Data analysis/data analytics services

To handle large amounts of data, it is necessary to confront various obstacles and risks. If you have any concerns, please leave it to Neutral!

We have started an analysis service that clarifies the causal relationships in data by implementing the LiNGAM (linear non-Gaussian acyclic model) model into our in-house tools. The necessity of statistical causal exploration Why causality? When you want to take some action based on insights gained from data analysis, relying solely on predictions is often insufficient. Generally, data analysis tends to focus on building models that explain phenomena well, which are then used for predictions, such as forecasting sales for the next month or predicting harvest yields. What are the goals of data analysis that recent users are seeking? It is important to know the next actions to take (such as advertising strategies, marketing strategies, etc.). Traditional statistical causal exploration Statistical causal exploration is not an entirely new concept, but it is very challenging within data analysis and is not something that can be used like regression analysis. The initial task is to set assumptions, and at this stage, it is necessary to create a causal diagram (DAG) using experiences from observational data. In other words, it is not a magic trick where causal relationships are automatically derived.

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