Data & Governance
Data quality is the number-one prerequisite for reliable AI. Articles on governance, lineage, observability and best practices.
AI is only as good as your data. If it is incomplete, inconsistent or poorly documented, AI amplifies errors instead of solving them. This series covers the fundamentals: quality, governance, lineage, observability and compliance.
15 articles in this cluster
Claude Mythos Preview: Anthropic builds an AI too powerful to be made public
Anthropic unveils Claude Mythos Preview, a model able to detect previously unknown security flaws. But it is reserved for a club of 40 tech giants. What this means for SMB cybersecurity and unequal access to the best technologies.
The AI Act comes into force: what changes for businesses
The European AI regulation (AI Act) comes into force. Which systems are affected, which obligations apply and how to prepare right now.
Quality control plan: example rules (completeness, freshness)
A quality control plan defines the rules that guarantee the reliability of your data: completeness, uniqueness, format compliance, freshness. Here are 20 concrete example rules to apply to your CRM, ERP and BI tables, with the tools to automate them.
Business glossary: unifying definitions before automating
When marketing counts 12,000 customers and accounting 9,500, the problem isn't technical — it's that 'customer' doesn't have the same definition. A business glossary unifies business terms before you automate. Here's how to build it.
Open data and AI: creating value without exposing your internal data
Open data is a goldmine underused by SMBs. Combined with AI, it enriches your analyses without exposing your internal data. Here's how to identify, integrate and exploit open data in your AI projects.
AI dashboards: the 15 metrics to track
An AI model in production without a dashboard is like a plane without an instrument panel: you fly blind. Here are the 15 essential metrics to monitor your AI models — from technical performance to business ROI — and how to build an actionable dashboard.
GDPR and AI: data checklist (collection, minimization, retention)
Deploying AI on personal data without complying with the GDPR means taking a major legal and financial risk. Here's a concrete 15-point checklist to secure your AI projects: collection, minimization, retention period and individuals' rights.
Building a 'RAG-ready' document base (process and formats)
Your generative AI is only as good as the documents it consults. Building a RAG-ready document base means structuring, cleaning and formatting your content so that retrieval works. Here's the complete process, from PDFs to optimized chunks.
Automating ESG reports with AI
The CSRD directive imposes new ESG reporting obligations on companies. AI makes it possible to automate data collection, analysis and report writing to save up to 60% of the time.
Data observability: detecting drift before incidents
Your data changes silently: volumes that fluctuate, distributions that drift, freshness that degrades. Data observability detects these anomalies before they break your dashboards and your AI models. Here's how to set it up.
AI and regulatory compliance: automate without risk
Manual regulatory monitoring is costly and lets critical changes slip through. AI automates detection, impact analysis and the generation of compliance reports.
Data lineage: why it's essential in AI
When an AI model produces a wrong result, the first question is: where does the data come from? Data lineage traces the journey of each data point, from its source to its consumption. Essential for debugging, auditing and trusting your AI pipelines.
Data governance: a simple model (Data Owner / Steward / rules)
Data governance doesn't have to be a monumental project. A simple model — Data Owner, Data Steward, quality rules — is enough to lay the foundations of a reliable data organization. Here's how to roll it out in SMBs/mid-market companies in 8 weeks.
The AI Act comes into force: a first assessment
Since February 2025, the first provisions of the European AI Act have been applicable. Between new obligations and compliance opportunities, here is an overview of what is concretely changing for French companies.
Data quality: the number 1 prerequisite for useful AI
80% of AI projects fail because of the data, not the algorithms. Discover the most frequent problems, an assessment framework in 5 dimensions and the metrics to track to ensure genuinely useful AI.