1. FROM DATA TO DECISION: HOW TO BUILD AN EFFECTIVE JOURNEY THAT STARTS WITH DATA, IDENTIFIES TRENDS, RISKS, AND OPPORTUNITIES, AND SUPPORTS STRATEGIC DECISIONS?

In recent years, the exponential growth of data and the emergence of new tools for fast and effective analysis have profoundly changed how companies make decisions. Today, thanks to increasingly accessible storage systems, powerful computing capabilities, and natural language-based Business Intelligence tools, data is no longer the exclusive domain of analysts: it has become a shared resource across all business functions. This shift enables more informed decisions based on concrete evidence, enhancing the ability to identify trends, risks, and opportunities, and to quickly adapt business strategies.

Data-Driven Decision Making (DDDM) is built on three fundamental pillars: data quality and management, structured and repeatable analysis, and integrated decision-making systems that include dashboards, alerts, and human review. This approach makes it possible to promptly detect changes, tailor products and services to customer needs, and continuously monitor business performance. Moreover, DDDM does not aim to replace the experience or intuition of managers, but rather to enhance their contribution by reducing uncertainty and promoting more solid and reliable choices.

The centrality of data in business management is now a key competitive factor: those who adopt DDDM can optimize processes, overcome cognitive biases, and seize new growth opportunities.

Concepts such as Data-Driven Management (DDM) and Data-Driven Strategic Management (DDSM) are gaining increasing relevance, precisely because they combine the value of human insight with the power of data.

The so-called “Digital Divide” will become an increasingly decisive factor: the gap between companies that stand out through intelligent data use and those that remain anchored to outdated models will continue to widen. DDDM is therefore an essential tool for any organization aiming to lead in the digital era, enabling data-driven decisions that fully unlock business potential and deliver tangible results.

3. HOW IS AI SHAPING THE EVOLUTION OF DATA VISUALIZATION, AND WHAT OPPORTUNITIES OR CHALLENGES DOES THIS TRANSFORMATION BRING?

Artificial Intelligence is revolutionizing data visualization, transforming it from a descriptive tool into a powerful means of data discovery and storytelling. Through the integration of machine learning algorithms and natural language models, today’s analytics platforms can automatically generate relevant visualizations, uncover hidden correlations, and deliver real-time insights. This evolution, known as Augmented Analytics, is making data analysis more accessible and interactive, allowing users to “converse” with information in a simple and natural way.

However, this transformation also introduces new challenges: ensuring transparency in algorithmic processes, preventing bias in results, and maintaining a critical human role in interpreting information, essentially giving data a voice.

At Engineering, we are providing that voice through the native integration between Knowage, our open-source business intelligence platform, and EngGPT, the Italian Large Language Model developed by the Group for Private GenAI. This synergy combines the analytical power of data with the contextual understanding of AI, making visualizations smarter and more personalized.

Ultimately, AI does not replace human intelligence: it enhances it. It turns numbers into stories, supports more informed decisions, and makes data visualization more human, narrative, and inclusive.

5. IN A CONTEXT WHERE DATA SOVEREIGNTY IS BECOMING INCREASINGLY CENTRAL, HOW CAN BUSINESSES COMBINE THE NEED FOR ADVANCED, ACCESSIBLE INSIGHTS WITH FULL CONTROL AND SECURITY OVER THEIR DATA? WHAT SOLUTIONS DOES ENGINEERING OFFER IN THIS REGARD?

In today’s digital landscape, data sovereignty is a strategic priority. Organizations must strike a balance between the need for advanced, user-friendly insights and the imperative to ensure data security, governance, and regulatory compliance.

To meet this challenge, it is essential to adopt solutions that combine innovation with data protection, leveraging “trusted by design” architectures and Private GenAI models that keep processing and intelligence within secure, controlled environments.

Engineering stands out in this area by offering flexible solutions that integrate seamlessly with existing systems, along with continuous support in security and compliance. A concrete example is EngGPT, the Italian Large Language Model for Private GenAI developed by the Group, which enhances business data without exposing it to external platforms. It generates insights and language-based automations while fully respecting principles of data sovereignty and privacy-by-design.

In this way, organizations can harness the potential of AI to make more informed and competitive decisions, while maintaining full control, security, and trust over their data.