modellingcentric

modellingcentric is a term used to describe an approach in software development and data science where the focus is placed heavily on the creation, refinement, and utilization of models. This means that the core activities revolve around building representations of systems, processes, or data, and then using these models to understand, predict, or control outcomes. In contrast to code-centric or process-centric methodologies, a modelling-centric approach prioritizes the conceptual and structural aspects of the problem being solved.

This paradigm shift can be observed in various fields. In software engineering, it might manifest as a strong reliance on model-driven architecture (MDA) or domain-specific languages (DSLs), where models are the primary artifacts from which code is generated. In data science and machine learning, a modelling-centric approach emphasizes the selection, training, and evaluation of predictive or descriptive models. The model itself, rather than the specific implementation details of its algorithms, becomes the central object of attention and improvement.

The benefits of a modelling-centric approach often include improved communication between stakeholders, enhanced maintainability of systems, and a clearer understanding of complex domains. By abstracting away from low-level implementation details, teams can focus on the essential logic and structure of their solutions. However, it also requires specialized skills in modelling languages and tools, and can sometimes lead to challenges in integrating models with existing codebases if not managed carefully.