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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. Optimize data flows for agility.
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.
A company that adopts agentic AI will gain competitive advantages in innovation, efficiency and responsiveness and may become more agile in operations. Stephen Kaufman serves as a chief architect in the Microsoft Customer Success Unit Office of the CTO focusing on AI and cloud computing. There are many reasons to build your own.
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. How Agile Lab and Enel Group used Dremio to connect their disparate organizations across geographies and business units. Leveraging Dremio for data governance and multi-cloud with Arrow Flight.
IBM Institute for Business Value (IBV), in collaboration with Oxford Economics, surveyed 2,551 global IT executives to determine how mainframes are being used and prepped for increased use in AI and hybrid cloud environments. Most enterprises have built tech estates on hybrid cloudarchitecture, the researchers stated. “In
I believe that the fundamental design principles behind these systems, being siloed, batch-focused, schema-rigid and often proprietary, are inherently misaligned with the demands of our modern, agile, data-centric and AI-enabled insurance industry. This is where Delta Lakehouse architecture truly shines.
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.
CIOs often have a love-hate relationship with enterprise architecture. In the State of Enterprise Architecture 2023 , only 26% of respondents fully agreed that their enterprise architecture practice delivered strategic benefits, including improved agility, innovation opportunities, improved customer experiences, and faster time to market.
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.
The public cloud turns 23 this year, and enterprise migration of on-premises workloads isnt just continuing its speeding up. According to the Foundry Cloud Computing Study 2024 , 63% of enterprise CIOs were accelerating their cloud migrations, up from 57% in 2023. Which can be true if your efforts end with migration.
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.
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).
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. Such a dynamic culture ensures that the organization remains flexible and adaptable in an ever-evolving digital landscape.
Particularly well-suited for microservice-oriented architectures and agile workflows, containers help organizations improve developer efficiency, feature velocity, and optimization of resources. Containers power many of the applications we use every day.
The latter option had emerged as a compelling solution, offering the promise of enhanced agility, reduced operational costs, and seamless scalability. Financial services unique challenges However, it is important to understand that serverless architecture is not a silver bullet. Architecture complexity. Legacy infrastructure.
Speed and agility bring in the top transformation prize. Go all-in with agile Another way to ensure IT can quickly deliver transformative results is to go all-in with modern approaches, starting with a full embrace of agile development. The 2024 State of Agile report from Digital.ai
What companies need to do in order to cope with future challenges is adapt quickly: slim down and become more agile, be more innovative, become more cost-effective, yet be secure in IT terms. Generally speaking, a healthy application and data architecture is at the heart of successful modernisation.
As enterprises scale their digital transformation journeys, they face the dual challenge of managing vast, complex datasets while maintaining agility and security. With the rise in hybrid and multi-cloud environments, businesses will increasingly need to secure sensitive data across diverse systems.
To keep up, IT must be able to rapidly design and deliver application architectures that not only meet the business needs of the company but also meet data recovery and compliance mandates. Moving applications between data center, edge, and cloud environments is no simple task. Typically, IT must create two separate environments.
With growing concerns over advanced threats, VPN security issues, network complexity, and adversarial AI, enterprises are showing increased interest in a zero trust approach to security and moving away from firewall-and-VPN based architecture. Only 15% do not have a plan to embrace zero trust this year.
Aerospike, provider of next-generation, real-time NoSQL data solutions, has released the Aerospike Kubernetes Operator and advancements in Aerospike Cloud Managed Service to help enterprises unlock cloud productivity and agility with scale-out cloud data. To […].
Technology investments, such as in generative AI, are a priority in addressing the need to meet rising expectations while also driving operational agility and resilience. He advises beginning the new year by revisiting the organizations entire architecture and standards. Are they still fit for purpose?
Enterprise architecture definition Enterprise architecture (EA) is the practice of analyzing, designing, planning, and implementing enterprise analysis to successfully execute on business strategies. Another main priority with EA is agility and ensuring that your EA strategy has a strong focus on agility and agile adoption.
Pre-COVID, agility became an aspiration and rallying cry for organizations seeking to embrace emerging technologies and pursue technology-enabled innovation, often to stave off digital disruption in their industries. This goes beyond implementing agile methodology. Balance control with agility. Think a step ahead.
Here, agility is essential, and smart IT leaders are doubling down on efforts to streamline IT, whether that involves reprioritizing projects and realigning the IT portfolio, rationalizing applications and pursuing cloud-native approaches, increasing automation through DevOps or AIOps adoption, or overhauling the structure of IT operations.
The topics of technical debt recognition and technology modernization have become more important as the pace of technology change – first driven by social, mobile, analytics, and cloud (SMAC) and now driven by artificial intelligence (AI) – increases. Which are not longer an architectural fit? Which are obsolete?
It is available both in a cloud-based SaaS and an on-premises version. 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 2010, SAP introduced the HANA database.
We spoke with Siddhartha Gupta, Global Head of Application Modernization on Azure at Tata Consultancy Services (TCS) , about this trend and what financial services organizations need to do to improve their capacity for agility and innovation. To remain relevant, traditional financial services firms must become as cutting-edge as fintechs.
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?
These outdated systems are not only costly to maintain but also hinder the integration of new technologies, agility, and business value delivery. For instance, Capital One successfully transitioned from mainframe systems to a cloud-first strategy by gradually migrating critical applications to Amazon Web Services (AWS).
Here, agility is essential, and smart IT leaders are doubling down on efforts to streamline IT, whether that involves reprioritizing projects and realigning the IT portfolio, rationalizing applications and pursuing cloud-native approaches, increasing automation through DevOps or AIOps adoption, or overhauling the structure of IT operations.
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.
Data is the foundation of innovation, agility and competitive advantage in todays digital economy. We also examine how centralized, hybrid and decentralized data architectures support scalable, trustworthy ecosystems. Data lives across siloed systems ERP, CRM, cloud platforms, spreadsheets with little integration or consistency.
The evolution of agile development The agile manifesto was released in 2001 and, since then, the development philosophy has steadily gained over the previous waterfall style of software development. Agents can be more loosely coupled than services, making these architectures more flexible, resilient and smart.
Although organizations have embraced microservices-based applications, IT leaders continue to grapple with the need to unify and gain efficiencies in their infrastructure and operations across both traditional and modern application architectures. VMware Cloud Foundation (VCF) is one such solution.
Organisations are now operating entire businesses in the cloud and benefiting from faster, more efficient, reliable, secure and scalable computing, but many with legacy architecture and code are finding themselves competing with businesses that have been ‘born in the cloud’ and lacking the agility of their competitors.
Pursue cryptographic agility Once companies have figured out which assets and communications they need to protect first, how do they actually go about switching to quantum-safe cryptography? Instead, Horvath and other experts recommend that enterprises pursue quantum agility. It seems easy to do but its actually catastrophic.
With digital operating models altering business processes and the IT landscape, enterprise architecture (EA) — a rigid stalwart of IT — has shown signs of evolving as well. An enterprise architecture tool is often sold as a prerequisite by consulting firms that often earn software commissions. Therefore EA is broadening its focus, too.
Innovation with respect to the customer experience remains crucial as global CX technology spending grows year-over-year , including increased spending on generative AI, the cloud, and digital services. Yet this acceleration can aggravate business management and create fundamental business risk, especially for established enterprises.
What companies need to do in order to cope with future challenges is adapt quickly: slim down and become more agile, be more innovative, become more cost-effective, yet be secure in IT terms. Generally speaking, a healthy application and data architecture is at the heart of successful modernisation.
Now that systems are being replaced, it’s also about creating a new architecture without those types of connections. The cloud journey completed Even before the system landscape is changed, Volvo Cars has made its cloud journey — one of the big, ongoing programs when Altehed started his role in 2019. “We
Today’s cloud strategies revolve around two distinct poles: the “lift and shift” approach, in which applications and associated data are moved to the cloud without being redesigned; and the “cloud-first” approach, in which applications are developed or redesigned specifically for the cloud.
It’s no longer a question of whether organizations are moving to the cloud but rather how well it’s going. Cloud isn’t that shiny new object in the distance, full of possibility. Companies may have had highly detailed migration or execution plans, but many failed to develop a point of view on the role of cloud in the enterprise.
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