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Data architecture definition Data architecture describes the structure of an organizations logical and physical data assets, and data management resources, according to The Open Group Architecture Framework (TOGAF). An organizations data architecture is the purview of data architects. Cloud storage.
To succeed, Operational AI requires a modern data architecture. These advanced architectures offer the flexibility and visibility needed to simplify data access across the organization, break down silos, and make data more understandable and actionable.
The evolution of cloud-first strategies, real-time integration and AI-driven automation has set a new benchmark for data systems and heightened concerns over data privacy, regulatory compliance and ethical AI governance demand advanced solutions that are both robust and adaptive.
Nobody wants to waste money on cloud services. But by failing to fully address a handful of basic issues, many IT leaders squander funds on cloud services that could be used to support other important projects and initiatives especially as AI comes along to alter the cloud economics equation.
Increasingly, enterprises are leveraging cloud data lakes as the platform used to store data for analytics, combined with various compute engines for processing that data. Read this paper to learn about: The value of cloud data lakes as the new system of record.
In todays fast-paced digital landscape, the cloud has emerged as a cornerstone of modern business infrastructure, offering unparalleled scalability, agility, and cost-efficiency. As organizations increasingly migrate to the cloud, however, CIOs face the daunting challenge of navigating a complex and rapidly evolving cloud ecosystem.
However, trade along the Silk Road was not just a matter of distance; it was shaped by numerous constraints much like todays data movement in cloud environments. Merchants had to navigate complex toll systems imposed by regional rulers, much as cloud providers impose egress fees that make it costly to move data between platforms.
Accenture reports that the top three sources of technical debt are enterprise applications, AI, and enterprise architecture. What CIOs can do: Avoid and reduce data debt by incorporating data governance and analytics responsibilities in agile data teams , implementing data observability , and developing data quality metrics.
Zero Trust architecture was created to solve the limitations of legacy security architectures. It’s the opposite of a firewall and VPN architecture, where once on the corporate network everyone and everything is trusted. In today’s digital age, cybersecurity is no longer an option but a necessity.
Speaker: Jeremiah Morrow, Nicolò Bidotti, and Achille Barbieri
Yet they are continually challenged with providing access to all of their data across business units, regions, and cloud environments. In this session, you will learn: How the silos development led to challenges with data growth, data quality, data sharing, and data governance (an example of datamesh paradigm adoption).
With the rapid advancement and deployment of AI technologies comes a threat as inclusion has surpassed many organizations governance policies. Governance is also seen as a roadblock to the agility needed to quickly deploy into production. Leaving 55% saying that their organization had not yet implemented an AI governance framework.
One of the most significant enablers of digital transformation is cloud computing. Strategic options for cloud adoption When it comes to cloud adoption, organizations have several strategic options to consider. Public cloud. Private cloud. Hybrid cloud. Multi-cloud.
Data centers this year will face several challenges as the demand for artificial intelligence introduces an evolution in AI hardware, on-premises and cloud-based strategies for training and inference, and innovations in power distributionsall while opposition to new data center developments continues to grow.
Skills in architecture are also in high demand, as power-hungry AI systems require rethinking of data center design. Additionally, the industry is looking for workers with knowledge of cloudarchitecture and engineering, data analytics, management, and governance skills.
To address this, a next-gen cloud data lake architecture has emerged that brings together the best attributes of the data warehouse and the data lake. This new open data architecture is built to maximize data access with minimal data movement and no data copies.
Migration to the cloud, data valorization, and development of e-commerce are areas where rubber sole manufacturer Vibram has transformed its business as it opens up to new markets. Much of this growth is driven by investments in AI technologies, and IDC also expects cloud infrastructure spend to increase 26% compared to 2023.
Jenga builder: Enterprise architects piece together both reusable and replaceable components and solutions enabling responsive (adaptable, resilient) architectures that accelerate time-to-market without disrupting other components or the architecture overall (e.g. compromising quality, structure, integrity, goals).
At Cloud Next 2025, Google announced several updates that could help CIOs adopt and scale agents while reducing integration complexity and costs. This strategy could make it a real differentiator for CIOs who want agents that are interoperable, observable, and enterprise-governed, not just chatbots bolted onto SaaS , Hinchcliffe added.
The proposed model illustrates the data management practice through five functional pillars: Data platform; data engineering; analytics and reporting; data science and AI; and data governance. The choice of vendors should align with the broader cloud or on-premises strategy. However, this landscape is rapidly evolving.
Yet, as transformative as GenAI can be, unlocking its full potential requires more than enthusiasm—it demands a strong foundation in data management, infrastructure flexibility, and governance. Trusted, Governed Data The output of any GenAI tool is entirely reliant on the data it’s given.
The core of their problem is applying AI technology to the data they already have, whether in the cloud, on their premises, or more likely both. And all of that data is stored on premises, but your training is taking place on the cloud where your GPUs live. Imagine that you’re a data engineer. How did we achieve this level of trust?
AI and machine learning are poised to drive innovation across multiple sectors, particularly government, healthcare, and finance. Data sovereignty and the development of local cloud infrastructure will remain top priorities in the region, driven by national strategies aimed at ensuring data security and compliance.
IBM is offering expanded access to Nvidia GPUs on IBM Cloud to help enterprise customers advance their AI implementations, including large language model (LLM) training. IBM Cloud users can now access Nvidia H100 Tensor Core GPU instances in virtual private cloud and managed Red Hat OpenShift environments.
With more and more businesses moving to the Cloud, FinOps is becoming a vital framework for efficiently controlling Cloud expenses. Given that SaaS accounts for a sizable amount of Cloud expenses for businesses of all kinds, including small and medium-sized firms, this addition is essential.
These strategies, such as investing in AI-powered cleansing tools and adopting federated governance models, not only address the current data quality challenges but also pave the way for improved decision-making, operational efficiency and customer satisfaction. When customer records are duplicated or incomplete, personalization fails.
Your cloud usage continues to grow. Your cloudgovernance must match this new reality. The types of workloads you’re migrating are trending increasingly mission-critical.
There is a pending concern about how to manage AI agents in the cloud, says Dave McCarthy, research vice president at IDC, noting that the expanding availability of AI agents from startups and established vendors will give CIOs asset management, security, and versioning challenges.
AI and Machine Learning will drive innovation across the government, healthcare, and banking/financial services sectors, strongly focusing on generative AI and ethical regulation. Data sovereignty and local cloud infrastructure will remain priorities, supported by national cloud strategies, particularly in the GCC.
The first is migrating data and workloads off of legacy platforms entirely and rehosting them in new environments, like the public cloud. This is a way to reap the benefits of cloud migration without having to overhaul all existing workloads. Your data governance procedures must change accordingly.
Broadcoms decisions to replace perpetual VMware software licenses with subscriptions and to eliminate point products in favor of an expensive bundle of private cloud tools are driving longtime VMware customers to look for an exit strategy. For customers looking elsewhere, theres no shortage of alternatives. Lets talk about risk reduction.
Artificial intelligence (AI) has rapidly shifted from buzz to business necessity over the past yearsomething Zscaler has seen firsthand while pioneering AI-powered solutions and tracking enterprise AI/ML activity in the worlds largest security cloud. Here are the notable findings: 1.
Enterprise architecture definition Enterprise architecture (EA) is the practice of analyzing, designing, planning, and implementing enterprise analysis to successfully execute on business strategies. Making it easier to evaluate existing architecture against long-term goals.
The World Economic Forum shares some risks with AI agents , including improving transparency, establishing ethical guidelines, prioritizing data governance, improving security, and increasing education. Placing an AI bet on marketing is often a force multiplier as it can drive data governance and security investments.
To overcome this, many CIOs originally adopted enterprise data platforms (EDPs)—centralized cloud solutions that delivered insights quickly, securely, and reliably across various business units and geographies. When evaluating options, prioritize platforms that facilitate data democratization through low-code or no-code architectures.
VMware by Broadcom has unveiled a new networking architecture that it says will improve the performance and security of distributed artificial intelligence (AI) — using AI and machine learning (ML) to do so. For today’s CIO, they need to understand that AI will be the biggest disruptor to networks since the cloud,” he said. “In
Initially, I would expect most AI workloads will be in the public cloud, as opposed to on premise, given the high cost and potentially low utilization of AI infrastructure in private data centers, says Fung. In addition, governments and tier one telecom operators are getting involved in data center expansion, making it a long-term trend.
Effective cost management in the cloud is, therefore, becoming increasingly important. Yet many companies still find it difficult to keep an eye on the costs of their cloud deployment and to continuously optimize them. In this context, more than a quarter expect cloud costs to rise by 20% or more.
Alan Qi, President of Huawei Cloud Middle East and Central Asia, stated that Huawei Cloud is transforming how enterprises harness AI.He Huawei Cloud combines Cloud for AI and AI for Cloud, leveraging cloud infrastructure to build AI model capabilities and utilizing AI to enhance cloud development processes.
So as a CIO, how should you reign in the chaos and implement a suitable level of governance and control? This change affects the entire IT architectural stack and impacts everything youre currently doing from business transformation to digital transformation and more. Todays challenge is perhaps far greater.
Its not unusual [for organizations] to take six months to get to a yes, or worse yet, get to a no, he says, because their governance processes arent guardrails but rather governing by committee. Using AI is a faster way to do things, from writing code to having gen AI test and deploy, he says.
NIMs are pre-built microservices that simplify the deployment of AI models including inference engines like Triton Inference Server, TensorRT, TensorRT-LLM and PyTorch, according to Nvidia and are aimed at accelerating AI inference development from the cloud to the edge. With increased memory bandwidth (1.4x
For instance, Capital One successfully transitioned from mainframe systems to a cloud-first strategy by gradually migrating critical applications to Amazon Web Services (AWS). It adopted a microservices architecture to decouple legacy components, allowing for incremental updates without disrupting the entire system.
CIOs must take an active role in educating their C-suite counterparts about the strategic applications of technologies like, for example, artificial intelligence, augmented reality, blockchain, and cloud computing. As the CTO at Microsoft Cloud Technologies in the Netherlands, he guided enterprise clients in maximizing the Microsoft platform.
As a networking and security strategy, zero trust stands in stark contrast to traditional, network-centric, perimeter-based architectures built with firewalls and VPNs, which involve excessive permissions and increase cyber risk. The main point is this: you cannot do zero trust with firewall- and VPN-centric architectures.
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