Description

This curriculum spans the design and operationalization of data governance frameworks across a nine-module sequence comparable to a multi-workshop organizational rollout, addressing the same governance challenges encountered in enterprise data mining and ML initiatives—from stakeholder alignment and policy enforcement to auditability and continuous improvement.

Module 1: Establishing Governance Objectives and Stakeholder Alignment

Define data ownership models by business unit versus functional domain to resolve accountability conflicts in cross-departmental data mining initiatives.
Negotiate data access thresholds between legal, compliance, and analytics teams when structuring permissible use cases for customer data.
Select governance KPIs (e.g., data accuracy rate, lineage coverage) that align with enterprise risk appetite and regulatory exposure.
Document data lineage requirements for auditability when integrating third-party data sources into predictive modeling pipelines.
Balance speed-to-insight demands from data science teams with data quality validation gates enforced by governance bodies.
Map regulatory mandates (e.g., GDPR, CCPA) to specific data handling rules within training and test datasets.
Establish escalation protocols for data policy violations detected during model development or deployment.
Conduct stakeholder workshops to prioritize data domains (e.g., customer, financial) for governance rollout based on business impact and risk.

Module 2: Designing Data Governance Structures and Roles

Assign Data Stewards to specific data assets (e.g., customer transaction logs) with documented authority to approve schema changes.
Implement a tiered governance council (executive, operational, technical) with defined decision rights for data classification and access.
Integrate data governance responsibilities into existing job descriptions for data engineers and ML ops engineers.
Resolve conflicts between centralized governance mandates and decentralized data science team autonomy through service-level agreements.
Design escalation paths for disputes over data definitions (e.g., “active customer”) used in model features.
Formalize the role of the Chief Data Officer in approving exceptions to data retention policies in model retraining workflows.
Define escalation criteria for data quality incidents that trigger governance board review during model lifecycle stages.
Establish quorum and voting rules for governance council decisions on data sharing between regulated and non-regulated business units.

Module 3: Data Classification and Sensitivity Management

Classify data elements in training datasets using sensitivity tiers (public, internal, confidential, restricted) based on PII and regulatory scope.
Implement dynamic data masking rules in development environments to prevent exposure of sensitive attributes during model prototyping.
Apply tokenization to personally identifiable information in historical datasets used for time-series forecasting.
Enforce encryption-at-rest policies for datasets containing health or financial information used in supervised learning.
Configure metadata tagging to automatically flag datasets containing high-risk fields (e.g., SSN, health diagnoses) for governance review.
Define data de-identification standards for external model validation using third-party vendors.
Implement automated scanning of data lakes to detect unauthorized storage of classified data in unapproved zones.
Update classification rules when new regulatory requirements (e.g., AI Act) introduce restrictions on biometric or behavioral data.

Module 4: Data Quality Frameworks for Analytical Workloads

Define data quality rules (completeness, consistency, timeliness) for input features used in churn prediction models.
Integrate data profiling into ETL pipelines to detect distribution shifts before model retraining.
Establish thresholds for missing data in training sets that trigger governance alerts or model freeze.
Implement automated data quality scoring for feature stores to assess reliability of candidate variables.
Document root cause analysis procedures for data anomalies detected during model performance monitoring.
Configure reconciliation checks between source systems and feature engineering outputs to ensure transformation integrity.
Set data freshness SLAs for real-time scoring systems based on upstream data pipeline latency.
Enforce referential integrity rules when merging external market data with internal customer records for segmentation models.

Module 5: Metadata Management and Data Lineage

Automate lineage capture from raw data sources through feature engineering to model output in MLOps pipelines.
Implement metadata standards (e.g., schema, update frequency, owner) for all datasets used in model training.
Integrate lineage tracking with model registries to support audit trails for regulatory submissions.
Map data transformations in Python notebooks to metadata repositories using code parsing tools.
Enforce metadata completeness checks before datasets are promoted to production model environments.
Visualize end-to-end data flow for high-impact models to support impact analysis during schema changes.
Configure metadata retention policies aligned with data lifecycle management and compliance requirements.
Link data lineage records to incident response workflows when model drift is traced to upstream data changes.

Module 6: Policy Development and Enforcement Mechanisms

Translate regulatory requirements into executable data policies (e.g., “no use of race in credit scoring features”).
Embed policy checks into CI/CD pipelines for ML models to prevent deployment of non-compliant code.
Define data retention schedules for model training artifacts based on legal hold requirements.
Implement role-based access control (RBAC) for model training datasets using centralized identity providers.
Create policy exception workflows with time-bound approvals and audit logging for urgent model development needs.
Enforce data usage logging at the query level to monitor access patterns in analytical sandboxes.
Integrate policy violation alerts with SIEM systems for centralized security monitoring.
Update data sharing agreements when models are deployed across international jurisdictions with conflicting regulations.

Module 7: Data Access Control and Provisioning

Implement just-in-time access provisioning for data scientists working on high-sensitivity projects.
Configure attribute-level access controls to mask specific fields (e.g., income) in customer datasets used for modeling.
Enforce approval workflows for access requests to datasets containing regulated health or financial data.
Integrate data access logs with user behavior analytics tools to detect anomalous query patterns.
Design secure data enclave environments for external collaborators working on joint modeling initiatives.
Apply data masking techniques (e.g., generalization, perturbation) when provisioning datasets for model validation.
Automate access revocation upon project completion or role change using HR system integrations.
Balance data discoverability with access restrictions by implementing searchable data catalogs with permission-aware results.

Module 8: Integration with Data Mining and ML Workflows

Embed data validation checks within feature engineering scripts to enforce governance rules at point of use.
Integrate data lineage tools with ML experiment tracking platforms (e.g., MLflow) for auditability.
Enforce model documentation standards that include data sources, transformations, and quality metrics.
Implement data drift detection mechanisms that trigger governance review before model retraining.
Standardize feature store governance to prevent duplication and ensure consistency across modeling teams.
Define data rollback procedures for models when upstream data corrections invalidate prior training sets.
Coordinate schema change management between data platform teams and data science teams to prevent pipeline breaks.
Establish data versioning protocols for training datasets to support reproducibility and model validation.

Module 9: Monitoring, Auditing, and Continuous Improvement

Deploy automated dashboards to track governance KPIs (e.g., policy compliance rate, steward response time).
Conduct quarterly audits of model training data against approved data usage policies.
Perform root cause analysis on data-related model failures to refine governance controls.
Implement automated alerts for unauthorized data access or policy violations in modeling environments.
Review data classification accuracy annually using sampling and manual validation.
Update governance playbooks based on findings from regulatory examinations or internal audits.
Measure time-to-resolution for data quality incidents impacting model performance.
Conduct maturity assessments to prioritize governance capability enhancements based on business risk.

Data Governance Frameworks in Data mining