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Digital transformation 2025: What’s in, what’s out

CIO Business Intelligence

Organizations will always be transforming , whether driven by growth opportunities, a pandemic forcing remote work, a recession prioritizing automation efficiencies, and now how agentic AI is transforming the future of work.

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It’s 2025. Are your data strategies strong enough to de-risk AI adoption?

CIO Business Intelligence

If 2023 was the year of AI discovery and 2024 was that of AI experimentation, then 2025 will be the year that organisations seek to maximise AI-driven efficiencies and leverage AI for competitive advantage. Primary among these is the need to ensure the data that will power their AI strategies is fit for purpose.

Risk 111
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Business Strategies for Deploying Disruptive Tech: Generative AI and ChatGPT

Rocket-Powered Data Science

Third, any commitment to a disruptive technology (including data-intensive and AI implementations) must start with a business strategy. These changes may include requirements drift, data drift, model drift, or concept drift. encouraging and rewarding) a culture of experimentation across the organization.

Strategy 290
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The DataOps Vendor Landscape, 2021

DataKitchen

This is not surprising given that DataOps enables enterprise data teams to generate significant business value from their data. Companies that implement DataOps find that they are able to reduce cycle times from weeks (or months) to days, virtually eliminate data errors, increase collaboration, and dramatically improve productivity.

Testing 304
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How EUROGATE established a data mesh architecture using Amazon DataZone

AWS Big Data

For container terminal operators, data-driven decision-making and efficient data sharing are vital to optimizing operations and boosting supply chain efficiency. Together, these capabilities enable terminal operators to enhance efficiency and competitiveness in an industry that is increasingly data driven.

IoT 111
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What you need to know about product management for AI

O'Reilly on Data

AI products are automated systems that collect and learn from data to make user-facing decisions. All you need to know for now is that machine learning uses statistical techniques to give computer systems the ability to “learn” by being trained on existing data. Why AI software development is different.

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AI Product Management After Deployment

O'Reilly on Data

From a technical perspective, it is entirely possible for ML systems to function on wildly different data. For example, you can ask an ML model to make an inference on data taken from a distribution very different from what it was trained on—but that, of course, results in unpredictable and often undesired performance. I/O validation.