Challenges with traditional approach:
Whilst traditional ML models are extremely powerful, there are numerous challenges.
• The Black-Box: Automated ML models are ‘black-boxes’ with a distinct lack of explainability on outputs relied upon for decision making.
• Trust the Machines: There is a lack of explainability which can lead to blind acceptance of Automated ML outputs. This is a concern in high-stakes areas.
• Correlation not Causation: ML models focus on predicting outcomes rather than understanding causality. A high level of correlation may be misleading and doesn’t necessarily identify the true cause. See our Oil and Gas case study as an example of this.
• Efficient Use of Data: Incorporating proven Causal analytics allows for more efficient use of smaller data sets meaning greater insights at less cost.
BDC adopts human-guided causal discovery, combining domain expertise with proven algorithms to uncover causality between data variables. This is no longer the case of "trust the machines’.
Our approach ensures data outputs are grounded in real-world cause-and-effect relationships, thereby enhancing decision-making intelligence.