How to Prepare for CDMP® Data Modelling Specialist Exam

Executive Summary

This webinar outlines key elements of Data Modelling, including its significance in enterprise architecture and the critical success factors for exam preparation in a Data Modelling course. Howard Diesel emphasises the importance of Data Governance, best practices for data normalisation, and the application of data models in business communication and management. Additionally, he highlights essential readings, techniques for effective data visualisation, and the implementation of structured design principles in data architecture. Challenges faced during the Data Modelling specialist exam and the role of feedback in enhancing learning outcomes are also discussed, providing a comprehensive overview of the course content and grading considerations.

Webinar Details

Title: How to Prepare for CDMP® Data Modelling Specialist Exam
Date: 08 October 2023
Presenter: Howard Diesel
Meetup Group: African Data Management Community
Write-up Author: Howard Diesel

Contents

• Executive Summary
• Webinar Details
• Introduction to Data Modelling and Course Selection in the Canvas Course
• Critical Success Factors for Exam Preparation
• Data Governance and Data Modelling: A Discussion on Course Content and Grading
• Understanding Steve Hoberman and the Data Modelling Course
• Overview of Data Modelling and Design Course
• Data Modelling and Its Importance in Enterprise Architecture
• Intricacies of Data Modelling
• Implementing Data Modelling Techniques
• Essential Reads on Data Modelling
• Utilising Data models in Business Communication and Management
• Understanding Data Modelling and Notation
• Best Practices for Data Normalization and Modelling
• The Principles and Techniques of Data Visualisation
• Implementing Structure and Design Principles in Data Architecture
• The Importance of Data Architecture and Presentation Guidelines
• Data Modelling Governance and its Importance in Business
• Challenges in the Data Modelling Specialist Exam
• Feedback Session on a Canvas Quiz
• Data Modelling and Exam Discussion
• Testing and Learning in Professional Settings

Introduction to Data Modelling and Course Selection in the Canvas Course

Howard Diesel opened the webinar and shared that the focus would be on Data Modelling, and we will provide important insights that will be beneficial for your upcoming course. For those who were not present last week, please take a moment to register and enrol in the Canvas course. This will allow you to access sample questions that reflect the types of inquiries you will encounter in the Data Modelling course, enhancing your preparation and understanding of the subject matter.

We will discuss the structure of the specialist exams after covering the fundamentals. By understanding how to choose your specialist exams based on your role and departmental needs, you can tailor your learning experience effectively. Additionally, we will provide guidance on recognising the different levels of questions within the DMBOK framework—practitioner, associate, and master. Throughout the session, we will also engage with sample quizzes and exam techniques to ensure you are well-prepared for the assessments ahead.

Figure 1 Speed Data CDMP Specialist Exams

Figure 2 CDMP Specialist Exams

Figure 3 CDMP Specialist Exam Q&A

Critical Success Factors for Exam Preparation

A crucial factor in preparing for the specialist exam is understanding its various categories. Engaging in discussions, such as the one Paul and he had regarding Data Governance, can significantly enhance comprehension of the course material. By familiarising yourself with the different sections of the exam and employing techniques like tagging essential concepts in your study notes, you can better identify relevant information and improve your chances of success.

Moreover, it is important to recognise the interconnections between topics like Data Governance and Data Modelling, as many exam questions will address these relationships. Preparing for a master ‘s-level pass also requires a willingness to retake the exam if necessary, as rewrites are more cost-effective than the initial attempt. With the right training courses and support, you can navigate the exam structure effectively and enhance your readiness for success.

Figure 4 Spcialist Exam Sample Quiz using Instructure

Figure 5 Exam Critical Success Factors

Figure 6 Modelware Systems Training Courses

In the recent sessions, we covered foundational topics, including Data Governance and the associated exam. This week, as you log into Canvas, you will notice that the previous week’s Data Governance exam is no longer available; instead, you’ll find the Data Modelling questions being published. Looking ahead, next week will focus on Data Quality, followed by Business Intelligence reference concepts in the subsequent week.

Figure 7 Question the Exams

Figure 8 Data Modelling and Design

Figure 9 DM CDMP Preparation Course ILTC

Understanding Steve Hoberman and the Data Modelling Course

Steve Hoberman’s sixth edition of “How to Model Data” is an invaluable resource for both newcomers and seasoned professionals in Data Modelling. This course stands out for its practical approach and systematic methodology, equipping participants with the tools to build effective models from scratch. Through a series of thoughtful questions, learners are guided through the essential characteristics of relational and dimensional models, ensuring a comprehensive understanding of the subject matter.
Over three days, participants engage in hands-on exercises that reinforce their learning, culminating in a use-case study in which they create conceptual and logical models tailored to specific business needs. The program includes exam preparation to familiarise attendees with the structure and types of questions they may encounter, ultimately equipping them for success. By incorporating four practical examples from the book as homework, the course enables learners to apply Hoberman’s techniques in real scenarios, making it an excellent experience for anyone looking to enhance their Data Modelling skills.

Overview of Data Modelling and Design Course

The Data Modelling and design course emphasises key categories essential for understanding and applying Data models effectively. The primary areas of focus include an introduction to Data Modelling, the various activities involved, tools and best practices, and the importance of Data Modelling governance. Collectively, these components represent a significant portion of the exam content, highlighting the necessity for thorough preparation in these areas.

To excel in Data Modelling, it is crucial to understand how Data models are applied to generate business glossaries and enable metadata collection for Data Governance. Understanding the relationship between data models, data dictionaries, and data catalogues is vital, as they collectively enhance data management activities. Additionally, learners should appreciate the differences between various model types, including enterprise, application, conceptual, logical, physical, and dimensional data models. By focusing on best practices, such as developing scorecards to validate data quality, participants will gain practical insights into effectively implementing these modelling techniques.

Figure 10 Data Modelling and Design

Figure 11 What you Need to Study

Data Modelling and Its Importance in Enterprise Architecture

Data Modelling is a fundamental aspect of enterprise architecture that outlines the structure and relationships within data, significantly influencing various organisational models. It is essential to understand how Data models fit within frameworks such as the Zachman Framework, particularly in relation to process models and organisational structures. The enterprise Data Model provides a comprehensive overview of defined subject areas, ensuring alignment with Data Governance practices, such as those outlined by the Data Governance Council. Additionally, the conceptual model plays a crucial role, as data stewards collaborate with data architects to establish a robust framework, reinforcing principles of data stewardship that emphasise the necessity of Data Modelling.

Moreover, the intricacies of metadata in the Data Modelling process highlight the importance of documenting business rules, relationships, and constraints. Collecting and defining metadata —including data definitions and associated information —enriches the overall data framework. For organisations lacking existing physical, logical, or conceptual models, reverse engineering databases into tools like erwin can automatically generate corresponding physical models and facilitate direct integration with live databases to produce DDLs. Thus, reverse engineering provides significant benefits, serving as a valuable strategy for organisations to effectively establish their Data models.

Figure 12 Introduction, Uses and Methods

Intricacies of Data Modelling

In Data Modelling, understanding the components —entities, relationships, and attributes —is essential for effective implementation of a physical data model. The DMBoK outlines critical topics such as identifying entities and subtypes, establishing relationships with appropriate cardinality, and recognising the nuances of supertype-subtype rules. This foundational knowledge is crucial, especially when considering how to implement subtypes—whether through identity, roll-down, partitioning, or absorption—to avoid potential data quality issues.

Furthermore, discussions surrounding denormalisation highlight the importance of employing effective techniques to maintain data integrity during the physical implementation of models. Questions arise about how to resolve data quality concerns when attributes are absorbed into supertypes or when moving from supertype to subtype structures. Engaging in this analytical process, as emphasised in relevant courses, facilitates a deeper understanding of Data Governance and the intricacies of Data Modelling, enabling practitioners to navigate complex scenarios effectively.

Figure 13 Activities

Implementing Data Modelling Techniques

Data Modelling is a crucial process that requires careful planning and execution to ensure effective implementation. Techniques such as those introduced by Steve Hoberman, particularly the concept of “camera settings,” help practitioners determine the essential criteria for building a Data Model. Understanding whether to focus on concrete or abstract elements significantly influences the model’s direction. To effectively position the data model, one must identify core information and adopt a step-by-step approach to building policies and procedures, including eliciting, defining, refining requirements, and obtaining necessary sign-offs.

Moreover, there are various strategies for creating data models, including top-down, bottom-up, and the less commonly discussed middle-out approach. Although many may be familiar with top-down and bottom-up methodologies, middle-out remains a topic of curiosity among practitioners. Additionally, it is essential to clarify the distinction between data models and data lineage, as confusion can arise in business contexts. By utilising both horizontal data lineage, which illustrates the data journey, and vertical lineage, which presents the hierarchical structure of Data models from physical to conceptual levels, professionals can effectively communicate these concepts. Ultimately, clear explanations and visual examples are vital for fostering understanding within the business.

Essential Reads on Data Modelling

Effective Data Modelling is essential for understanding data architecture and governance, particularly for professionals preparing for specialist exams. Two highly recommended books in this field are “Business Modelling Made Simple” by Steve Hoberman and Chris Bradley, which offers a straightforward approach to business modelling. Additionally, it’s crucial to review the DM BOK and its fundamental questions before tackling any specialist exam, ensuring a solid foundation in key concepts.
Furthermore, familiarity with related areas, such as Data Governance and reference data implementation, is vital to success. For instance, understanding how to apply foreign keys to reference data tables can significantly enhance your Data Modelling skills. Veronica has compiled a comprehensive list of additional books that can further deepen your knowledge in these topics, providing valuable resources for anyone looking to excel in Data Modelling. You can read.

Utilising Data models in Business Communication and Management

Data Modelling is an essential activity that enhances communication between technical teams and business stakeholders. By utilising a well-defined subject-area model, organisations can effectively illustrate their data landscape, facilitating a clearer understanding during SWOT analyses of data usage. The visual representation of Data models not only aids in conveying complex relationships but also serves as a powerful tool in aligning business strategies with data management practices.

Additionally, Data models play a critical role in understanding software package requirements and ensuring that business rules are enforced across various systems. While software vendors may be reluctant to share their internal Data models, leveraging established business and relationship rules helps identify discrepancies in software functionality. Ultimately, recognising the significance of Data models extends beyond database management systems and contributes to comprehensive Data Governance across all data management knowledge areas.

Understanding Data Modelling and Notation

Understanding the fundamentals of Data Modelling is essential for effectively defining and implementing a data structure that meets business requirements. When creating a data model, it is important to establish the schema that the model will utilise and determine whether it will operate as a relational, abstract, or concrete model. This process often involves conducting a thorough review of camera settings, allowing data architects to take a snapshot of the current configuration and integrate that information into the Data Model for clarity.

Once the schema and database system are selected, the next steps involve developing the conceptual and logical data models based on business requirements and identifying the appropriate modelling notations for the various modelling levels. Whether dealing with NoSQL databases like MongoDB or traditional relational databases, understanding the structure—such as document format for NoSQL—is crucial. By employing appropriate notations and methodologies, professionals can create a robust data architecture that accurately reflects business needs and enables efficient data management. we can go through that.

Figure 14 Tools

Best Practices for Data Normalization and Modelling

Effective data management relies heavily on the principles of normalisation, as well as the strategic use of layout techniques for presentation. Understanding the normalisation levels allows for efficient data structuring, with the key being to focus on the essential elements. While normalisation enhances data integrity, denormalisation can improve performance in specific scenarios. Instead of merely denormalising, it is often beneficial to create views that provide users with access to the necessary data without compromising the overall structure.

In addition to normalisation, employing effective layout techniques and adopting a robust Data Model Scorecard are critical components of best practices. Techniques such as the Hoberman Scorecard, along with well-considered layouts and presentation strategies, ensure clear and impactful communication of data insights. By mastering both the Normalisation process and effective presentation techniques, data professionals can significantly enhance their ability to convey information and facilitate better decision-making.

Figure 15 Best Practices (Improving your Data models)

The Principles and Techniques of Data Visualisation

Effective visualisation relies on several key principles that ensure clear communication of complex information. At the heart of this process is the need to present data in a way that facilitates understanding rather than confusion. If a viewer struggles to decode a diagram or model, they will be unable to grasp the underlying concepts. Therefore, it is crucial to identify the visualisation’s primary purpose, the intended audience, and the key messages to be conveyed.

Additionally, careful consideration of the data elements included and excluded in the visualisation is crucial to its effectiveness. Establishing which core entities and relationships matter, and ensuring they align with the visualisation’s purpose, is essential. Avoid cluttering the diagram with unnecessary details, as this can hinder comprehension. Ultimately, a well-structured visualisation should lead the viewer to the most critical takeaways, enable informed decision-making, and minimise the cognitive load required to decode the information presented. they can even understand it.

Figure 16 Visualisation

Figure 17 Purpose – Why this Visualisation

Figure 18 Content – What to Visualise

The structure of visual information is crucial for effective communication and understanding. It involves selecting meaningful layouts that enhance clarity and organisation, thereby significantly impacting how relationships and important data are perceived. Data Architects play a key role in this process by establishing diagramming standards, ensuring that visual representations are not only informative but also aesthetically appealing and easy to navigate.

To achieve a well-structured visual layout, several design principles must be considered. Emphasis through colour, contrast, and size can draw attention to critical elements, while maintaining balance—whether symmetrical or asymmetrical—enhances the overall composition. Repetition can effectively reinforce relationships among various entities, and attention to proportion further contributes to a harmonious design. Ultimately, a thoughtful approach to visual structuring helps convey complex information in a more digestible and engaging manner.

Figure 19 Structure – How to Visualise

Figure 20 Diagram Design Principles

The Importance of Data Architecture and Presentation Guidelines

In Data Modelling, it is crucial for the Data Architect to adhere to established guidelines to facilitate clear communication and comprehension among stakeholders. This includes meticulously organising entities to avoid confusion and ensuring that relationship lines are presented in a logical manner. For instance, aligning the connections between entities, such as authors and titles, in a clockwise direction makes their relationships easier to interpret. Additionally, it is essential to incorporate questions or uncertainties that arise during modelling into presentations, as these can guide discussions and clarify concepts.

Furthermore, the approach to Data Modelling often involves reverse engineering from the physical model to the logical and conceptual models. By simplifying complex indexes and focusing on core business attributes, architects can create a more accessible representation of data relationships. While normalisation is vital for accuracy, it may sometimes be necessary to present many-to-many relationships for improved readability. This practice not only enhances the model’s clarity but also encourages thoughtful consideration of how varied entities interact within the database. we need to be able to deal with it.

Figure 21 Techniques: Diagramming Clarity

Figure 22 Suggestions for Technical Diagrams

Figure 23 Best Practices (Improving your Data models)

Effective Data Modelling governance is essential for integrating data practices into the Software Development Life Cycle (SDLC). To assess the maturity of Data Modelling practices, it’s crucial to evaluate the iterative cycles, procedures, and scorecards used to manage data models. This process also involves considering core business aspects, such as defining the necessary data for business operations, ensuring clarity in terminology through a glossary, and establishing vertical lineage from conceptual to physical models. Accountabilities, skills, and the fitness of data for specific purposes must be continuously reviewed to ensure that all required information is available for decision-making.

Furthermore, the relationship between data models and business objectives underscores the importance of aligning Data Governance with organisational goals. The mapping process, as illustrated in the Zachman Framework, shows how various levels of abstraction—from subject-area models for executives to data dictionaries for database administrators—contribute to a comprehensive understanding of data in relation to business processes and applications. Engaging with these levels of abstraction is vital for effective interpretation and specification of data needs, thereby further enhancing governance practices across the organisation.

Figure 24 Data Model Governance

Figure 25 Data Modelling Enables these Core Business Considerations

Figure 26 Data Modelling Facilitates

Figure 27 Data Governance and Models – Metadata Extensions

Figure 28 Business Data Model (Governance Model)

Figure 29 Data Model Integration

Challenges in the Data Modelling Specialist Exam

The Data Modelling specialist exam presents several notable challenges that candidates must navigate. Among these, the DMF questions stand out, with a significant emphasis on concepts related to normalisation and physical implementation. Participants will encounter a variety of questions, ranging from essential concepts such as normalisation and Boyce-Codd Normal Form to practical aspects such as building views, creating indexes, and setting up keys for data access, all aimed at enhancing performance. However, many candidates find the exam format, particularly the true/false questions, frustrating, as these often offer fewer options than expected, making it difficult to gauge the correct answers.

In addition to the format, the focus on higher normalisation and the trade-offs between performance and denormalisation add complexity to the exam. These challenges require thorough preparation and an understanding of both theoretical and practical elements of Data Modelling. Ultimately, success in the exam hinges on the candidate’s ability to grasp these concepts while effectively managing the frustrations that arise from the exam’s structure and question types.

Figure 30 Reasons for Abstraction

Figure 31 Data Modelling Specialist Exam

Figure 32 CDMP Data Governance Specialist and Data Modelling Exam Preparation

Feedback Session on a Canvas Quiz

Before delving into the quiz, it’s important to ensure everyone is prepared and understands the material presented. If any participants have questions or require clarification, now is the perfect time to ask. However, if there are no inquiries, we can proceed to the quiz, which consists of multiple-choice options designed to test your knowledge effectively.

The quiz comprises 22 different assessments, with the top one offering 65 points for those looking for a challenge. I recommend focusing on the 30-question quiz, which can be completed in 10 minutes. As you complete the quiz, please share your thoughts and observations on the questions; your feedback is valuable for further improvement. So, get ready to showcase your knowledge, and let’s make the most of this time together!

Data Modelling and Exam Discussion

In our Data Modelling Master Class, the energy and engagement among participants have been remarkable. Many individuals have actively contributed to the discussions, showcasing their knowledge and skills. As the session progressed, it was encouraging to see participants supporting each other, especially as they tackled the quiz and shared their thoughts on the exam’s challenges.

Furthermore, the emphasis on collaboration was evident when participants raised their hands on teams to signal their progress, creating a supportive learning environment. Congratulations were shared among peers, highlighting achievements and encouraging a healthy competitive spirit. Overall, the session not only fostered learning but also strengthened camaraderie among participants, making it a valuable experience for everyone involved.

Testing serves as a valuable tool for identifying areas of weakness in our understanding and knowledge. It’s important to approach these assessments without embarrassment or anxiety about potential incorrect answers. Instead, they highlight the specific topics that require further review and reinforcement, showcasing the opportunity for growth and learning. This perspective emphasises that testing is not a judgment of what you don’t know but rather a guide to enhance your knowledge base.
Engaging in discussions about exam experiences can further enrich the learning process. Many participants report that certain question types, such as double negatives, challenge their thinking, prompting deeper reflection and understanding. Additionally, for those needing extra time, such as our Saudi participants who select the ESL option, it’s crucial to utilise available resources effectively. Overall, while there may be gaps in knowledge, continuous practice and questioning lead to improvement, equipping us with the skills necessary for future exam success.

If you would like to join the discussion, please visit our community platform, the Data Professional Expedition.

Additionally, if you would like to watch the edited video on our YouTube please click here.

If you would like to be a guest speaker on a future webinar, kindly contact Debbie (social@modelwaresystems.com)

Don’t forget to join our exciting LinkedIn and Meetup data communities not to miss out!