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Data Literacy

A network representation of the topic model describing the scientific literature of AI and Business.

The Data Literacy

Data literacy represents an organisation's ability to systematically integrate, interpret, and utilise digital information to support strategic and operational decisions. It goes beyond mere access to a vast array of data and focuses on having the skills and processes needed to extract value. In a context where Artificial Intelligence, advanced analytics systems, and real-time data management are reshaping business models, embracing this culture means adopting an approach centred on measurable and up-to-date evidence. This enables businesses to anticipate market trends, optimise customer experience, and foster sustainable growth in productivity and competitiveness.

Adopting a data culture also means recognising that intuition and experience alone are no longer sufficient. It requires merging managerial insight and market signal interpretation with analytical, technological, and organisational expertise. This involves creating multidisciplinary teams, investing in staff training, redefining decision-making processes, and establishing effective data infrastructures. It is a profound cultural transformation that relies on data to build an ecosystem where every participant – from field operators to top management – can contribute to the company’s growth by accessing timely and relevant information. In doing so, businesses not only respond more precisely to customer needs but also position themselves to innovate and thrive in increasingly complex and interconnected markets.

Measuring

The ability to accurately measure business processes is the cornerstone of any data-driven strategy. Without consistent metrics, reliable analytical tools, and a constant flow of accurate data, a company cannot fully understand its operations or identify areas for improvement. A solid measurement methodology, including well-defined data collection systems, quality controls, and verification protocols, ensures dependable information to inform decisions. This enables performance analysis across operational, financial, and market dimensions, identifying inefficiencies, process bottlenecks, and opportunities for innovation.

Developing a robust measurement methodology is not merely a technical matter but also an organisational and cultural one. It requires training personnel, defining clear responsibilities, and aligning departments around common goals for data quality. Investing in analytical tools and skills must go hand-in-hand with establishing transparent procedures, ensuring that every measurement is meaningful and actionable. Only then can a company build a shared "measure" that supports tactical decisions while fostering a virtuous cycle of continuous improvement. In this cycle, every element of the value chain contributes to achieving higher, tangible, and sustainable results.