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What is data architecture? A framework to manage data

CIO

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). According to data platform Acceldata , there are three core principles of data architecture: Scalability.

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Cloud analytics migration: how to exceed expectations

CIO

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. Intel’s cloud-optimized hardware accelerates AI workloads, while SAS provides scalable, AI-driven solutions.

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Can serverless fix fintech’s scaling problem?

CIO

Add to this the escalating costs of maintaining legacy systems, which often act as bottlenecks for scalability. The latter option had emerged as a compelling solution, offering the promise of enhanced agility, reduced operational costs, and seamless scalability. For instance: Regulatory compliance, security and data privacy.

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Security is dead: Long live risk management

CIO

As regulators demand more tangible evidence of security controls and compliance, organizations must fundamentally transform how they approach risk shifting from reactive gatekeeping to proactive enablement. They demand a reimagining of how we integrate security and compliance into every stage of software delivery.

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The Importance of Security and Compliance in Enterprise Applications

OTS Solutions

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.

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AI market evolution: Data and infrastructure transformation through AI

CIO

Most AI workloads are deployed in private cloud or on-premises environments, driven by data locality and compliance needs. This allows organizations to maximize resources and accelerate time to market. Other key uses include fraud detection, cybersecurity, and image/speech recognition.

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Navigating the future of national tech independence with sovereign AI

CIO

This ensures data privacy, security, and compliance with national laws, particularly concerning sensitive information. Compliance with the AI Act ensures that AI systems adhere to safety, transparency, accountability, and fairness principles. It is also a way to protect from extra-jurisdictional application of foreign laws.