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Cloud analytics migration: how to exceed expectations

CIO Business Intelligence

If expectations around the cost and speed of deployment are unrealistically high, milestones are missed, and doubt over potential benefits soon takes root. The right tools and technologies can keep a project on track, avoiding any gap between expected and realized benefits. But this scenario is avoidable.

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Should finance organizations bank on Generative AI?

CIO Business Intelligence

In the finance and banking industry, however, organizations are seeking extra guidance on the best way forward. That’s because generative AI large language models (LLMs) have prowess in text-based generation, readily finding language and word patterns. And the finance industry is investing to do so. In short, yes. Automation.

Finance 133
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Balancing the costs and opportunities of GenAI adoption

CIO Business Intelligence

But alongside its promise of significant rewards also comes significant costs and often unclear ROI. For CIOs tasked with managing IT budgets while driving technological innovation, balancing these costs against the benefits of GenAI is essential. million in 2026, covering infrastructure, models, applications, and services.

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How data privacy leader Apple found itself in a data ethics catastrophe

O'Reilly on Data

Three months ago, Apple released a new credit card in partnership with Goldman Sachs that aimed to disrupt the highly regulated world of consumer finance. Apple is a great producer of computer hardware, while Goldman knows finance and its complex rules backwards and forwards. Ethics is much more slippery.

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To understand the risks posed by AI, follow the money

O'Reilly on Data

Others retort that large language models (LLMs) have already reached the peak of their powers. These are risks stemming from misalignment between a company’s economic incentives to profit from its proprietary AI model in a particular way and society’s interests in how the AI model should be monetised and deployed.

Risk 311
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Build a high-performance quant research platform with Apache Iceberg

AWS Big Data

Our experiments are based on real-world historical full order book data, provided by our partner CryptoStruct , and compare the trade-offs between these choices, focusing on performance, cost, and quant developer productivity. You can refer to this metadata layer to create a mental model of how Icebergs time travel capability works.

Metadata 106
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Structural Evolutions in Data

O'Reilly on Data

” Web3 has similarly progressed through “basic blockchain and cryptocurrency tokens” to “decentralized finance” to “NFTs as loyalty cards.” Hadoop’s value—being able to crunch large datasets—often paled in comparison to its costs. The elephant was unstoppable. Until it wasn’t.