In a context of growing technological innovation, where digital systems are becoming increasingly complex, competences in requirements engineering and data analysis play a central role. At CCG/ZGDV, they are essential to ensure that the solutions developed effectively meet the needs of users and organisations. Rui Ribeiro, Coordinator and Researcher in the Software Engineering and Data Intelligence Department at CCG/ZGDV, shares the importance of these competences, their sectoral applicability, and the vision for the future in this strategic area.
 

How do you explain the competences of Requirements Engineering and Decision Support, combined with Predictive and Prescriptive Analytics, in the CCG/ZGDV context?

These areas combine rigorous methodologies for gathering, specifying, and validating requirements with advanced data analysis techniques, with the aim of supporting well-founded decision-making. In the  CCG/ZGDV context, these competences are fundamental to the success of the projects I am involved in. We often work with heterogeneous data integration, machine learning algorithms, and decision support systems – all of which require a structured and systematic approach. Projects such as TexBoost, Connect@Fashion, PPC 4.0, Health from Portugal and Be.Neutral are good examples of how this combination of competences is essential to ensuring scalable, efficient solutions with a strong focus on user experience. This consolidation stems from experience in complex industrial and urban contexts, where collaboration with multidisciplinary teams and the need to adapt to different environments make the requirements engineering process even more critical. This is where the importance of continuous monitoring and data analysis from systems comes into play.

 

What is the applicability of this competence and in which sectors is it manifested?

These competences are incredibly versatile and have applicability in various sectors where data-driven decision-making is crucial for success and innovation.

• Digital Health (eHealth): They are essential for defining requirements that support personalised care, ensuring interoperability with technical standards, and developing systems that assist medical decision-making based on data. In the Health from Portugal project, we faced precisely the challenges related to the integration of clinical data to improve the quality of healthcare decisions.
• Manufacturing Industry: In the TexBoost and Connect@Fashion projects, these competences made it possible to predict material properties, optimise production processes, and create interfaces and APIs that effectively communicate analytical results to users.
• Smart Cities and Mobility: In initiatives such as Be.Neutral and CityCatalyst, well-structured requirements definition and decision support are crucial in complex systems involving sensors, urban data, and multiple stakeholders. This enables more collaborative and informed decisions.
• Education: These competences also play an active role in teaching. I teach curricular units such as Software Processes and Methodologies, and Data Science and Artificial Intelligence, with the aim of training professionals capable of developing user-oriented and data-supported systems.

What is the vision for the future of this competence?

I foresee a dynamic and challenging future for these competences, as digital systems become more complex and social and regulatory demands increase. I highlight five major trends:

1. Dynamic and Evolving Requirements. Systems will be increasingly adaptive. This will require requirements to be continuously updated based on real data and user behaviour, supported by real-time monitoring tools.
2. Integration between Technical and Business Domains. Professionals in the field will act as bridges between technology and strategic objectives, ensuring that the systems developed are aligned with the needs of the user and the organisation.
3. Transparency and Reliability of Requirements. It will be essential to ensure that systems are auditable, transparent, and that data-supported decisions are explainable, which is crucial for user and regulator trust.
4. Intelligent Automation. Artificial intelligence will be used to automate tasks such as eliciting and validating requirements, freeing up time for activities of greater strategic value.
5. Simulation and Digital Twins. Validating requirements in simulated environments, through digital twins, will become increasingly common. This will make it possible to test solutions before implementation, increasing their robustness.

In short, I firmly believe that these competences will become ever more central to the development of reliable, robust, and user-centred digital systems. I will continue to invest in their advancement, both through applied research and by training new professionals capable of tackling the challenges ahead.