Data governance in the era of generative artificial intelligence
In a scenario characterised by rapid digitalisation, data governance has emerged as a key strategic leverage tool. With the spread of Generative Artificial Intelligence (GenAI) which enables the complex content to be created autonomously, the implementation of management models able to ensure data control, safety and value has become indispensable.
The focus is no longer solely on the gathering or storage of information, it now extends to the ability to structure, protect and make best use of the data, in order to be able to support intelligent, reliable decisions across a range of contexts.
Data governance as a decision-making structure
At present, data provides a basis for essential factors such as decision-making processes, product customisation, risk management and the digital transformation. Data has in fact assumed the role of a critical infrastructure, just like energy or the internet.
On the one hand, it is important to bear in mind that without good quality information, no automated or artificial intelligence system can produce viable results, but on the other hand, it should be emphasised that merely gathering large volumes of data is no longer sufficient. Considering that GenAI enhances the advantages of well-managed data while multiplying the risks when it is poorly managed, it is essential to know how to:
- structure it;
- verify it;
- update it;
- make it interoperable;
- and (above all) contextualise it.
If we take this into account, it is clear that ‘governing data’ does not simply mean imposing rules, but rather building an authentic decision-making structure. The origin of every piece of information must be traceable, the data must be classifiable by importance and relevance, safe to use and coherent with the processes it is to be used for.
Companies wishing to make strategic use of AI must equip themselves with data governance polices capable of combining technological, organisational and regulatory aspects effectively.
One key issue is the accountability of those involved: the clear definition of roles, eg data owner and data steward is required, as well as of workflow approvals and auditing procedures. Only in this way can coherence and reliability be ensured throughout the data’s lifecycle.
The cultural challenge: beyond technology hype
Many organisations have embraced GenAI enthusiastically, encouraged by the prospect of a rapid competitive advantage. Nevertheless, without a solid governance base, projects soon come up against structural limitations, such as incomplete data sets, isolated systems, unrecognised bias and misunderstandings regarding regulations.
As no algorithm exists which can compensate for a poor quality or badly managed database, the evolution towards mature governance requires a radical cultural change: instead of focusing on immediate performance, the the aim must be to build trust, transparency and accountability.
GenAI can revolutionise the way in which organisations produce content, interact with clients and manage internal processes. This potential, however, also brings some vulnerabilities, such as the phenomenon known as ‘hallucination’: the generation of false or misleading content by models, usually when a system has been trained with ambiguous or unverified data.
Furthermore, given the opaque nature of many models, it is not easy to identify which data has influenced them or what rules they are following. If we also consider the possibility of improper use or eccessive reliance on automated answers, the need for a governance system which imposes limits, traceability and control mechanisms becomes very apparent.
A new framework: adaptive governance
In order to respond to the dynamics of GenAI, data governance must evolve from a rigid model to an adaptive structure. This basically means not only monitoring the use of the information itself, but predicting its effects, protecting against the risks and constantly updating data management criteria.
Adaptive governance involves technogies for monitoring data quality, smart classification systems and predictive anomaly detection models. It also requires the presence of a multidisciplinary team who can interpret the technical, legal and ethical implications in a carefully coordinated manner.
In a world where AI can influence medical, judicial, financial and political choices, trust becomes a central factor. Data governance is no longer limited to its technical aspect, it must also include ethical considerations. If citizens or clients perceive that their data is being used without respecting their privacy or for dubious reasons, resistance and mistrust are likely to be triggered.
It is therefore necessary to build transparent control systems, document the origin and use of the information and provide auditing tools which are easily accessible even to non-experts. Transparency therefore becomes a prerequisite for the legitimacy of artificial intelligence in social contexts.
The value of data governance as a competitive advantage
Organisations able to invest in intelligent, longterm governance will also be able to obtain more from emerging technologies. While others will be hindered by decision-making errors, regulatory issues or reputational crises, these companies will have access to a stable, responsible and (above all) scalable decision-making infrastructure.
It is not only a question of avoiding problems but also of freeing up new opportunities, such as:
- more reliable predictive models;
- customised services;
- greater operational efficiency;
- opening up to regulated markets.
Data governance can therefore be transformed from a restriction into a means of adding extra value, but in order to develop mature governance, organisations must rethink their own internal processes. It is essential to start by mapping the flows of information and individuals involved. This must be followed by policy definition, the adoption of automated control tools and the implementation of data quality and safety indicators.
Investment in skills is also required to train data analysts, compliance experts and managers who are required to take quantitatively based decisions. Training provided must become ongoing and transversal, not only to create individual specialists, but rather to spread shared awareness of the importance and power of data.
In the digital world, data is the new language used by companies to communicate, innovate and build relationships. Handling it with care, forethought and respect, is not only best practice, it has become a necessity. Data governance is what distinguishes mature companies from those who try to embrace technology without fully understanding it.
Today, adopting an efficient data governance strategy means building trust, ensuring fairness and facilitating responsible innovation. In an era dominated by GenAI, the real challenge is how to  govern data without being governed by it.
Translated by Joanne Beckwith
