This book is a curated collection of articles published throughout 2025 by DataS2, an independent research laboratory dedicated to understanding data as a way of thinking — not as a product or a promise of optimization.
Rather than offering tutorials, frameworks, or definitive answers, these texts document questions as they emerged in real time: questions about small data, decision-making under uncertainty, automation, ethics, system design, and the limits of scale. Each article reflects a moment of inquiry, shaped by practical constraints, technical trade-offs, and intellectual doubt.
Readers should approach this book not as a guide to implementation, but as an invitation to slow down and examine how data-driven decisions are framed, justified, and often misunderstood. The value of the book lies in its coherence over time. Taken together, the articles reveal a consistent way of reasoning — skeptical of hype, attentive to context, and cautious about delegating judgment to systems.
This book is for readers who are less interested in quick answers and more interested in how good questions are formed. For professionals, researchers, and decision-makers working with limited data, imperfect information, or complex systems, it offers a way to recognize familiar problems from a different angle.
Reading this book means engaging with uncertainty deliberately. Not to resolve it, but to understand where it comes from — and why that understanding matters.