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In todays rapidly evolving business landscape, the role of the enterprise architect has become more crucial than ever, beyond the usual bridge between business and IT. In a world where business, strategy and technology must be tightly interconnected, the enterprise architect must take on multiple personas to address a wide range of concerns.
As enterprises scale their digital transformation journeys, they face the dual challenge of managing vast, complex datasets while maintaining agility and security. Unlike traditional masking methods, their solution ensures that the data remains usable for testing, analytics, and development without exposing the actual values.
A cloud analytics migration project is a heavy lift for enterprises that dive in without adequate preparation. A modern data and artificial intelligence (AI) platform running on scalable processors can handle diverse analytics workloads and speed data retrieval, delivering deeper insights to empower strategic decision-making.
In today’s data-driven world, large enterprises are aware of the immense opportunities that data and analytics present. Yet, the true value of these initiatives is in their potential to revolutionize how data is managed and utilized across the enterprise.
Its an offshoot of enterprise architecture that comprises the models, policies, rules, and standards that govern the collection, storage, arrangement, integration, and use of data in organizations. Limit the times data must be moved to reduce cost, increase data freshness, and optimize enterprise agility. Real-time analytics.
To capitalize on the enormous potential of artificial intelligence (AI) enterprises need systems purpose-built for industry-specific workflows. Enterprise technology leaders discussed these issues and more while sharing real-world examples during EXLs recent virtual event, AI in Action: Driving the Shift to Scalable AI.
Azure Synapse Analytics is Microsofts end-to-give-up information analytics platform that combines massive statistics and facts warehousing abilities, permitting advanced records processing, visualization, and system mastering. What is Azure Synapse Analytics? Why Integrate Key Vault Secrets with Azure Synapse Analytics?
AI practitioners and industry leaders discussed these trends, shared best practices, and provided real-world use cases during EXLs recent virtual event, AI in Action: Driving the Shift to Scalable AI. And its modular architecture distributes tasks across multiple agents in parallel, increasing the speed and scalability of migrations.
At the same time, many organizations have been pushing to adopt cloud-based approaches to their IT infrastructure, opting to tap into the speed, flexibility, and analytical power that comes along with it. This integration enhances the overall efficiency of IT operations. Better leverage their mainframe data with near real-time access.
research firm Vanson Bourne to survey 650 global IT, DevOps, and Platform Engineering decision-makers on their enterprise AI strategy. The Nutanix State of Enterprise AI Report highlights AI adoption, challenges, and the future of this transformative technology. Nutanix commissioned U.K.
While AI projects will continue beyond 2025, many organizations’ software spending will be driven more by other enterprise needs like CRM and cloud computing, Lovelock says. The rapid accumulation of data requires more sophisticated data management and analytics solutions, driving up costs in storage and processing,” he says.
The event focused on providing enterprises with an AI-optimized platform and open frameworks that make agents interoperable. Taken together, these tools aim to make enterprise AI more practical to deploy, scale, and manage, said Kaustubh K, practice director at Everest Group.
A high hurdle many enterprises have yet to overcome is accessing mainframe data via the cloud. Without integrating mainframe data, it is likely that AI models and analytics initiatives will have blind spots. It enhances scalability, flexibility, and cost-effectiveness, while maximizing existing infrastructure investments.
It’s a position many CIOs find themselves in, as Guan noted that, according to an Accenture survey, fewer than 10% of enterprises have gen AI models in production. “What’s Next for GenAI in Business” panel at last week’s Big.AI@MIT It’s time for them to actually relook at their existing enterprise architecture for data and AI,” Guan said.
TigerGraph , a well-funded enterprise startup that provides a graph database and analytics platform, today announced that it has raised a $105 million Series C funding round. The round was led by Tiger Global and brings the company’s total funding to over $170 million. ”
In April 2024, Dataiku and Cognizant surveyed 200 senior analytics and IT leaders from large enterprises worldwide. The results revealed a significant gap between what CIOs aim to achieve with Generative AI (GenAI) and analytics — and what they can realistically deliver.
Many companies have been experimenting with advanced analytics and artificial intelligence (AI) to fill this need. Yet many are struggling to move into production because they don’t have the right foundational technologies to support AI and advanced analytics workloads. Some are relying on outmoded legacy hardware systems.
In healthcare, AI-driven solutions like predictive analytics, telemedicine, and AI-powered diagnostics will revolutionize patient care, supporting the regions efforts to enhance healthcare services. Governments and enterprises will leverage AI for operational efficiency, economic diversification, and better public services.
To ensure every IT initiative directly contributes to measurable business outcomes, CIOs must move from operational managers to strategic partners, collaborating with business leaders to align IT decisions with enterprise goals. These metrics might include operational cost savings, improved system reliability, or enhanced scalability.
Running AI on mainframes as a trend is still in its infancy, but the survey suggests many companies do not plan to give up their mainframes even as AI creates new computing needs, says Petra Goude, global practice leader for core enterprise and zCloud at global managed IT services company Kyndryl.
The professional services arm of Marsh McLennan advises clients on the risks, shifts, and challenges facing the modern enterprise, most poignantly the vital role technology now plays in business and on the world stage. Simultaneously, major decisions were made to unify the company’s data and analytics platform.
In this role, she empowers and enables the adoption of data, analytics and AI across the enterprise to achieve business outcomes and drive growth. Mike Vaughan serves as Chief Data Officer for Brown & Brown Insurance. Arti Deshpande is a Senior Technology Solutions Business Partner for Brown & Brown Insurance.
Everstream Analytics , a supply chain insights and risk analytics startup, today announced that it raised $24 million in a Series A round led by Morgan Stanley Investment Management with participation from Columbia Capital, StepStone Group, and DHL. Plenty of startups claim to do this, including Backbone , Altana , and Craft.
Native Multi-Agent Architecture: Build scalable applications by composing specialized agents in a hierarchy. The announcements at Next ’25 included several enhancements: Unified Enterprise Search : Employees can access Agentspace’s search, analysis, and synthesis capabilities directly from Chrome’s search box.
DataOps (data operations) is an agile, process-oriented methodology for developing and delivering analytics. It brings together DevOps teams with data engineers and data scientists to provide the tools, processes, and organizational structures to support the data-focused enterprise.
Rockset , a cloud-native analytics company, announced a $40 million Series B investment today led by Sequoia with help from Greylock, the same two firms that financed its Series A. Series D as scalable database resonates. The startup has now raised a total of $61.5 million, according to the company. Cockroach Labs scores $86.6M
Israeli startup Firebolt has been taking on Google’s BigQuery, Snowflake and others with a cloud data warehouse solution that it claims can run analytics on large datasets cheaper and faster than its competitors. The opportunity that Firebolt is targeting is a ripe one in the world of enterprise. billion valuation. (It’s
The professional services arm of Marsh McLellan advises clients on the risks, shifts, and challenges facing the modern enterprise, most poignantly the vital role technology now plays in business and on the world stage. Simultaneously, major decisions were made to unify the company’s data and analytics platform.
As enterprise CIOs seek to find the ideal balance between the cloud and on-prem for their IT workloads, they may find themselves dealing with surprises they did not anticipate — ones where the promise of the cloud, and cloud vendors, fall short versus the realities of enterprise IT.
Infinidat Recognizes GSI and Tech Alliance Partners for Extending the Value of Infinidats Enterprise Storage Solutions Adriana Andronescu Thu, 04/17/2025 - 08:14 Infinidat works together with an impressive array of GSI and Tech Alliance Partners the biggest names in the tech industry. Its tested, interoperable, scalable, and proven.
Analyzing data generated within the enterprise — for example, sales and purchasing data — can lead to insights that improve operations. That’s why Uri Beitler launched Pliops , a startup developing what he calls “data processors” for enterprise and cloud data centers. Marvell has its Octeon technology.
As enterprises navigate complex data-driven transformations, hybrid and multi-cloud models offer unmatched flexibility and resilience. Heres a deep dive into why and how enterprises master multi-cloud deployments to enhance their data and AI initiatives. The terms hybrid and multi-cloud are often used interchangeably.
As a result, enterprises can gain business benefits, including the ability to: Maximize and protect their existing VMware investments Easily connect workloads running in different environments Place workloads where they want, when they want Gain access to all the benefits of the public cloud.
Beyond improved patient outcomes, AI integrated into site reliability engineering can help improve the scalability of software systems. Enterprises should use ethical frameworks to ensure that AI applications undergo rigorous testing and validation before being deployed in order to safeguard patient safety and data privacy.
To move faster, enterprises need robust operating models and a holistic approach that simplifies the generative AI lifecycle. With Amazon Cognito , you can authenticate and authorize users from the built-in user directory, from your enterprise directory, and from other consumer identity providers.
Because data management is a key variable for overcoming these challenges, carriers are turning to hybrid cloud solutions, which provide the flexibility and scalability needed to adapt to the evolving landscape 5G enables. However, the complexity of managing workloads across different environments can be daunting.
Datasphere empowers organizations to unify and analyze their enterprise data landscape without the need for complex extraction or rebuilding processes. It enables seamless and scalable access to SAP and non-SAP data with its business context, logic, and semantic relationships preserved.
Enterprises generate massive volumes of unstructured data, from legal contracts to customer interactions, yet extracting meaningful insights remains a challenge. In a world whereaccording to Gartner over 80% of enterprise data is unstructured, enterprises need a better way to extract meaningful information to fuel innovation.
In a world of emerging technologies and powerful new analytics models, speed is as critical as accuracy—and in this world, the cloud is going to fall short. Oliver Schabenberger, Executive Vice President and Chief Technology Officer at analytics firm SAS, argues the edge should be the starting point for enterprise organizations.
In this role, she empowers and enables the adoption of data, analytics and AI across the enterprise to achieve business outcomes and drive growth. Mike Vaughan serves as Chief Data Officer for Brown & Brown Insurance. Arti Deshpande is a Senior Technology Solutions Business Partner for Brown & Brown Insurance.
The companys ability to provide scalable, high-performance solutions is helping businesses leverage AI for growth and transformation, whether that means improving operations or offering better customer service. A key point shared during the summit was how the Kingdoms organizations are increasingly investing in AI.
In today’s digital world, the ability to make data-driven decisions and develop strategies that are based on data analytics is critical to success in every industry. This team eventually became Enterprise D&A, with team members based around the world. We need future-ready, scalable, and flexible data platforms.
Commercial enterprises are increasingly leveraging technology to drive sustainable growth and optimize operations, all while minimizing environmental impact. Through scalable processes, real-time data, and advanced analytics, companies are reinventing their business models to achieve efficiency and reduce waste.
Beyond banking and payments, multi-national enterprises across various industries are exploring super-apps potential to enhance operational efficiency and improve customer and employee user experiences. Understanding these obstacles is crucial for enterprises seeking to leverage the super-app model effectively.
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