Building a Cleaner Data Ecosystem - From Data Swamps to Data Lakes

Overview

Discover practical strategies to transform messy data swamps into organized, high-value data lakes through quality controls and automation, enabling reliable AI-ready data streams.

Syllabus

• Introduction to Data Ecosystems
Understanding Data Swamps vs. Data Lakes
Importance of a Clean Data Ecosystem for AI
• Assessing Data Quality
Identifying Data Quality Issues
Metrics for Data Quality Measurement
• Strategies for Data Cleaning
Data Profiling Techniques
Data Standardization and Consistency
De-duplication and Error Correction
• Designing Quality Control Frameworks
Establishing Data Governance Policies
Implementing Data Quality Checks and Audits
Role of Metadata Management
• Automation in Data Transformation
Tools and Technologies for Automated Data Cleaning
Workflow Automation for Continuous Data Quality
• Transitioning from Data Swamps to Data Lakes
Data Architecture for Building Data Lakes
Migrating and Integrating Data
Ensuring Scalability and Flexibility
• Ensuring Data Security and Compliance
Data Privacy Regulations and Compliance
Techniques for Securing Sensitive Data
• Operationalizing AI-Ready Data Streams
Establishing Data Pipelines for AI
Monitoring and Maintaining Data Ecosystems
• Case Studies and Industry Applications
Real-world Examples of Data Lake Implementations
Success Stories and Lessons Learned
• Future Trends in Data Ecosystem Management
Emerging Technologies and Their Impact
The Evolving Role of Data Lakes in AI
• Course Wrap-up and Final Assessment
Key Takeaways and Best Practices
Evaluating Participants through Practical Assignments

Subjects

Business