Introduction:

The rapid growth of data in enterprises has led to challenges in managing storage costs, data security, compliance, and efficiency. Without a robust Data Lifecycle Management (DLM) strategy, organizations risk data sprawl, non-compliance penalties, and reduced operational effectiveness.

This course introduces modern DLM strategies to ensure data is accessible when needed, protected from threats, and disposed of securely when it becomes obsolete. Participants will explore DLM frameworks, automation tools, and industry standards (such as GDPR, HIPAA, ISO 27001, and NIST), ensuring they can implement best-in-class data governance and lifecycle processes.

Objectives:

By the end of this course, participants will be able to:

1. Understand the Data Lifecycle – Learn the phases of data lifecycle management and why it is crucial for business success.
2. Implement Data Classification and Retention Policies – Develop frameworks to categorize data based on value, sensitivity, and compliance needs.
3. Optimize Data Storage and Access – Explore best practices for tiered storage management, archiving, and retrieval strategies.
4. Secure Data Throughout Its Lifecycle – Apply encryption, access controls, and compliance frameworks to protect sensitive data.
5. Automate Data Lifecycle Processes – Leverage AI and automation for data retention, archival, and disposal.
6. Ensure Regulatory Compliance – Align DLM strategies with legal requirements such as GDPR, CCPA, and industry-specific mandates.
7. Develop a Future-Ready DLM Strategy – Adapt DLM policies to emerging trends, including cloud-native architectures and decentralized storage.

Who Should Attend?

This course is designed for professionals responsible for managing, securing, and governing enterprise data assets, including:

• Data Architects and Engineers
• IT and Data Governance Professionals
• Compliance and Risk Officers
• Database Administrators (DBAs)
• Business Intelligence and Data Analytics Teams
• Enterprise Storage Managers
• CIOs, CTOs, and IT Managers

Training Agenda:

Day 1: Introduction to Data Lifecycle Management

• Morning Session:

  • Understanding the Data Lifecycle: Creation, Usage, Storage, Retention, and Disposal
  • The Business Value of Data Lifecycle Management
  • Challenges in Data Growth, Storage Costs, and Compliance
  • Case Study: How Poor Data Lifecycle Management Led to Compliance Breaches

• Afternoon Session:

  • Data Classification Strategies: Categorizing Data Based on Business and Regulatory Needs
  • Mapping Data Lifecycles to Business Processes
  • Hands-on Exercise: Creating a Data Lifecycle Framework for an Organization

Day 2: Data Retention, Archiving, and Storage Optimization

• Morning Session:

  • Data Retention Policies: Balancing Business Needs with Compliance
  • Tiered Storage Management: Hot, Warm, and Cold Data Storage
  • Archival Strategies: When and How to Move Data to Cost-Effective Storage Solutions

• Afternoon Session:

  • Cloud vs. On-Premise Data Storage: Benefits, Challenges, and Cost Considerations
  • Implementing Data Lifecycle Automation with AI and ML
  • Hands-on Exercise: Designing a Data Retention and Archival Policy

Day 3: Data Security, Privacy, and Compliance Throughout the Lifecycle

• Morning Session:

  • Data Protection Strategies: Encryption, Masking, and Tokenization
  • Access Control and Identity Management in Data Lifecycle Management
  • Security Challenges in Data Retention and Disposal

• Afternoon Session:

  • Compliance Frameworks: GDPR, CCPA, HIPAA, ISO 27001, and NIST Standards
  • Auditing and Monitoring Data Access and Usage
  • Hands-on Exercise: Conducting a Data Compliance Audit

Day 4: Data Disposal, Deletion, and End-of-Life Strategies

• Morning Session:

  • Secure Data Deletion Methods: Wiping, Shredding, and Cryptographic Erasure
  • Data Disposal Compliance: Legal Requirements and Industry Standards
  • Managing End-of-Life for Cloud-Based and On-Premise Data

• Afternoon Session:

  • The Role of Blockchain in Data Integrity and Deletion Auditing
  • Automated Data Lifecycle Tools for Deletion and Cleanup
  • Hands-on Exercise: Developing a Data Disposal Plan for a Case Scenario

Day 5: Future Trends and Enterprise DLM Strategy Development

• Morning Session:

  • AI and Machine Learning in Data Lifecycle Management
  • The Impact of Edge Computing and IoT on Data Lifecycle Strategies
  • Cloud-Native and Serverless DLM Approaches

• Afternoon Session:

  • Building a Resilient and Scalable DLM Strategy for Enterprises
  • Final Capstone Project: Developing a Comprehensive Data Lifecycle Management Strategy
  • Course Wrap-Up, Q&A, and Certification of Completion