fusion-bicc-data-drift-detect
Detect data drift within Oracle Fusion BICC extracted objects by comparing profile snapshots over time and rate findings with criticality and remediation guidance.
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- oracle-samples/fusion-ai-skills
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- 2026年9月4日 17:06
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- fusion-bicc-data-drift-detect
- description
- Detect data drift within Oracle Fusion BICC extracted objects by comparing profile snapshots over time and rate findings with criticality and remediation guidance.
- compatibility
- Requires Python 3.10+; optional PyYAML for YAML policy files; CSV input and JSON outputs.
- metadata
- {"author":"fusion-data-architecture","version":"1.0.0"}
- allowed-tools
- Bash(python:*) Read Write
## Copyright (c) 2026, Oracle and/or its affiliates.
## Licensed under the Universal Permissive License v 1.0 as shown at http://oss.oracle.com/licenses/upl
# Fusion BICC Data Drift Detection
Use this skill when schema appears stable but data behavior changes across extraction windows and can impact KPIs, model quality, or ODT load quality.
## When to use
- Null rates spike after a Fusion release.
- Distinct cardinality unexpectedly collapses or expands.
- Measure ranges/means shift materially.
- You need severity-rated drift findings with remediation actions.
## Inputs
- Baseline and current BICC CSV extracts.
- Data drift policy (`assets/data_drift_policy.yaml`).
## Workflow
1. Build baseline data profile snapshot from known-good extraction window.
2. Build current data profile snapshot for the latest run.
3. Run drift detection with policy thresholds.
4. Review critical findings and associated remediation in report outputs.
## Commands
```bash
# 1) Build profile snapshots
python skills/fusion-bicc-data-drift-detect/scripts/build_data_profile_snapshot.py \
--input-dir ./inputs/baseline \
--output ./outputs/baseline_data_profile.json
python skills/fusion-bicc-data-drift-detect/scripts/build_data_profile_snapshot.py \
--input-dir ./inputs/current \
--output ./outputs/current_data_profile.json
# 2) Detect data drift
python skills/fusion-bicc-data-drift-detect/scripts/detect_data_drift.py \
--baseline ./outputs/baseline_data_profile.json \
--current ./outputs/current_data_profile.json \
--policy skills/fusion-bicc-data-drift-detect/assets/data_drift_policy.yaml \
--output-json ./outputs/data_drift_report.json \
--output-md ./outputs/data_drift_report.md
```
## Outputs
- `data_drift_report.json`
- `data_drift_report.md`
## Critical examples
- Required field null ratio exceeds critical threshold.
- Row count drops below expected freshness/completeness threshold.
- Categorical domain shifts beyond policy tolerance.
See `references/data-drift-metrics.md` and `references/severity-model.md`.
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