Complexity & Systems of MeaningGet the book

Part II

Data as a strategic asset

Is the data fit for the decision?

The idea, in brief

I prefer to describe data as AI’s ground rather than its fuel. I follow the chain from data to decisions and ask where it breaks in practice. I examine quality, taxonomies, contextual ontologies and governance responsibilities, without confusing a plausible answer with a true one.

Here I summarise the chapter’s argument. Below you will find an excerpt from my book and the references I cite there, numbered as in the bibliography.

Companion page reviewed · 17 October 2026

From the book’s companion

An excerpt from my book

Original Italian text, from the chapter opening. · 1 min

Spinti dalla necessità di trovare metafore ad effetto o locuzioni memorabili che ancorino un concetto a una frase che possa richiamarlo senza doverlo esprimere, abbiamo tutti, prima o dopo, affermato (senza interrogarci a fondo sul significato di quell'affermazione) che "i dati sono il carburante dell'AI". Sarà che l'attuale situazione mondiale rende problematico riferirsi senza doppiosensismi ai carburanti e ai loro precursori; però vorrei provare ad offrire una lettura diversa e suggerire una metafora alternativa: i dati sono il terreno su cui la rivoluzione si gioca, e la qualità di quel terreno determina se quella trasformazione produce valore o solo macerie elegantemente formulate. Nella prima parte avevamo introdotto la catena del valore digitale come strumento epistemologico ed etico per localizzare dove, nella sequenza che va dal dato grezzo alla decisione operativa supportata dalla conoscenza, le questioni etiche emergono con più urgenza [46, 15]. Qui riuso quella stessa catena come strumento operativo chiedendomi "dove si spezza nella pratica?". La risposta, nella maggior parte delle organizzazioni che mi è capitato di assistere in questi anni, è brutale e semplice: si spezza al primo anello, perché i dati (se ci sono) non sono adeguati e "pronti".

My note

There was no more open door to push at than this one. It is easy to say trite things when talking about data, yet it is so hard to talk about it well that it seems impossible the problem could, at times, lie entirely there.

The chapter’s infographics

Enlarge each plate to inspect it. The overview and extended text above explain the chapter in text; they are not a word-for-word transcription of the images.

Chapter infographic map #

Check and compare

References I cite in this chapter

I have gathered the references cited in this chapter, preserving the entries and numbering of my book’s bibliography. Links are included only where supplied in the book.

  • [46] Floridi, Luciano. The Ethics of Artificial Intelligence: Principles, Challenges, and Opportunities. Oxford University Press, Oxford, 2023.
  • [15] Benanti, Paolo. Human in the Loop. Decisioni umane e intelligenze artificiali. Mondadori Università, Milano, 2022.
  • [52] Gartner. Lack of AI-Ready Data Puts AI Projects at Risk. Report, febbraio 2025.
  • [54] Gartner. Organizations with Successful AI Initiatives Invest Up to Four Times More in Data and Analytics Foundations. Report, aprile 2026.
  • [107] Redman, Thomas C. Data Driven: Profiting from Your Most Important Business Asset. Harvard Business Press, Boston, MA, 2008.
  • [31] DAMA International. DAMA-DMBOK: Data Management Body of Knowledge. 2nd Edition Revised, Technics Publications, Basking Ridge, NJ, 2017.
  • [16] Bender, Emily M., Timnit Gebru, Angelina McMillan-Major e Shmargaret Shmitchell. "On the Dangers of Stochastic Parrots: Can Language Models Be Too Big?". Proceedings of the 2021 ACM Conference on Fairness, Accountability, and Transparency (FAccT '21), 610–623, 2021.
  • [22] Brown, Tom B., Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, Sandhini Agarwal, Ariel Herbert-Voss, Gretchen Krueger, Tom Henighan, Rewon Child, Aditya Ramesh, Daniel M. Ziegler, Jeffrey Wu, Clemens Winter, Christopher Hesse, Mark Chen, Eric Sigler, Mateusz Litwin, Scott Gray, Benjamin Chess, Jack Clark, Christopher Berner, Sam McCandlish, Alec Radford, Ilya Sutskever e Dario Amodei. "Language Models are Few-Shot Learners". Advances in Neural Information Processing Systems, 33, 1877–1901, 2020.
  • [94] Osservatorio Innovazione Digitale nelle PMI. PMI, prospettive di analisi per la trasformazione digitale: stato dell'arte e passi per il futuro. Politecnico di Milano, Milano, 2025.
  • [93] Osservatorio Artificial Intelligence. Artificial Intelligence: adozione, trasformazione, equilibrio. Politecnico di Milano, Milano, febbraio 2026.
  • [25] Challapally, Aditya, Chris Pease, Ramesh Raskar e Pradyumna Chari. The GenAI Divide: State of AI in Business 2025. Tech report, MIT NANDA Project, 2025.
  • [90] Moretti, Franco. Distant Reading. Verso Books, London/New York, 2013.
  • [48] Floridi, Luciano. Distant Writing: Literary Production in the Age of Artificial Intelligence. SSRN preprint, 2025.
A term is unfamiliar? Open the glossary →

Bring it into your work

Is the data fit for the decision?

Write down one real example, identify what you would delegate and name who will check the result.

Use the worksheet

Connected ideas

is introduced by

From AI User to AI Value Creator

from creating value to the ground it is built on

introduces

The illusion of agentic AI

from data to the illusion of autonomous action

reinforces

ConcrEthica

the digital value chain: ethical map and operational tool on data

contributes to

Integration framework

feeds the Data and Information domain of the framework

contributes to

The formula of meaning

C₂ — where data leaves the factual

View these connections on the map →

Choose your edition

Keep reading in the book

Riccardo Bovetti · Essay · English edition. Purchase and current availability on Amazon.

Paperback on Amazon ↗

The links open the store; they do not purchase the book.