Data Retention Framework

Enabling GDPR-Compliant, Automated Data Lifecycle Management

Introduction

In today’s data-driven world, organizations are not only responsible for collecting and processing data—they are also accountable for how long they retain it and when they delete it.

Regulations such as GDPR require strict control over data retention, especially for personally identifiable information (PII). Yet, most organizations rely on manual processes, ad hoc scripts, or incomplete policies to manage data lifecycle.

At Datilis, we address this challenge with a Data Retention Framework—a scalable, automated solution that ensures data is retained, managed, and deleted in full compliance with regulatory and business requirements.

The Problem: Data Retention is Complex and Risky

Organizations typically struggle with:

• Lack of centralized retention policies
• Inconsistent handling of PII across systems
• Manual deletion processes prone to errors
• Difficulty responding to customer deletion requests (GDPR “right to be forgotten”)
• No traceability of what data was deleted and why

This leads to:

• Regulatory risk and potential fines
• Data over-retention (increased storage and risk exposure)
• Operational inefficiencies
• Loss of customer trust

The Datilis Approach: Retention as a Framework

Our solution transforms retention from manual processes into a configurable, automated framework embedded in your data platform.

Core Principles

1. Metadata-Driven Retention Policies

Retention rules are defined directly within your data models (e.g., dbt):

• Retention column (e.g., timestamp field)
• Retention period (e.g., 14 days)
• PII classification and tagging

Example:

data_retention:
  data_retention_column: customer_id
  data_retention_days: 14

This ensures:

• Policies are transparent and version-controlled
• Retention is aligned with data definitions
• No hidden logic in scripts

2. Automated Retention Job Generation

Using metadata and tags, the framework automatically:

• Identifies tables requiring retention enforcement
• Generates the necessary data pileines
• Applies consistent deletion logic

This eliminates:

• Manual SQL scripting
• Inconsistent retention logic
• Human error

3. Scheduled Deletion Workflows

Retention jobs run automatically (e.g., nightly):

Example logic:

DELETE FROM customer_tbl
WHERE customer_ingest_ts < CURRENT_DATE - INTERVAL '14 days'

Business impact:

• Continuous compliance
• No manual intervention
• Predictable data lifecycle

Key Use Cases

🔹 Use Case 1: Time-Based Retention (Automated Cleanup)

Scenario:
Data must be deleted after a defined retention period.

How it works:

• PII data is labeled and tagged
• Retention rules defined in metadata
• Pipeline jobs execute deletion queries daily

Outcome:

• Fully automated lifecycle management
• Reduced storage and risk exposure

🔹 Use Case 2: Customer-Initiated Deletion (GDPR Compliance)

Scenario:
A customer requests deletion of their personal data.

How it works:

1. Customer IDs are collected in a staging table
2. Framework identifies all datasets containing PII
3. Deletion jobs are generated dynamically
4. SQL updates/delete operations are executed across all relevant tables

Example:

UPDATE customer_raw
SET customer_name = NULL
WHERE customer_id IN (...)

Outcome:

• Full compliance with “right to be forgotten”
• Consistent deletion across all systems
• Audit-ready processes

Architecture Overview

The Data Retention Framework integrates seamlessly into your data platform:

Step 1: Metadata Definition (dbt)

• Retention rules defined in models
• PII columns tagged

Step 2: Retention Job Generation

• Dagster scans metadata
• Generates deletion workflows

Step 3: Execution Layer

• SQL-based deletion or anonymization
• Runs on data platform (e.g., BigQuery, Hive)

Step 4: Data Platform Impact

• Data cleaned automatically
• Retention policies enforced consistently

Integration with Data Platform

The framework works directly on your data platform:

• BigQuery
• Hive / HBase
• Any SQL-compatible system

It ensures:

• No data duplication
• No external processing needed
• Native performance and scalability

Key Capabilities

Centralized Retention Governance

• Single place to define policies
• Aligned with data models

Automated Deletion Workflows

• No manual scripts
• Fully orchestrated

PII-Aware Processing

• Identify sensitive data automatically
• Apply correct retention rules

Auditability & Traceability

• Track what was deleted and when
• Support compliance audits

Flexible Retention Strategies

• Time-based deletion
• Event-based deletion (customer request)
• Anonymization support

Business Benefits

Organizations implementing this framework achieve:

• Full GDPR compliance readiness
• 80% reduction in manual retention effort
• Reduced risk of regulatory fines
• Lower storage costs through controlled retention
• Increased customer trust

Strategic Value

Data retention is not just compliance—it is:

• Risk management
• Cost optimization
• Trust enablement

By embedding retention into your data platform, you create:

A governed, compliant, and future-proof data ecosystem

Why Datilis

Datilis delivers:

• Deep expertise in data governance and platform engineering
• Framework-driven approach (not one-off solutions)
• Integration with modern tools (dbt, Dagster, Spark)
• Focus on compliance + scalability + automation

Conclusion

With increasing regulatory pressure and growing data volumes, organizations must rethink how they manage data lifecycle.

The Datilis Data Retention Framework ensures that:

• Data is kept only as long as needed
• Sensitive data is handled responsibly
• Compliance is automated and auditable

Next Steps

• Assess current retention policies
• Identify PII-heavy datasets
• Implement metadata-driven retention rules

Contact Datilis to implement your GDPR-compliant data retention framework