Start with the use case, not the data
We use mathematical modelling to put your business problems squarely into focus. This method can be applied to anything from the strategic allocation of your advertising budget across marketing channels to technical IoT issues. You could, for example, use forecasting to determine the optimal operational mode for a physical or machine process for which you already have a great deal of domain-specific knowledge.
Modelling means understanding
We begin by talking to your domain experts in order to fully understand the use case in question. We then use the insights gained to analyse the existing data and formulate a comprehensive mathematical model. This model covers everything from the cost functions (KPIs) to be optimised to the use case’s underlying mechanisms and relationships.
Advantages of mathematical models
Causality trumps correlation
Mathematical modelling is particularly suited for supporting use cases which require the understanding of causal mechanisms, such as generating recommendations for marketing activities. Our models allow us to go beyond mere predictions to provide practical (prescriptive) insights for action. This method adds a great deal of value compared to machine learning methods, which are often based on pure correlation.
Data efficiency is crucial — even for big data
As mathematical models rely heavily on domain knowledge, they are significantly more data-efficient than other types of models. Even though we live in the big-data era, labelled data is often scarce. Especially in industrial use cases, data is often highly imbalanced or has low variance. We can use specialised methods to represent and consistently estimate hierarchically structured data, as well as censored, truncated, or zero-inflated distributions.
This is in contrast to traditional machine-learning approaches, which learn these structures solely from the data fed to the model (without the benefit of domain-specific knowledge) and which are, therefore, much more data-greedy.
Transparent, interpretable, and explainable
By design, mathematical models focus on interpretability and explainability. The models’ mechanics are therefore transparent, allowing for much easier detection of biases and discrimination. This becomes particularly important when we want to use a model to support high-stakes decisions in sensitive areas, such as medicine, justice, security, or vehicle-related applications. Mathematical models also outperform machine-learning methods when it comes to extrapolation tasks, such as predicting behaviour outside the range of the data already observed.
Partnering with inovex
Choosing inovex as your partner enables you to benefit from our broad experience in different fields of mathematical modelling, such as causal modelling, hierarchical modelling, Bayesian methods or uncertainty quantification.
We would recommend beginning our collaboration with one of our workshops, where we can discuss your use case and available data in order to determine the appropriate approach.