Introduction:
Augmented Data Management (ADM) is a new approach to managing and processing data by integrating advanced technologies such as artificial intelligence (AI), machine learning (ML), automation, and natural language processing (NLP). ADM automates and enhances traditional data management processes, enabling organizations to optimize data operations, improve data quality, and streamline decision-making. This course will explore how ADM tools and techniques are transforming data governance, data quality, data integration, and metadata management. Participants will gain hands-on experience using cutting-edge ADM tools and methodologies to improve data management practices in their organizations.

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

• Understand the concept of augmented data management and its impact on traditional data management practices.
• Learn about the role of AI, ML, and automation in enhancing data governance, data quality, and integration.
• Explore the tools and technologies used in augmented data management for metadata management, data cataloging, and data profiling.
• Implement augmented data management strategies for real-time data processing and decision-making.
• Understand the challenges and opportunities in adopting ADM in an organization’s data strategy.
• Gain hands-on experience with key ADM tools for automating data workflows and improving data management efficiency.

Who Should Attend?
This course is designed for professionals involved in data management, IT, and business intelligence, including:

• Data architects and engineers.
• Data analysts and data scientists.
• IT managers and CTOs.
• Business intelligence and analytics professionals.
• Data governance and compliance officers.
• Anyone interested in leveraging AI and ML for improving data management processes.

Day 1: Introduction to Augmented Data Management

Morning Session:

• What is Augmented Data Management?

  • Definition and principles of augmented data management (ADM).
  • The evolution of data management: Traditional vs. augmented data management.
  • Benefits of ADM: Automation, improved data quality, efficiency, and cost reduction.
  • Key components of ADM: AI, ML, NLP, automation, and cloud integration.

• The Role of AI and ML in Data Management

  • Understanding AI and ML in the context of data management.
  • How AI/ML enhances data governance, data integration, and data quality management.
  • Examples of AI/ML use cases in data management: Data profiling, anomaly detection, and data cleansing.

Afternoon Session:

• Data Governance and Metadata Management

  • The importance of data governance in the era of augmented data management.
  • Metadata management in ADM: Data cataloging, data lineage, and impact analysis.
  • Implementing effective data governance strategies using augmented technologies.
  • Key tools and technologies for metadata management in ADM.

• Hands-On Lab: Exploring Metadata Management Tools

  • Overview of popular metadata management tools: Alation, Collibra, and Informatica.
  • Using metadata management tools to catalog and classify data.
  • Creating a data lineage map using ADM tools.

Day 2: AI and Automation in Data Integration and Quality

Morning Session:

• Automating Data Integration with ADM

  • Introduction to data integration in augmented data management: Real-time and batch processing.
  • The role of automation in data extraction, transformation, and loading (ETL) processes.
  • How AI and automation simplify data integration from diverse sources and formats.
  • Data pipeline automation: Managing data flows and integration with cloud platforms.

• Enhancing Data Quality with AI and ML

  • AI and ML for data quality management: Data profiling, validation, and cleansing.
  • Using AI algorithms to detect and correct data errors, duplicates, and inconsistencies.
  • Real-time data quality monitoring and automated data correction.

Afternoon Session:

• Automated Data Lineage and Data Validation

  • Understanding data lineage in the context of ADM.
  • Automating data lineage tracking and ensuring transparency across data workflows.
  • Validating data flows and maintaining data accuracy through automation.

• Hands-On Lab: Automating Data Integration and Quality Checks

  • Building an automated ETL pipeline using ADM tools.
  • Using AI-powered tools to monitor and improve data quality in real-time.
  • Implementing data lineage tracking and validation using metadata management software.

Day 3: Real-Time Data Processing and Advanced Use Cases

Morning Session:

• Real-Time Data Processing and Decision-Making

  • The role of augmented data management in real-time data processing.
  • Real-time data integration and its impact on decision-making.
  • How ADM enables predictive and prescriptive analytics in real-time.
  • Use cases of real-time data processing in finance, healthcare, and manufacturing.

• Advanced Use Cases of Augmented Data Management

  • Fraud detection and risk management using ADM.
  • Personalization and customer segmentation in marketing and sales.
  • Predictive maintenance in industrial operations using real-time data insights.

Afternoon Session:

• Data Privacy and Security in ADM

  • Ensuring data privacy and compliance in augmented data management systems.
  • Managing sensitive data and adhering to data protection regulations (GDPR, CCPA).
  • Security measures in automated data workflows: Encryption, access controls, and audit trails.

• Hands-On Lab: Real-Time Data Processing and Automation

  • Implementing a real-time data pipeline using cloud platforms (e.g., AWS, Azure).
  • Using ADM tools to implement real-time analytics and decision-making processes.
  • Applying security and privacy best practices in real-time data workflows.

Day 4: Advanced Data Governance and Ethical Considerations

Morning Session:

• Advanced Data Governance Techniques

  • How augmented data management enhances data stewardship and governance.
  • Creating data policies and automating compliance checks.
  • Data stewardship roles and responsibilities in the ADM framework.
  • Integration of ADM with enterprise governance frameworks (e.g., DAMA, COBIT).

• Ethical Considerations in Augmented Data Management

  • Understanding the ethical challenges of automating data processes.
  • Addressing bias and transparency in AI/ML models.
  • Ethical data use and responsible data management practices.

Afternoon Session:

• Implementing a Data Governance Framework with ADM

  • Building a robust data governance framework using augmented data management tools.
  • Ensuring data accountability, transparency, and compliance in automated environments.
  • Best practices for data privacy, ethics, and security.

• Hands-On Lab: Implementing Data Governance and Ethical Practices

  • Setting up a governance framework within an ADM tool.
  • Automating compliance checks for data quality, privacy, and security.

Day 5: Future of Augmented Data Management and Capstone Project

Morning Session:

• Future Trends in Augmented Data Management

  • The role of AI, big data, and cloud computing in the future of ADM.
  • Emerging trends: Self-service analytics, augmented data discovery, and AI-driven data preparation.
  • The impact of 5G, edge computing, and IoT on data management.

• The Impact of Augmented Data Management on Business

  • How businesses can leverage ADM to drive innovation, reduce costs, and improve decision-making.
  • Real-world success stories of organizations using ADM to optimize their data operations.

Afternoon Session:

• Capstone Project

  • Applying ADM concepts to a real-world business case: Data integration, data quality, and governance.
  • Group project: Design and implement an ADM solution for a specific industry or use case (e.g., healthcare, finance, or retail).
  • Presentation of the capstone project and group discussion.

• Final Q&A and Course Review

  • Recap of key concepts and strategies for implementing augmented data management.
  • Open Q&A session to address any remaining questions.

• Certification Exam

  • Final assessment to test participants’ understanding of the concepts, tools, and techniques covered in the course.

• Closing Remarks and Certification

  • Awarding certificates to participants who successfully complete the course.