Certified Data Management Professional (Cdmp) - Associate

Master the Core Principles of Data Management and Prepare for the CDMP Associate Certification

What you'll learn

Master the core principles of data governance and the roles and responsibilities involved.

Understand the DAMA-DMBOK framework and its importance in data management.

Learn the structure and certification levels of the CDMP exam.

Explore strategies for preparing and studying for the CDMP certification exam.

Grasp the foundational concepts of data architecture, including logical and physical data models.

Design scalable data systems that meet organizational needs.

Gain a deep understanding of data modeling, including normalization and denormalization techniques.

Develop expertise in data storage models, data retention, backup, and recovery strategies.

Understand the principles of data security and how to mitigate risks in data management.

Implement best practices for ensuring data quality and improving data integrity.

Explore the concepts of master and reference data management and their role in data consistency.

Learn the fundamentals of metadata management and how it enhances data accessibility and governance.

Understand the role of data warehousing and business intelligence in strategic decision-making.

Explore emerging technologies like AI and big data and their impact on data management.

Dive into cloud data management and its benefits for scalable and secure data storage.

Understand ethical data management practices and how to ensure regulatory compliance.

Requirements

No Prerequisites.

Description

This course offers an in-depth exploration of the core principles and frameworks surrounding data management, with a specific emphasis on preparing students for the CDMP (Certified Data Management Professional) certification. The course is designed to provide a comprehensive overview of the various aspects of data management, including governance, architecture, modeling, security, quality, and more. While the course encompasses the theory of these data management concepts, it also provides valuable insights into how they can be applied in real-world scenarios, making it an essential resource for those looking to deepen their understanding of data management or prepare for the CDMP exam.Beginning with an introduction to the CDMP certification process, students will gain a detailed understanding of the certification levels, exam structure, and essential study strategies. This foundational knowledge not only prepares students for the certification itself but also provides a solid framework for comprehending the broader field of data management. In particular, students will appreciate the subtle focus on theoretical aspects that underpin data management, allowing them to explore the key concepts without the distraction of immediate hands-on applications.The course delves into data governance, one of the most crucial pillars of effective data management. Students will examine the roles and responsibilities that come with governance, as well as the policies, procedures, and frameworks that support a strong data governance strategy. Understanding governance frameworks is essential for ensuring that data remains secure, accurate, and compliant with industry standards. Students will learn how governance ties into the overall architecture of data systems and how it forms the backbone of a sustainable data management strategy.Next, the course takes a closer look at data architecture, providing insights into how data is structured, modeled, and managed across an organization. Key concepts such as logical versus physical data models and the principles of designing scalable data systems are explored in detail. Students will also study enterprise architecture and its integration with data management practices, which is crucial for organizations aiming to align their data systems with strategic business goals. This section encourages students to think critically about the theoretical models that shape modern data architecture and how these models can be adapted to meet an organization’s unique needs.Data modeling and design are fundamental to ensuring that data is both useful and efficient in meeting organizational objectives. The course covers essential topics such as normalization, denormalization, and data relationships, providing students with the knowledge needed to design and optimize data models for various industries. In doing so, students will gain an understanding of best practices in data design, with an emphasis on conceptual, logical, and physical data models, further cementing their grasp of data management theory.Students will also explore the intricacies of data storage and operations, including storage models, techniques, and policies for data retention, backup, and recovery. The importance of data security management is also highlighted, focusing on principles, policies, and strategies for mitigating risks and ensuring regulatory compliance. In today’s digital age, where data breaches and cybersecurity threats are constant concerns, understanding these security principles is vital for anyone working in data management.Furthermore, the course covers essential topics such as data quality management, metadata management, and reference and master data management. Each of these areas contributes to the overall goal of maintaining high standards of data integrity, accessibility, and usability. By the end of these sections, students will be equipped with the knowledge to assess and improve data quality, manage metadata repositories, and ensure that master and reference data are handled efficiently.As the course progresses, students will learn about data warehousing and business intelligence, which are critical for leveraging data in decision-making processes. The course also addresses emerging trends in data management, including the role of big data, artificial intelligence, and cloud technologies, which are reshaping the future of data systems.In summary, this course offers a thorough examination of data management principles with a focus on preparing students for CDMP certification. Through its structured approach to theoretical concepts, students will build a robust foundation in data management, which can be applied to a wide range of professional settings. Whether you are new to the field or looking to formalize your expertise, this course provides the essential knowledge and tools needed to excel in the dynamic and evolving world of data management.

Overview

Section 1: Course Resources and Downloads

Lecture 1 Course Resources and Downloads

Section 2: Introduction to CDMP and Data Management

Lecture 2 Section Introduction

Lecture 3 Overview of CDMP Certification

Lecture 4 Case Study: Empowering Data Management Careers

Lecture 5 Introduction to Data Management

Lecture 6 Case Study: Enhancing RetailHub's Data Management

Lecture 7 The DAMA-DMBOK Framework

Lecture 8 Case Study: Transforming Data Management at TechNova

Lecture 9 Certification Levels and Exam Structure

Lecture 10 Case Study: Achieving the CDMP

Lecture 11 Study Strategies for the CDMP Exam

Lecture 12 Case Study: Strategic Mastery

Lecture 13 Section Summary

Section 3: Data Governance

Lecture 14 Section Introduction

Lecture 15 Introduction to Data Governance

Lecture 16 Case Study: Empowering RetailNet

Lecture 17 Governance Roles and Responsibilities

Lecture 18 Case Study: Enhancing Data Governance

Lecture 19 Data Stewardship and Accountability

Lecture 20 Case Study: Implementing Data Governance at NexFinance

Lecture 21 Policies, Procedures, and Standards

Lecture 22 Case Study: TechCo's Data Governance Overhaul

Lecture 23 Governance Frameworks and Best Practices

Lecture 24 Case Study: Implementing Data Governance Frameworks

Lecture 25 Section Summary

Section 4: Data Architecture

Lecture 26 Section Introduction

Lecture 27 Introduction to Data Architecture

Lecture 28 Case Study: Data Architecture Excellence

Lecture 29 Data Architecture Principles

Lecture 30 Case Study: Transforming Data Architecture

Lecture 31 Logical vs. Physical Data Models

Lecture 32 Case Study: Optimizing CRM Data Management

Lecture 33 Enterprise Architecture and Data Management

Lecture 34 Case Study: Strategic Integration of Enterprise Architecture and Data Management

Lecture 35 Designing Scalable Data Systems

Lecture 36 Case Study: Balancing Vertical and Horizontal Scalability

Lecture 37 Section Summary

Section 5: Data Modeling and Design

Lecture 38 Section Introduction

Lecture 39 Introduction to Data Modeling

Lecture 40 Case Study: Optimizing Data Modeling for Business Growth

Lecture 41 Conceptual, Logical, and Physical Data Models

Lecture 42 Case Study: Optimizing Patient Management Systems

Lecture 43 Normalization and Denormalization

Lecture 44 Case Study: Balancing Normalization and Denormalization

Lecture 45 Data Relationships and Entities

Lecture 46 Case Study: Building a Scalable Data Model

Lecture 47 Best Practices in Data Design

Lecture 48 Case Study: Transforming Data Management

Lecture 49 Section Summary

Section 6: Data Storage and Operations

Lecture 50 Section Introduction

Lecture 51 Introduction to Data Storage

Lecture 52 Case Study: Optimizing Data Storage

Lecture 53 Storage Models and Techniques

Lecture 54 Case Study: Strategizing Scalable and Secure Data Storage

Lecture 55 Data Retention Policies

Lecture 56 Case Study: Developing a Robust Data Retention Policy

Lecture 57 Data Backup and Recovery

Lecture 58 Case Study: Strengthening Data Resilience

Lecture 59 Data Archiving and Deletion

Lecture 60 Case Study: Optimizing Data Management

Lecture 61 Section Summary

Section 7: Data Security Management

Lecture 62 Section Introduction

Lecture 63 Introduction to Data Security

Lecture 64 Case Study: Enhancing Data Security

Lecture 65 Data Security Principles and Policies

Lecture 66 Case Study: Enhancing Data Security

Lecture 67 Data Access Control and Encryption

Lecture 68 Case Study: Integrated Data Access Control and Encryption Strategies

Lecture 69 Security Risks and Mitigation Strategies

Lecture 70 Case Study: Enhancing Data Security

Lecture 71 Regulatory Compliance and Data Privacy

Lecture 72 Case Study: TechNova Data Breach

Lecture 73 Section Summary

Section 8: Data Quality Management

Lecture 74 Section Introduction

Lecture 75 Introduction to Data Quality

Lecture 76 Case Study: Enhancing Data Quality in Healthcare

Lecture 77 Data Quality Dimensions

Lecture 78 Case Study: Optimizing Data Quality Dimensions

Lecture 79 Data Quality Assessment Techniques

Lecture 80 Case Study: Enhancing Data Quality

Lecture 81 Tools and Processes for Data Quality Improvement

Lecture 82 Case Study: Enhancing Data Quality at TechNova

Lecture 83 Implementing a Data Quality Program

Lecture 84 Case Study: Enhancing Decision-Making

Lecture 85 Section Summary

Section 9: Reference and Master Data Management

Lecture 86 Section Introduction

Lecture 87 Introduction to Master and Reference Data

Lecture 88 Case Study: Transforming Data Management

Lecture 89 Differences Between Master and Reference Data

Lecture 90 Case Study: Enhancing GlobalTech's Data Management

Lecture 91 Master Data Management Frameworks

Lecture 92 Case Study: Optimizing Healthcare and Retail Operations

Lecture 93 Reference Data Standardization

Lecture 94 Case Study: Enhancing Data Integrity and Compliance

Lecture 95 Tools for Managing Master and Reference Data

Lecture 96 Case Study: Implementing Master and Reference Data Management

Lecture 97 Section Summary

Section 10: Metadata Management

Lecture 98 Section Introduction

Lecture 99 Introduction to Metadata Management

Lecture 100 Case Study: Enhancing Retail Success through Robust Metadata Management

Lecture 101 Types of Metadata: Descriptive, Structural, and Administrative

Lecture 102 Case Study: Transformative Metadata Management

Lecture 103 Metadata Repositories and Standards

Lecture 104 Case Study: Enhancing Data Management

Lecture 105 Metadata Governance

Lecture 106 Case Study: TechNova's Metadata Governance

Lecture 107 The Role of Metadata in Data Integration

Lecture 108 Case Study: Leveraging Metadata for Efficient and Quality-Driven Data

Lecture 109 Section Summary

Section 11: Data Warehousing and Business Intelligence

Lecture 110 Section Introduction

Lecture 111 Introduction to Data Warehousing

Lecture 112 Case Study: Strategic Data Warehousing

Lecture 113 Data Marts and Data Lakes

Lecture 114 Case Study: Balancing Data Marts and Data Lakes for Scalable Analytics

Lecture 115 Business Intelligence Principles

Lecture 116 Case Study: Unlocking the Full Potential of Business Intelligence at TechNova

Lecture 117 Data Warehousing Models: Star and Snowflake

Lecture 118 Case Study: Optimizing Data Warehousing

Lecture 119 Reporting and Analytics in BI

Lecture 120 Case Study: Transforming Patient Care Through Business Intelligence

Lecture 121 Section Summary

Section 12: Data Integration and Interoperability

Lecture 122 Section Introduction

Lecture 123 Introduction to Data Integration

Lecture 124 Case Study: TechFusion's Comprehensive Data Integration Strategy

Lecture 125 Data Sources and Extraction Techniques

Lecture 126 Case Study: Integrating Diverse Data Sources for Enhanced Business Insights

Lecture 127 Data Transformation and Loading (ETL)

Lecture 128 Case Study: Optimizing ETL for Data Integration

Lecture 129 Data Interoperability Standards

Lecture 130 Case Study: Enhancing Patient Care Through HL7 Interoperability

Lecture 131 Managing Data Silos

Lecture 132 Case Study: Overcoming Data Silos

Lecture 133 Section Summary

Section 13: Document and Content Management

Lecture 134 Section Introduction

Lecture 135 Introduction to Document Management

Lecture 136 Case Study: Transforming Healthcare Efficiency

Lecture 137 Content Management Systems (CMS)

Lecture 138 Case Study: Transforming E-Commerce Efficiency

Lecture 139 Managing Unstructured Data

Lecture 140 Case Study: Optimizing Unstructured Data Management

Lecture 141 Version Control and Document Security

Lecture 142 Case Study: Integrating Version Control and Document Security

Lecture 143 Document Classification and Retrieval

Lecture 144 Case Study: Optimizing Document Management with Machine Learning

Lecture 145 Section Summary

Section 14: Big Data and Emerging Technologies

Lecture 146 Section Introduction

Lecture 147 Introduction to Big Data

Lecture 148 Case Study: Strategic Evolution at TechNova

Lecture 149 The Role of Big Data in Data Management

Lecture 150 Case Study: TechNova's Big Data Transformation

Lecture 151 Big Data Storage and Processing

Lecture 152 Case Study: Strategic Innovations in Big Data Management

Lecture 153 Emerging Data Technologies (AI, Blockchain)

Lecture 154 Case Study: Integrating AI and Blockchain

Lecture 155 Implications of Big Data for Data Management

Lecture 156 Case Study: Transforming Data Management

Lecture 157 Section Summary

Section 15: Data Management Ethics and Compliance

Lecture 158 Section Introduction

Lecture 159 Introduction to Data Ethics

Lecture 160 Case Study: Ethical Data Management in Tech

Lecture 161 Ethical Data Use and Decision Making

Lecture 162 Case Study: Navigating Ethical Challenges

Lecture 163 Regulatory Frameworks (GDPR, HIPAA)

Lecture 164 Case Study: Navigating GDPR and HIPAA

Lecture 165 Legal Considerations in Data Management

Lecture 166 Case Study: Ensuring GDPR and HIPAA Compliance in Global Healthcare

Lecture 167 Building an Ethical Data Culture

Lecture 168 Case Study: Building an Ethical Data Culture

Lecture 169 Section Summary

Section 16: Emerging Trends in Data Management

Lecture 170 Section Introduction

Lecture 171 Introduction to Data Management Trends

Lecture 172 Case Study: Mastering Data Management

Lecture 173 Data Governance in the Age of Big Data

Lecture 174 Case Study: TechNova's Strategic Overhaul

Lecture 175 Artificial Intelligence and Data Management

Lecture 176 Case Study: TechNova's AI-Driven Transformation

Lecture 177 Cloud Data Management

Lecture 178 Case Study: Transforming E-Commerce

Lecture 179 Data Privacy and Ethical Challenges in the Digital Age

Lecture 180 Case Study: ZypherTech Data Breach

Lecture 181 Section Summary

Section 17: Course Summary

Lecture 182 Conclusion

Aspiring data management professionals seeking to earn the CDMP Associate certification.,IT and data professionals looking to enhance their knowledge of data governance, architecture, and security.,Individuals transitioning into data management roles who want a comprehensive understanding of key principles.,Business analysts and data analysts aiming to improve their data modeling and quality management skills.,Project managers and team leads overseeing data-driven projects and seeking to improve data strategies.,Recent graduates in IT, computer science, or business-related fields who want to specialize in data management.,Professionals interested in staying updated on emerging trends like AI, big data, and cloud technologies in data management.