Digital Trends constitute the main technological and market directions that influence the future of the business. They represent real strategic guidelines for business planning, elaborated from the observation of current dynamics.
For season #DigitalTrendsSS25, we have identified three main trends, and in this article we focus on the first trend.
We are used to thinking of data as ordered elements, precisely classified within well-defined containers: a sort of chest of drawers, where each piece of information finds its own designated space. This reassuring and familiar approach has long represented an effective model of organization.
However, today we are faced with a profoundly different reality. The data we need to collect and analyze no longer lend themselves to a rigid and schematic subdivision: they require more flexible, dynamic containers, capable of accommodating complexity and interconnections. In other words, we need a new paradigm, more similar to a large basket than a chest of drawers.
Traditional database technology, on which classic CRMs are based, is no longer adequate for the nature of contemporary data. However, when we try to force the use of these structures, the limits emerge clearly: performance drops, artificial intelligence models fail to function properly and relationships between data are lost or become difficult to interpret.
Data is essential but for it to have value it must be available and fast and, above all, used. It's not enough to accumulate them, you need to analyze them and make decisions in real time to transform them into concrete changes.
Leading database manufacturers have long grasped this shift and are introducing technologies designed to natively handle richer, more complex and dynamic data. The problem? Many current CRMs are not designed to accommodate these innovations and often end up hindering them, making information management more opaque and fragmented.
This is why today it is necessary to consider alternative tools, capable of supporting, or in some cases replacing, traditional relational databases. Among these, two solutions are emerging forcefully.
Understanding data through semantic proximity
Vector databases allow each piece of data to be represented as a set of numerical coordinates, thanks to embedding techniques. This allows you to analyze similarities between data in an extremely sophisticated way, beyond simple textual or personal correspondence.
We no longer ask “who has the same name” but “which elements are close in the space of meaning”. It is possible to identify semantic clusters, find similar elements, suggest content: an ideal technology for intelligent search systems, advanced semantic analysis and artificial intelligence applications.
Mapping complex relationships between entities
I graph database si fondano su un principio diverso: quello di gestire in tempo reale le relazioni tra oggetti. Clienti, prodotti, eventi, interazioni possono essere rappresentati come un nodo e un collegamento all’interno di una rete dinamica.
Questo consente di rilevare pattern nascosti, individuare comportamenti sospetti (come nelle applicazioni antifrode) oppure esplorare la struttura delle reti sociali e commerciali. Per un CRM moderno questa tecnologia rappresenta un valore strategico, perché sposta il focus dal dato anagrafico alla relazione stessa.
Today, favorable conditions exist to deal with this change, both technologically and methodologically:
Consolidated technologies such as NoSQL, vector DB and graph DB are now mature and easily integrated.
Artificial intelligence solutions are increasingly based on embedding and techniques that presuppose flexible data structures.
Denormalization-oriented architectures (such as BigQuery) simplify data access and querying, including through functional duplication.
The growing importance of historical data, often read-only, makes it easier to duplicate them to improve their consultation.
Some practical steps to prepare for this transformation:
If the goal is to prepare for the adoption of AI it is necessary to rethink data management. Today there is a need for a system capable of collecting, understanding and enhancing the enormous complexity of contemporary relational information.
Do you want to understand if your data system is ready for Artificial Intelligence? Write to us: we can help you make an initial assessment and show you the most suitable direction for your company.