurate performance baseline comparisons through real-time monitoring comes in third. Service-to-service latency, server performance (which might reveal undiscovered hidden effects), error rates, and response time valuations are further measures.
The phenomenon of reparation of cloud workloads occurs when cloud migration tactics fail to produce the anticipated cost or performance improvements. The highest-risk industries, such as banking, insurance, and financial services, now often have a repatriation strategy in place. These businesses need to have a backup plan ready in case cloud migration encounters unforeseen delays or issues.
What will the post big data era look like?
With the continuous growth of data volume, optimizing data efficiency will become a long-term challenge faced by enterprises and organizations. Enterprises need to constantly innovate and improve to adapt to the constantly changing business environment and customer needs.
The development of data security requires joint efforts from governments, enterprises, and individuals.
The optimization of data efficiency is not only a technical issue, but also requires enterprises to establish a good data governance mechanism to ensure the security, compliance, and availability of data.
The development of data technology will change our way of life and social structure.
Optimizing data efficiency requires comprehensive consideration of multiple aspects such as technology, processes, and personnel. Enterprises need to adopt advanced technologies and tools, optimize data processes, improve data quality, and cultivate employee data awareness and skills.
In today’s data-driven business environment, optimizing data efficiency is crucial. Enterprises need to ensure high data quality, strong accuracy, and the ability to quickly obtain and analyze data to support decision-making.
The optimization of data efficiency is one of the key factors for enterprises and organizations to achieve success in the digital age. By optimizing data efficiency, enterprises can improve operational efficiency, reduce costs, and better meet customer needs.