Xplain Data Gmbh | Discover Causality

Uncover cause-and-effect relationships in your “Real World Data” with our Causal AI algorithms. Insights into causal relationships are key to making smart decisions & driving more intelligent and effective analytics interventions. What we do * Development of innovative technologies for Causal ML & AI * Identify potential causal relationships in “Real World Data” (Causal Discovery) * Discover causes (vs. correlations) for failures or (un)wanted outcome/effects in complex data * Enable customers to leverage these insights for better decision making & intelligent interventions Your business is focused around a specific object – it might be the “customer”, the “patient”, or a technical device, like the “machine” or the “car”. No matter what the central focus of your business is, understanding it holistically - meaning, with all information available - is vital to your business’ success. That, however, is exactly where todays databases and analytical technologies perform poorly. Databases are meant for managing complex data, but they split an object into atomic parts and store them in different tables. Once distributed across many tables, it is extremely difficult to analyse the object “as a whole”. Many companies have come to realise that even though they have “all the information” in their database, they are still not able to analyse their business objects holistically. With our object-centric data store, Xplain Data allows you to analyse your complete object of interest. This holistic view is in particular important to understand cause-and-effect relationships in real world data – which is facilitated by our Causal Discovery approach. Visit our website to learn why correlation does not imply causation, what the difference is between Predictive Models and Causal Discovery, and why understanding cause-and-effect is vital to intelligently manage your business.