Big Data Casuality

Our Services

Causal Discovery

BDC's causal discovery combines client domain expertise with proven algorithms to uncover causality between your data variables. This leads to strong explainability of data outputs and enhances decision-making intelligence to drive enterprise-wide improvements.
Our Casual Engine’s algorithms move beyond identifying pure correlations (which can be misleading) and identifies all the causal relationships from within.

Benefits of Causal Discovery:

BDC's Causal AI Engine allows a company to identify and understand cause-and-effect inferences in their data:
- Revalidating known Causal relationships, and,
- Discovering unknown Causal relationships which can provide immense value.
This is the real value, and once you know the Cause, you can begin to remedy and predict the Effect.
Trusted and reliable outputs generate new insights leading to increased profits, potential increased workplace safety and sustainability outcomes, system efficiencies, and enhanced decision-making intelligence.

Causal Mapping

BDC’s causal consulting service whereby we work alongside our clients to develop and incorporate causal mapping and modelling into their data strategy and objectives.

Why perform Causal Mapping?

Through our advanced causal algorithms, Causal Mapping enables a comprehensive causal quantification and enhanced insights. This provides a clearer understanding of the causal mechanisms improving the context and explainability of your data. This is vital when basing decision making off data outputs.

Benefits of Causal Mapping:

BDC's causal mapping combines the client's domain expertise and BDC's proprietary algorithms to validate new or existing data analytics solutions.
The value in implementing causal mapping is that you are beginning with a cause-and-effect premise and not a correlation-based solution.
Causal mapping delivers informed and reliable data outputs enhancing decision making intelligence.