Introduction

As businesses generate more data, managing and utilizing it efficiently becomes increasingly complex. Traditional data management techniques can often fall short in terms of speed, scalability, and adaptability. Augmented Data Management (ADM) is a modern approach that combines AI, machine learning, automation, and advanced analytics to optimize data governance, integration, quality, and accessibility. This course will guide participants through the concepts, tools, and techniques needed to implement and leverage augmented data management for more efficient, scalable, and intelligent data operations.

Objectives

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

• Understand the principles and benefits of Augmented Data Management in the context of modern data ecosystems.
• Leverage AI-powered tools and machine learning algorithms to automate data integration, cleansing, and transformation tasks.
• Enhance data governance and data quality management using augmented technologies.
• Utilize natural language processing (NLP) and machine learning to improve data discovery, lineage, and categorization.
• Implement advanced analytics and predictive data management techniques to gain actionable insights from data.
• Integrate automation into data pipelines for real-time data processing and reporting.
• Manage and optimize metadata and data catalogs using augmented technologies.
• Build scalable and intelligent data management frameworks for data-driven decision-making.

Who Should Attend?

This course is ideal for:

• Data engineers and data architects who are looking to enhance their skills in managing and automating data workflows.
• Business intelligence (BI) professionals and data analysts who want to understand how augmented technologies can improve data analysis and insights.
• Data governance and compliance officers who are interested in implementing AI and automation for data quality and privacy management.
• IT professionals and data managers seeking to implement intelligent, scalable data management practices.
• Chief Data Officers (CDOs) and data-driven business leaders who need to understand the strategic impact of augmented data management technologies.

Course Outline

Day 1: Introduction to Augmented Data Management (ADM)

• What is Augmented Data Management?: Overview of ADM, its role in modern data ecosystems, and how it differs from traditional data management techniques
• Key Technologies Driving ADM: AI, machine learning, automation, and advanced analytics in data management
• The ADM Lifecycle: Data integration, quality management, governance, lineage, and security in the context of augmented data management
• Benefits of Augmented Data Management: Faster data integration, improved data quality, enhanced data governance, and smarter decision-making
• Real-World Use Cases: Examples of organizations successfully implementing ADM for better data management
• Tools and Technologies for ADM: Overview of popular tools like IBM Watson, Informatica, and Talend that support augmented data management
• Hands-on Session: Exploring an ADM Toolset for Data Integration and Transformation
• Case Study: How AI and Automation Transformed Data Governance for a Global Retailer

Day 2: AI-Powered Data Integration and Transformation

• Data Integration Challenges: Common problems in traditional data integration (complexity, latency, scalability)
• AI and Machine Learning in Data Integration: Leveraging AI to automate and optimize data extraction, transformation, and loading (ETL) processes
• Data Cleansing and Quality Enhancement with AI: How machine learning models can identify and correct data quality issues such as duplicates, errors, and missing values
• Automating Data Transformation: Using AI-driven transformations to convert data into usable formats for analytics
• Data Integration in Real-Time: Building real-time data pipelines with AI and automation for quick insights
• Best Practices for Integrating AI into Data Workflows: Ensuring data governance, compliance, and security in AI-driven data processes
• Hands-on Session: Building an AI-Powered Data Pipeline for Real-Time Data Integration
• Case Study: How an AI-Driven Data Integration System Improved Real-Time Decision-Making in Financial Services

Day 3: Data Governance, Metadata, and Data Catalogs

• Data Governance and Compliance in the Age of AI: How augmented technologies can streamline data governance, ensuring data privacy, security, and compliance with regulations like GDPR
• AI for Data Lineage: Tracking and visualizing the flow of data from source to destination using AI-powered tools
• Metadata Management: Understanding how AI and machine learning enhance metadata collection, management, and optimization
• Building Intelligent Data Catalogs: Using AI to automatically categorize, tag, and organize data for easy discovery and access
• Automated Data Auditing: Implementing AI tools to track and log data access and modifications for auditing purposes
• Ensuring Data Quality with AI: Using predictive models and AI-driven algorithms to identify data issues before they impact the organization
• Hands-on Session: Implementing AI for Data Lineage and Metadata Management in an ADM System
• Case Study: How AI-Powered Data Governance Improved Data Quality for a Healthcare Organization

Day 4: Advanced Analytics and Predictive Data Management

• Predictive Data Management: Using machine learning and advanced analytics to predict data trends and manage data growth
• Predictive Data Quality: Leveraging AI to foresee and prevent data quality issues such as errors, anomalies, and data corruption
• AI for Data Profiling and Pattern Recognition: Automatically identifying patterns and trends in large datasets using machine learning
• Automating Data Classification and Tagging: Using AI to classify and tag data based on patterns, making it easier to manage and analyze
• Leveraging AI for Data Security: Implementing AI-driven tools to detect and prevent potential data breaches or vulnerabilities
• Real-Time Predictive Analytics: Building real-time dashboards and insights that use predictive analytics to inform business decisions
• Hands-on Session: Creating a Predictive Data Management System Using AI
• Case Study: How Predictive Analytics Transformed Customer Insights for a Telecom Company

Day 5: Implementing and Scaling Augmented Data Management Solutions

• Scalability in Augmented Data Management: How to design scalable data management solutions that can handle growing volumes of data
• AI-Driven Automation at Scale: Building large-scale AI-driven data pipelines, automating data processing, and improving operational efficiency
• Optimizing Data Workflows: Using AI and machine learning to continually optimize data workflows and improve performance
• Implementing ADM in the Cloud: Deploying augmented data management solutions in cloud environments (AWS, Azure, Google Cloud)
• Monitoring and Maintaining AI-Driven Data Management Systems: How to evaluate, monitor, and retrain AI models for continuous improvement
• Adopting ADM Across the Organization: Building a strategy for scaling and integrating augmented data management throughout the organization
• Best Practices and Future Trends: How to stay ahead with emerging technologies like AI-powered data observability and continuous learning systems
• Final Project: Designing an Augmented Data Management Framework for a Large Organization
• Hands-on Session: Deploying an ADM System with AI-Powered Automation in the Cloud