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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. Ensure security and access controls.
For companies investing in data science, realizing the return on these investments requires embedding AI deeply into business processes. To fully benefit from AI, organizations must take bold steps to accelerate the time to value for these applications. To succeed, Operational AI requires a modern data architecture.
Especially with companies like Microsoft, OpenAI, Meta, Salesforce and others in the news recently with announcements of agentic AI and agent creation tools and capabilities. Microsoft is describing AI agents as the new applications for an AI-powered world. This data would be utilized for different types of application testing.
After all, a low-risk annoyance in a key application can become a sizable boulder when the app requires modernization to support a digital transformation initiative. Accenture reports that the top three sources of technical debt are enterprise applications, AI, and enterprise architecture.
In his best-selling book Patterns of Enterprise ApplicationArchitecture, Martin Fowler famously coined the first law of distributed computing—"Don’t distribute your objects"—implying that working with this style of architecture can be challenging.
For starters, generative AI capabilities will improve how enterprise IT teams deploy and manage their SD-WAN architecture. With AI-driven network management and optimization capabilities, enterprises will be able to prioritize traffic and application performance based on user needs and business requirements, according to IDC.
No company has both driven and benefited from AI advancements more than Nvidia. Last year, Nvidias GTC 2024 grabbed headlines with its introduction of the Blackwell architecture and the DGX systems powered by it. With GTC 2025 right around the corner (it runs March 17- 21 in San Jose, Calif.), You can also expect a focus on edge AI.
Noting that companies pursued bold experiments in 2024 driven by generative AI and other emerging technologies, the research and advisory firm predicts a pivot to realizing value. 75% of firms that build aspirational agentic AI architectures on their own will fail. Forrester Research this week unleashed a slate of predictions for 2025.
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. The company said it has identified a need for more intelligent edge networking and computing.
Companies have historically secured OT systems which include physical security controls, HVAC systems, industrial control systems like factory automation equipment, and medical scanning equipment by air-gapping them. Companies often need new security solutions to protect OT.
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.
The Supermicro JBOF replaces the traditional storage CPU and memory subsystem with the BlueField-3 DPU and runs the storage application on the DPU’s 16 Arm cores. Companies are prioritizing investment in highly configured server clusters for AI, research firm Omdia reports.
Less than 10% of the FTSE 500 companies that existed fifty years ago are still around today and less than half of the companies founded since 2000 are still operating. Company executives are well aware that their businesses need to adapt to keep up with the rapid transformation now taking place.
The goal of the Kyndryl/Google Cloud service is to make it easier for organizations to utilize AI assistance to access and integrate mainframe-based data with cloud-based resources and combine that data with other information to build new applications, the companies stated.
The companys newly unveiled Passage L200 and M1000 platforms use light instead of electricity to move data, potentially unlocking major performance gains for companies running large AI models, the company said in a statement. Lightmatters approach could flatten this architecture.
Its no secret that more modern approaches to remote access have been usurping VPNs as organizations adapt to the realities of a more distributed workforce, increasingly cloud-based applications, and heightened security threats. Its really access to an individual resource or application instead of a whole network segment.
AI factories are specified data centers emphasizing AI applications as opposed to traditional line of business applications like databases and ERP. The architecture aims to optimize deployment speed, performance, resiliency, cost, energy efficiency and scalability for current- and future-generation data centers.
Otherwise, companies will struggle to realize business value with AI/ML capabilities left to endure high cloud cost expenses, as it has been for many companies in 2024 for AI solutions. The assessment provides insights into the current state of architecture and workloads and maps technology needs to the business objectives.
The FortiDLP platform provides automated data movement tracking, cloud application monitoring and endpoint protection mechanisms that work both online and offline. FortiDLP expands Fortinet’s data protection efforts FortiDLP’s architecture includes several key technical components.
The built-in elasticity in serverless computing architecture makes it particularly appealing for unpredictable workloads and amplifies developers productivity by letting developers focus on writing code and optimizing application design industry benchmarks , providing additional justification for this hypothesis. Vendor lock-in.
Our vision is to be the platform of choice for running AI applications, says Puri. The system integrator has the Topaz AI platform, which includes a set of services and solutions to help enterprises build and deploy AI applications. The updated product also has enhanced security features, including LLM guardrails. Red Hat reported $6.5
SAP R/1 was designed as standard software that could be offered to other companies. In 2008, SAP developed the SAP HANA architecture in collaboration with the Hasso Plattner Institute and Stanford University with the goal of analyzing large amounts of data in real-time. In 1979, the successor product, SAP R/2 , was launched.
As 2025 kicked off, I wrote a column about the network vendor landscape specifically, which networking players will step up and put their efforts into finding new applications with solid business benefits that could enable a network transformation. Its not an application, but an applicationarchitecture or model.
Later, as an enterprise architect in consumer-packaged goods, I could no longer realistically contemplate a world where IT could execute mass application portfolio migrations from data centers to cloud and SaaS-based applications and survive the cost, risk and time-to-market implications.
To keep up, IT must be able to rapidly design and deliver applicationarchitectures that not only meet the business needs of the company but also meet data recovery and compliance mandates. It’s a tall order, because as technologies, business needs, and applications change, so must the environments where they are deployed.
Generative AI has seen faster and more widespread adoption than any other technology today, with many companies already seeing ROI and scaling up use cases into wide adoption. The company says it can achieve PhD-level performance in challenging benchmark tests in physics, chemistry, and biology.
It has libraries, APIs, and a set of tools that allow developers to build high-performance applications for data centers supporting network virtualization, storage offload, and high-performance computing (HPC) workloads. Nvidia is not a cybersecurity provider, and doesnt aim to be.
While CIOs understand the crushing weight of technical debt — now costing US companies $2.41 The more strategic concern isn’t just the cost— it’s that technical debt is affecting companies’ abilities to create new business, and saps the means to respond to shifting market conditions.
This means that they have developed an application that shows an advantage over a classical approach though not necessarily one that is fully rolled out and commercially viable at scale. To solve the error problem, quantum computing companies use multiple physical qubits to create one error-corrected qubits.
Amazon Web Services has unveiled a new quantum computing chip, Ocelot , that the company claims could reduce error correction costs by up to 90% compared to traditional approaches. Both companies claim that their two approaches, both still in the very early stages, have the potential to scale better than their competitors.
MIT event, moderated by Lan Guan, CAIO at Accenture Accenture “98% of business leaders say they want to adopt AI, right, but a lot of them just don’t know how to do it,” claimed Guan, who is currently working with a large airliner in Saudi Arabia, a large pharmaceutical company, and a high-tech company to implement generative AI blueprints in-house.
Cisco and Nvidia have expanded their partnership to create their most advanced AI architecture package to date, designed to promote secure enterprise AI networking. Hypershield uses AI to dynamically refine security policies based on application identity and behavior.
Less than 10% of the FTSE 500 companies that existed fifty years ago are still around today and less than half of the companies founded since 2000 are still operating. Company executives are well aware that their businesses need to adapt to keep up with the rapid transformation now taking place.
The demand for AI skills is projected to persistently grow as these technologies become more central to network engineering and architectural roles. In the big picture, networking pros must have the skills to enable the integration of new AI applications with the underlying AI infrastructure.
Which are not longer an architectural fit? For example, a legacy, expensive, and difficult-to-support system runs on proprietary hardware that runs a proprietary operating system, database, and application. The application leverages functionality in the database so it is difficult to decouple the application and database.
Our digital transformation has coincided with the strengthening of the B2C online sales activity and, from an architectural point of view, with a strong migration to the cloud,” says Vibram global DTC director Alessandro Pacetti. Vibram certainly isn’t an isolated case of a company growing its business through tools made available by the CIO.
As part of this expansion, IBM is integrating its watsonx development platform with Nvidia Inference Microservices (NIM) software, the companies announced at Nvidias GTC conference. The idea is to let customers responsibly scale and enhance AI applications like retrieval-augmented generation (RAG) and AI reasoning, IBM stated.
also supports HPEs Data Fabric architecture which aims supply a unified and consistent data layer that allows data access across premises data centers, public clouds, and edge environments with the idea of bringing together a single, logical view of data, regardless of where it resides, according to HPE.
More organizations than ever have adopted some sort of enterprise architecture framework, which provides important rules and structure that connect technology and the business. For example, one of the largest energy companies in the world has embraced TOGAF — to a point.
For decades, businesses have relied on MPLS and SD-WAN to connect branch offices and remote workers to critical applications. Paying a premium to backhaul traffic to a central data center made sense when that was where all applications lived. That worked when everything lived in the corporate data center. Sounds promising, right?
For example, a company could have a best-in-class mainframe system running legacy applications that are homegrown and outdated, he adds. These types of applications can be migrated to modern cloud solutions that require much less IT talent overall and are cheaper and easier to maintain and keep current.”
More and more companies are adopting a multicloud strategy. For years, outsourcing IT processes to the cloud has been considered an essential step in the digitalization of companies. In this way, user companies work with the solution that is best suited to their specific requirements.
When organizations migrate applications to the cloud, they expect to see significant benefits: increased scalability, stronger security and accelerated adoption of new technologies. Certainly, no CIO would try to migrate a mainframe or a traditional monolithic application directly to the cloud. Whats the solution? Modernization.
Anecdotally, Ive heard of cases where consulting companies have been brought in to optimize the use of AI models because the resourcing cost increased by hundreds of thousands of dollars due to a lack of upfront planning. Optimizing resources based on application needs is essential to avoid setting up oversized resources, he states.
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