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Why Strong Data Engineering Makes Your AI Project Scalable
Many AI projects perform brilliantly in a test environment, only to break down once they go live. The root cause is rarely the model itself. More often, it’s the data foundation underneath it. This article explains what data engineering actually involves, why it’s the critical success factor behind scalable AI, and how Twentynext puts it into practice.
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Choosing a BI Platform? 7 Pitfalls for Growing Companies
For growing companies, choosing a Business Intelligence platform isn’t just a technical decision, it’s a strategic one. Yet many BI initiatives fail for the same seven reasons, and most of those issues only become visible later. This article breaks them down and shows you how to avoid them.
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An R&D Week at Twentynext: From Research Paper to Production Code
At Twentynext, data scientists don’t sit idle between projects. They read research papers, build prototypes, and sometimes turn them into production code within the same week. This article shows what that R&D cycle looks like in practice—and why it matters for both clients and talent.
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When Is Your Organization Truly Ready for an AI Use Case?
Many organizations want to adopt AI but lack the foundations to do it responsibly. This article walks through a step-by-step approach to assess whether your organization is ready for an AI use case, from data quality and business fit to governance and scalability.
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What CRISP-DM Brings to Modern Data Science Projects
CRISP-DM has been the go-to methodology for data science projects for decades. But what does it actually add now that more organizations want to roll out generative AI? This article shows how a structured approach makes the difference between a model that performs well in a lab and an AI solution that delivers real business value.
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The AI paradox in Dutch organizations: more data doesn’t always mean better decisions
Dutch organizations are investing heavily in data and AI, yet better decision-making remains out of reach for many. This article explains why the paradox of data overload and weak decision-making is so persistent, and how the right approach helps organizations turn AI into real results.
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