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Transforming data into value is a matter of survival in the market

Fabrizio Milano d'Aragona

The imperative of sustainability is becoming increasingly stringent across all industries. Today, it is essential to minimize energy waste in production. This transition profoundly transforms the industrial fabric, and the role of AI is to promote efficiency in all its forms: energy efficiency, production efficiency, and efficiency as a return on investment in marketing.

To fully embrace this paradigm in the near future, it is crucial to understand the opportunities and limitations of this scenario. There are elements of risk on one side and control on the other.

Data is an indispensable resource, the new oil that drives the world. Unlike traditional resources, data is constantly growing. However, like any resource, it also comes with costs related to security, compliance, and the need for privacy protection. Therefore, it is essential to make data intelligible to individuals as something that enriches their knowledge and potential for improvement across various fronts: industrial, social, and human.

In the current uncertain historical context, innovation is an urgent matter. Those who fail to innovate lose their competitive edge in the market. Change is often seen as uncertainty and risk, but the real question is: how much do we risk by not innovating and remaining stagnant in an ever-evolving market?


A sustainable approach to data-driven growth

The challenge for every company is to be able to transform the immense amount of data at its disposal into value, not only business value, but also ethical and sustainable growth.

What path should we take for a truly data-driven strategy? It lies in applying artificial intelligence in a more transparent and direct way, allowing business operators themselves to manage it, minimizing intermediation.

Today, business owners are the owners and custodians of a wealth of information from various touch points they manage, such as websites, physical channels, apps, and social networks.

Simultaneously, we must consider the enormous amount of alternative external data that may not be owned by the company but can still be analyzed to identify trends that contribute to the business landscape. Understanding these trends is vital not only for the current health of the business but also for launching new products or services. They allow us to explore the most promising characteristics for a specific market, as an alternative to lengthy and expensive traditional market research.

For instance, in the field of Marketing & Sales, companies that collect and use data correctly gain precise insights into various aspects of their strategy, enabling resource optimization. They can determine what type of product to offer to a particular target audience, how to increase customer lifetime value, and how to optimize their actions, among others.

Considering the vast amount of data companies encounter, there is a fundamental premise: not all data is equal. This brings us to a discussion beyond quantity and focuses on the quality of data, where AI once again plays a crucial role in establishing its veracity and compliance.

For alternative data, artificial intelligence can identify its source, assess the consistency over time, and process trend fluctuations for more reliable results. Meanwhile, for proprietary internal data, AI facilitates the correct extraction and processing according to GDPR criteria, ensuring privacy protection.


AI for innovation respecting privacy and the environment

The epochal shift that brought AI out of the laboratory and transformed it into a business application was the advent of cloud computing. Cloud computing allows for the secure organization of data aggregation in private environments. To reconcile security and compliance, key elements include the availability of European and Italian hubs for supercomputing and great computing, providing secure and compliant locations for data storage.

Hardware supporting Artificial Intelligence must continually become more powerful. Therefore, additional effort is required to mitigate the risk of an imbalanced environmental impact, which affects any digital process.

It is crucial to adopt techniques and strategies that respect ongoing energy optimizations from the outset. This will create a system where the resource efficiency enabled by AI far outweighs its carbon footprint in the long run.