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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.
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).
To overcome those challenges and successfully scale AI enterprise-wide, organizations must create a modern data architecture leveraging a mix of technologies, capabilities, and approaches including data lakehouses, data fabric, and data mesh. Another challenge here stems from the existing architecture within these organizations.
It has become a strategic cornerstone for shaping innovation, efficiency and compliance. Data masking for enhanced security and privacy Data masking has emerged as a critical pillar of modern data management strategies, addressing privacy and compliance concerns. In 2025, data management is no longer a backend operation.
Unfortunately, data replication, transformation, and movement can result in longer time to insight, reduced efficiency, elevated costs, and increased security and compliance risk.
Today, data sovereignty laws and compliance requirements force organizations to keep certain datasets within national borders, leading to localized cloud storage and computing solutions just as trade hubs adapted to regulatory and logistical barriers centuries ago. Regulatory and compliance challenges further complicate the issue.
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. Architecture complexity.
However, as more organizations rely on these applications, the need for enterprise application security and compliance measures is becoming increasingly important. Breaches in security or compliance can result in legal liabilities, reputation damage, and financial losses.
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. It’s a tall order, because as technologies, business needs, and applications change, so must the environments where they are deployed.
This is where Delta Lakehouse architecture truly shines. Approach Sid Dixit Implementing lakehouse architecture is a three-phase journey, with each stage demanding dedicated focus and independent treatment. Step 2: Transformation (using ELT and Medallion Architecture ) Bronze layer: Keep it raw.
It adopted a microservices architecture to decouple legacy components, allowing for incremental updates without disrupting the entire system. Security and compliance concerns Barrier: Modernizing IT systems often involves handling sensitive data and integrating with external platforms, raising security and compliance concerns.
In todays digital-first economy, enterprise architecture must also evolve from a control function to an enablement platform. This transformation requires a fundamental shift in how we approach technology delivery moving from project-based thinking to product-oriented architecture. The stakes have never been higher.
It prevents vendor lock-in, gives a lever for strong negotiation, enables business flexibility in strategy execution owing to complicated architecture or regional limitations in terms of security and legal compliance if and when they rise and promotes portability from an application architecture perspective.
GRC certifications validate the skills, knowledge, and abilities IT professionals have to manage governance, risk, and compliance (GRC) in the enterprise. With companies increasingly operating on a global scale, it can require entire teams to stay on top of all the regulations and compliance standards arising today.
Every day, modern organizations are challenged with a balancing act between compliance and security. While compliance frameworks provide guidelines for protecting sensitive data and mitigating risks, security measures must adapt to evolving threats. Here are several ways identity functions help both security and compliance efforts.
Adopting multi-cloud and hybrid cloud solutions will enhance flexibility and compliance, deepening partnerships with global providers. Businesses will increasingly implement zero-trust architectures, focusing on strict identity verification and minimizing access to sensitive systems.
And as part of the expanded partnership, the two companies are collaborating on a joint data flywheel architecture that will integrate ServiceNow Workflow Data Fabric with Nvidia NeMo microservices. Through the control tower, customers can govern and secure AI agents, models, and workflows from a single pane of glass.
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. However, overcoming challenges such as workforce readiness, regulatory compliance, and cybersecurity risks will be critical to realizing this vision.
Cloud architects are responsible for managing the cloud computing architecture in an organization, especially as cloud technologies grow increasingly complex. At organizations that have already completed their cloud adoption, cloud architects help maintain, oversee, troubleshoot, and optimize cloud architecture over time.
. – Hollie Hennessy, Principal Analyst, Omdia Our remote access solution features a simple, browser-based architecture with an integrated jump server that reduces deployment complexity, making secure remote access management easier for both users and administrators.
Renewing sustainable production and procurement while meeting compliance To extend its commitment to sustainable production and procurement, González Byass needed a holistic view of its operations across its value chain. The solution has also eased compliance. And it helps significantly reduce potential risks from future regulations.
Understanding this complexity, the FinOps Foundation is developing best practices and frameworks to integrate SaaS into the FinOps architecture. Another essential skill for managing the possible hazards of non-compliance and overuse is having a deep understanding of SaaS contracts.
Our Databricks Practice holds FinOps as a core architectural tenet, but sometimes compliance overrules cost savings. There is a catch once we consider data deletion within the context of regulatory compliance. However; in regulated industries, their default implementation may introduce compliance risks that must be addressed.
CIOs must develop comprehensive strategies to mitigate risks such as cybersecurity threats, data privacy issues, and compliance challenges. For example, as businesses migrate to cloud platforms, CIOs must ensure robust data protection mechanisms are in place to prevent security breaches and maintain regulatory compliance.
It is important for organizations to establish clear frameworks that help prevent their AI agents from putting their cloud operations at risk, including monitoring agent activities to ensure compliance with data regulations, he says. This will lead to an operational headache for the C-suite, Dutta says.
Without the right cloud architecture, enterprises can be crushed under a mass of operational disruption that impedes their digital transformation. What’s getting in the way of transformation journeys for enterprises? This isn’t a matter of demonstrating greater organizational resilience or patience.
When evaluating options, prioritize platforms that facilitate data democratization through low-code or no-code architectures. Effective data governance and quality controls are crucial for ensuring data ownership, reliability, and compliance across the organization.
“This shift and convergence is required for more than just compliance — it represents a fundamental move toward a more integrated, transparent, accountable, and ethically responsible approach to AI,” Chaurasia and Maheshwari said. 75% of firms that build aspirational agentic AI architectures on their own will fail.
Over the course of our work together modernizing data architectures and integrating AI into a wide range of insurance workflows over the last several months, we’ve identified the four key elements of creating a data-first culture to support AI innovation.
It adheres to enterprise-grade security and compliance standards, enabling you to deploy AI solutions with confidence. Legal teams accelerate contract analysis and compliance reviews , and in oil and gas , IDP enhances safety reporting. Loan processing with traditional AWS AI services is shown in the following figure.
Not my original quote, but a cardinal sin of cloud-native data architecture is copying data from one location to another. Prior to Equifax, Manish led the global platform architecture and shared services group for Travelport, where he brought hybrid cloud and big data platforms to the company.
Executives need to understand and hopefully have a respected relationship with the following IT dramatis personae : IT operations director, development director, CISO, project management office (PMO) director, enterprise architecture director, governance and compliance Director, vendor management director, and innovation director.
They chain together with the transaction boundaries to create the experience, and we created abstraction in every layer of the architecture to create flexibility, and help scale and pivot business models. We have a platform value goal and technology goals for reliability, stability, and compliance.
To achieve these goals, the AWS Well-Architected Framework provides comprehensive guidance for building and improving cloud architectures. The solution incorporates the following key features: Using a Retrieval Augmented Generation (RAG) architecture, the system generates a context-aware detailed assessment.
The reasons include higher than expected costs, but also performance and latency issues; security, data privacy, and compliance concerns; and regional digital sovereignty regulations that affect where data can be located, transported, and processed. The primary driver for leveraging private cloud over public cloud is cost, Hollowell says.
This solution is designed to accelerate platform modernization, streamline workflow assessment and enable data discovery, helping organizations drive efficiency, scalability and compliance, said Swati Malhotra, AI solutions leader at EXL. The EXLerate.AI
For example, a business that depends on the SAP platform could move older, on-prem SAP applications to modern HANA-based Cloud ERP and migrate other integrated applications to SAP RISE (a platform that provides access to most core AI-enabled SAP solutions via a fully managed cloud hosting architecture).
The architecture seamlessly integrates multiple AWS services with Amazon Bedrock, allowing for efficient data extraction and comparison. The following diagram illustrates the solution architecture. You can process and analyze the models response within your function, extracting the compliance score, relevant analysis, and evidence.
For Marc, the certification is not just a compliance checkboxits an affirmation of Cranes commitment to structured, scalable, and resilient systems. This business-oriented mindset is evident in his pursuit of ISO 27001 certification, which is expected to be completed in the near term.
He advises beginning the new year by revisiting the organizations entire architecture and standards. If not, Thorogood recommends IT leaders build platforms that savvy business managers can use and encourage or require compliance with enterprise standards and processes. Are they still fit for purpose?
Also, examine whether the company has mechanisms for ensuring the accuracy of retrieved information, as industries like healthcare and financial compliance require high levels of reliability. These architectures allow companies to iterate quickly, customize their solutions and reduce overhead.
MaestroQA also offers a logic/keyword-based rules engine for classifying customer interactions based on other factors such as timing or process steps including metrics like Average Handle Time (AHT), compliance or process checks, and SLA adherence. The following architecture diagram demonstrates the request flow for AskAI.
Were adopting best-in-class SaaS solutions, a next-generation data architecture, and AI-powered applications that improve decision-making, optimize operations, and unlock new revenue stream opportunities. What are some examples of this strategy in action?
Image: The Importance of Hybrid and Multi-Cloud Strategy Key benefits of a hybrid and multi-cloud approach include: Flexible Workload Deployment: The ability to place workloads in environments that best meet performance needs and regulatory requirements allows organizations to optimize operations while maintaining compliance.
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