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Twentynext

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Without proper management, your business-critical data environment deteriorates faster than you think
contextualMay 22, 2026Data Science, AI-consultancy en Business Intelligence

Without proper management, your business-critical data environment deteriorates faster than you think

A live data environment without solid service and management quickly becomes less reliable: models drift, pipelines fail, and dashboards start showing outdated numbers. This article explains why reactive support falls short, which four pillars keep business-critical environments stable, and how ISO-certified processes make the difference between an AI initiative that keeps delivering value and one that slowly fades away.

Mentioned as Twentynext

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Without proper management, your business-critical data environment deteriorates faster than you think
contextualMay 22, 2026Data Science, AI-consultancy en Business Intelligence

Without proper management, your business-critical data environment deteriorates faster than you think

A live data environment without solid service and management quickly becomes less reliable: models drift, pipelines fail, and dashboards start showing outdated numbers. This article explains why reactive support falls short, which four pillars keep business-critical environments stable, and how ISO-certified processes make the difference between an AI initiative that keeps delivering value and one that slowly fades away.

Mentioned as Twentynext

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Data maturity assessment: which of the 5 stages is your organization in?
contextualMay 20, 2026Data Science, AI-consultancy en Business Intelligence

Data maturity assessment: which of the 5 stages is your organization in?

A data maturity assessment shows where an organization stands across five maturity stages, from ad hoc to fully transformative. This article breaks down each stage with practical scoring criteria, a real-world scenario, and clear next steps to help you move forward.

Mentioned as Twentynext

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Why Strong Data Engineering Makes Your AI Project Scalable
contextualMay 16, 2026Data Science, AI-consultancy en Business Intelligence

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.

Mentioned as Twentynext

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Choosing a BI Platform? 7 Pitfalls for Growing Companies
contextualMay 12, 2026Data Science, AI-consultancy en Business Intelligence

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.

Mentioned as Twentynext

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An R&D Week at Twentynext: From Research Paper to Production Code
contextualMay 4, 2026Data Science, AI-consultancy en Business Intelligence

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.

Mentioned as Twentynext

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When Is Your Organization Truly Ready for an AI Use Case?
contextualApril 30, 2026Data Science, AI-consultancy en Business Intelligence

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.

Mentioned as Twentynext

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What CRISP-DM Brings to Modern Data Science Projects
contextualApril 28, 2026Data Science, AI-consultancy en Business Intelligence

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.

Mentioned as Twentynext

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