Executive Summary
In the evolving landscape of Data Management, establishing a unified meta model is crucial for enhancing deliverables and ensuring consistency across various data-related initiatives. This framework not only facilitates streamlined integration and interoperability of data but also serves as a foundation for effective Metadata Management and Data Governance. By leveraging templates designed for Unified Meta-Models and utilising specialist exams and tools in data modelling and warehousing, organisations can better prepare for business intelligence challenges. Furthermore, a focused approach to critical data elements and master Data Management is essential, which fosters improved data quality and enhances strategic decision-making capabilities.
Webinar Details
| Title | How to Prepare for CDMP® Specialist exams – Ref & Master, DW & BI, I.I |
| Date | 05 September 2023 |
| Presenter | Howard Diesel |
| Meetup Group | African Data Management Community |
| Write-up Author | Howard Diesel |
Contents
Importance of a Unified Meta Model in Data Management
Building a Unified Meta-Model for Deliverables in Data Management
Data Management Unified Meta-Model Templates
Specialist Exams and Tools for Data Modelling and Data Warehousing
Instructions for Business Intelligence Exam Preparation
Data Warehousing and Business Intelligence
Critical Data Elements and Master Data Management
Introduction to Master Data Management
Integration, Interoperability, and Metadata Management
Metadata Management and Its Role in Data Governance
Importance of a Unified Meta Model in Data Management
Howard Diesel opens the webinar and underscores several critical themes related to the management and assessment of specialist exams and Data Management practices. Key among these is the necessity for time extensions in specialist exams, which allows for a fair and comprehensive evaluation process. Additionally, a project has been initiated to evaluate the completeness of specifications, which plays a vital role in ensuring that all parameters are adequately covered. The importance of a unified metamodel in Data Management is also emphasised, as it streamlines processes and enhances consistency across various datasets.
Moreover, Howard emphasises the importance of well-defined benchmark deliverables within the DMMA framework, which are crucial for accurate assessment and comparison. Evidence must be gathered to measure performance against these benchmarks, reinforcing the value of good-practice procedures and scorecards in DMMA evaluations. As the upcoming month focuses on developing a template project, preparations are underway for a presentation next Thursday, where a slide detailing these themes will be discussed to foster a clearer understanding of the subject matter.
Figure 1 DM Unified Metamodel
Figure 2 Data Management Unified Metamodel Requirements
Building a Unified Meta-Model for Deliverables in Data Management
The project aims to establish a comprehensive metamodel for effectively storing and reviewing deliverables in Data Management. This initiative encompasses various elements, including a data catalogue, data dictionary, business glossary, and the education of data stewards. It addresses all deliverables outlined in the Data Management Body of Knowledge (DMBOK), as well as custom deliverables such as a data people strategy that links business architecture to value propositions. Central to this project is the goal of creating a deliverable metamodel that includes critical data elements and a quality scorecard, enhancing Data Governance and management processes.
To achieve the project objectives, a total of 139 templates are required, with 13 already completed, spanning across 17 knowledge areas. Each template must be accompanied by a scorecard, procedure, and evidence to reach a maturity level of three. Professionals skilled in various facets of Data Management are essential for project success. Current resources include a scope and quality review for project management and template development, as well as a template for development and training, ensuring a well-rounded approach to the project’s implementation.
Figure 3 Project Scope
Figure 4 Scope Quantification
Figure 5 Project Resourcing
Data Management Unified Meta-Model Templates
Hashan is providing organisations with a comprehensive Data Management Maturity Assessment (DMMA) framework, which is particularly beneficial for the National Disaster Management Organisation (NDMO) to evaluate its deliverables. This framework provides a range of resources, including access to a template library, professional training, and documentation to support compliance. The templates cover essential domains such as data security, data privacy, data catalogues, and Metadata, allowing organisations to demonstrate robust Data Management practices.
In addition, the framework emphasises the importance of vocabulary standards and data quality reviews. By consolidating these elements, the goal is to establish a Knowledge Graph that enhances Data Management capabilities. Individuals interested in contributing to the framework or gaining further insights are encouraged to contact Paul or the speaker. Notably, the upcoming specialist exam in September will concentrate on the Data Management Unified Metamodel templates, highlighting the framework’s relevance in the industry.
Figure 6 Project Benefits
Figure 7 Metadata Management: 5W1H
Figure 8 Conceptual Meta-Model
Figure 9 Data Catalogue Vocabulary Standards
Figure 10 Data Catalogue
Figure 11 Data Products: Sourcing and Creating a Data Product
Figure 12 DMBoK Deliverable
Figure 13 Speed Dating CDMP® Specialist Exam
Specialist Exams and Tools for Data Modelling and Data Warehousing
Becoming a data specialist requires a solid understanding of various concepts in Data Governance, modelling, and quality. Aspiring professionals should familiarise themselves with key topics, including ontologies, taxonomies, and reference models. Distinguishing between business intelligence, data warehousing, and enterprise data warehousing is also vital for effectively managing and analysing data.
To gain practical skills, utilising tools like Protege for Metadata modelling is highly recommended. Additionally, knowledge of data transformation approaches, such as the Kimball and Inmon methods, as well as the Data Vault approach, is essential. Developing and applying a dimensional data model, along with a maturity model, plays a crucial role in assessing and enhancing an organisation’s business intelligence readiness. Ultimately, a comprehensive grasp of these elements will significantly contribute to a successful career in data specialisation.
Figure 14 CDMP® Specialist Exams
Figure 15 Question the Exams
Figure 16 Key Topics
Instructions for Business Intelligence Exam Preparation
Howard explores a comprehensive range of Business Intelligence (BI) topics that are essential for professionals in the field. His coverage includes maturity assessments for various knowledge areas, visualisation techniques, and the intricacies of Data Integration and Implementation (DII) approaches. Additionally, he delves into different analytical styles employed in daily warehousing and reporting, emphasising how these strategies can enhance data-driven decision-making.
Moreover, Howard emphasises the importance of various technology solutions in meeting BI requirements, with a particular focus on big data analytics and data science as crucial components in BI assessments. He provides practical instructions for accessing the CDMP data warehouse and enrolling in the Canvas course for BI practice quizzes. Participants are encouraged to engage in discussions about modifying quiz access and scheduling as needed, ensuring an interactive and tailored learning experience.
Figure 17 Data Warehousing and Business Intelligence Practice Quiz
Data Warehousing and Business Intelligence
To excel in data warehousing and business intelligence (BI), it is essential to have a solid grasp of the fundamentals. While completing a refresher course on these principles is not a prerequisite for advancing to specialised exams, having this foundational knowledge can greatly enhance your performance. Key areas of focus include understanding master data, differentiating between master and reference data, and building a compelling business case. Additionally, aligning data subject areas with business initiatives is crucial for success.
To maintain master status in this field, candidates must complete three exams and achieve a passing score on the master’s examination. Preparation for the Data Management Fundamentals exam can be supported through practice quizzes and targeted questions. The Zachman framework serves as a valuable tool for defining data architecture techniques, particularly when identifying data subject areas relevant to organisational objectives. By mastering these concepts, candidates can significantly boost their proficiency in data warehousing and BI.
Figure 18 Reference and Master Data
Figure 19 Core Element Summary
Critical Data Elements and Master Data Management
Master Data Management (MDM) is a critical process for organisations aiming to streamline their data strategy. It involves identifying and managing essential data elements, known as master data, which have minimal dependencies within the business ecosystem. To effectively implement MDM, organisations must map the dependencies of various business capabilities, which helps in pinpointing these crucial data elements. An incremental implementation plan is then developed, commencing with domains that exhibit the least dependencies, such as the product and customer domains, to ensure a smoother integration process.
Various architectural models can be employed in MDM, including fully centralised hubs, hybrid models, and virtualised architectures. The virtualised architecture, for instance, facilitates the creation of a virtual model using platforms like Denodo for efficient data sharing. Reference architecture models are invaluable for identifying necessary components and selecting suitable tools for managing master and reference data. Depending on specific organisational needs, MDM can be tailored to single-domain focuses, like products, or a more flexible multi-domain approach. Ultimately, organisations should select the implementation style that aligns with their objectives, opting for operational MDM for consolidation or analytical MDM for data analysis.
Introduction to Master Data Management
Master data and Reference data are essential elements of modern applications, playing a pivotal role in maintaining data integrity and consistency across systems. Various types of master data systems exist, and implementing a comprehensive master Data Management strategy can yield significant benefits, enhancing overall organisational efficiency. Before embarking on this journey, organisations must assess their current maturity level, examine existing business processes, and evaluate the compatibility of different architectural approaches to ensure a successful implementation.
Furthermore, adopting an incremental approach to master data implementation can be beneficial, allowing organisations to adapt and respond to evolving needs. Available tools offer diverse implementation styles and capabilities, yet challenges related to data integration and interoperability often arise. Moreover, traditional point-to-point system connections may present limitations, underscoring the need for thoughtful planning and strategy in order to achieve effective master Data Management.
Figure 20 Understanding and Terminology
Figure 21 Important Elements
Figure 22 Reference and Master Data Quiz
Figure 23 Key Topics
Integration, Interoperability, and Metadata Management
Integration and interoperability are vital to the effective movement, consolidation, and sharing of information across systems. The canonical model serves as a framework for defining shared data within an organisation, while integration approaches such as Hub and point-to-point methods offer different pathways for achieving seamless connectivity. However, implementing these methods may pose challenges, especially when dealing with diverse use cases and specific requirements.
To further enhance integration, options such as Data Federation, virtualisation, and API architecture can be utilised. Metadata—encompassing business, technical, and operational elements—describes and enriches data assets, ultimately delivering significant business benefits when properly captured and utilised. Additionally, the hybrid model, which blends centralised and distributed architectural Metadata, highlights the importance of understanding the impact of integration on other knowledge areas, thereby ensuring a comprehensive approach to information management.
Figure 24 Data Integration and Interoperability Quiz
Figure 25 Key Topics
Metadata Management and Its Role in Data Governance
Metadata is a vital component of effective Data Governance, influencing various aspects of Data Management and administration. Adhering to industry standards such as ISO/IEC 179, BCBS 239, and SDMX is essential for establishing a robust Metadata framework. A well-structured business glossary is a significant enabler of Data Governance and is intricately linked to Metadata. The absence of Metadata in data lakes can lead to severe consequences, underscoring the importance of implementing proper Metadata Management practices. By prioritising Metadata, organisations can develop a comprehensive data catalogue that ensures consistency and alignment across datasets.
Moreover, Metadata extends to the collection, storage, and dissemination of data, offering numerous benefits when strategically managed. It plays a pivotal role in day-to-day governance and can be effectively supported by a Data Management project template. Real-world examples illustrate the practical applications of Metadata; however, challenges such as the data swamp issue and the alignment with master data persist in Big Data technologies. Thus, tagging data with Metadata is not just a best practice but a crucial step for enhancing retrieval and comprehension within a broader context.
Figure 26 Metadata Overview
Figure 27 Benefits and Uses of Metadata
Figure 28 Role of Metadata in Data Governance
Figure 29 Metadata Implementation
Figure 30 Big Data and Metadata
Figure 31 Metadata Exam Tips
Figure 32 Metadata Practice Quiz
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