What is data lineage in decisioning systems?

Data lineage in decisioning systems refers to the detailed tracking of how data flows and transforms as decision logic is executed. It’s essential in systems that automate decision-making, especially when decisions must be transparent, auditable, and optimized for performance. Understanding data lineage means capturing the complete life cycle of data as it travels through and is modified by various decision-making components.

Key aspects of data lineage in decision logic execution

1. State tracking across decision steps:

   • In a decision flow, each step in the process is recorded with its corresponding state. This means that for every action or decision made, the system preserves a snapshot of the data object, including any deltas—changes or updates—that were introduced at that point.
   • By recording these data states, the system ensures that you have a precise and comprehensive history of how the data evolved throughout the decision-making process.

2. Artifacts used in decision flows:

   • Modern decision systems can involve a variety of artifacts that interact with the data. These artifacts include:
     • Rulesets: Collections of predefined rules that apply specific logic based on the data context.
     • Data sources: External or internal databases and information feeds that provide necessary input or enrichment.
     • Scorecards: Tools that aggregate different data points into scores, often used in risk or credit assessment.
     • Machine learning models: Predictive models that analyze data and provide outputs that influence decisions.
     • Decision tables: Structured tables that map out different outcomes based on input criteria.
     • Decision trees: Hierarchical models that map data inputs to various outcomes using a tree-like structure.
     • Mathematical functions: Computations and transformations that perform calculations on data to derive values needed for the decision.

How data lineage works in practice

When a decision flow executes, the system records every interaction between data and the artifacts mentioned. Here’s how data lineage captures this process:

• Initial data state: The flow begins with an initial data object sourced from input data. This data might include raw values such as a user's credit score, transaction history, or personal details.
• Step-by-step recording: As the data passes through each decision step, the system logs the current state and any updates applied. For example, if a scorecard is used to calculate a risk score, the lineage would document the original data, the calculation performed, and the resulting score.
• Delta changes: At each step, any change in the data object is recorded as a delta. This could be a new field added, an existing value modified, or a calculated score introduced. By maintaining a clear record of these deltas, the system provides a transparent view of how each decision or transformation affected the data.
• Artifact influence: The system also tracks which artifact influenced the data at each point, whether it was a ruleset that filtered out certain records or a machine learning model that generated a prediction. This level of tracking allows for detailed auditing and performance analysis.

Why data lineage matters

1. Transparency and explainability: In environments where decision outcomes must be explained (such as finance or healthcare), data lineage provides a clear audit trail. It shows exactly how data was used and transformed, offering a reliable explanation for the final decision.
2. Error diagnosis and resolution: If an incorrect decision is made, data lineage allows analysts to backtrack and identify the specific step or artifact where the error occurred. This simplifies troubleshooting and ensures more robust system performance.
3. Regulatory compliance: Many industries require clear documentation of how decisions are made, especially when those decisions impact consumers or involve sensitive data. Data lineage ensures that the decision process is well-documented and can be reviewed by regulatory bodies if necessary.
4. Impact analysis for optimization: When updating or optimizing decision logic, understanding data lineage helps in evaluating how changes will affect downstream outcomes. It provides a clear picture of dependencies and the potential ripple effects of modifying a ruleset or updating a machine learning model.

Conclusion

In systems where decision flows are built with complex logic, capturing data lineage is crucial. Each decision step holds a snapshot of data states, along with any changes introduced, ensuring the entire process is well-documented and auditable. By leveraging rulesets, scorecards, models, and other decision artifacts, these systems maintain transparency and efficiency, making them powerful tools for automated and data-driven decision-making.