Essential Guide to the CDMP Specialist Exams

Ladley is a leader in the Data Management field and within DAMA. Several CDMP Study Group members have reported that reading this book was key to their success on the Specialist Exam on this topics.

Given the expense related to growth, privacy, and security, organizations face new sources of risk as they generate data assets. Data Governance represents the solution. A strong Data Governance Charter will help an organization navigate the perilous border between risk and opportunity.

CDMP Study Group member Simon Hindle recommends reading this high-level summary in order to build a solid foundation in Data Strategy before tackling the specificity of the DMBOK.

Sebastian-Coleman translates DAMA's ideas, frameworks, and procedures into a business-friendly book that bridges the gap between technical subject matter experts and executive decision makers. The book offers a fantastic overview of the overall goals of Data Management, the terminology, and how to implement Data Strategy at a high level.

This book, now in its second edition, offers guidance on how to best integrate Data Stewardship into project management, handle big data and data lakes, and pivot organizational strategy to benefit from data-driven decision making. It includes detailed lists of responsibilities for each type of data steward and strategies to help the Data Governance Program Office work effectively.

Plotkin provides clear and concise practical advice on implementing and running data stewardship, including guidelines on how to organize based on organizational/company structure, business functions, and data ownership. He provides insight into how to gain support for a stewardship effort and measure success..

Written by the current president of DAMA, this 2021 release is bound to have relevance to what's tested on the Data Governance Specialist Exams now and in the future. Aiken offers a roadmap for expanding data literacy across everyone who has access to the internet. The book offers a thorough guide on expanding your personal data literacy and helping your organization become more data driven.

Another great read from Laura Sebastian-Coleman, who truly has her finger on the pulse of Data Management best practices. As a leading expert, Sebastian-Coleman offers guidance on how to monitor and maximize Data Quality over time. She begins with standard measurement concepts and moves toward a detailed framework of different measurement techniques across the dimensions of Data Quality. Rather than single-instance activities, the organization's goal should be to promote ongoing measurement in order to reach the next level of Data Quality.

The Ten Steps approach specified in this book includes examples, several templates, and practical advice for execution. Readers are guided on how to choose the best action to take next to address their organization's unique situations. This book has been recommended time and time again to support Data Quality initiatives.

Provides practical knowledge of Data Modeling concepts and best practices through real world examples and business-oriented guidance. Hoberman outlines step-by-step approaches to building a subject area model and setting up a scorecard to grade models. This book will also help you advance your understanding of UML diagrams and SQL. Hoberman is a leading expert in the field of Data Modeling, and this book is a must read.

Data Strategy rockstars Hoberman, Burbank, and Bradley explain the technical aspects Data Modeling and illustrate how this important activity supports business objective. The authors explain the Data Modeling process with straightforward examples, and shed light on best practices. They offer user-friendly templates and a systematic guide to implementing a high-level data model.

As agile development has come to the fore as a technology management best practice, this excellent book provides a guide for DBAs interested aligning their work with efficient strategies for rapid development. Ambler describes Agile Modeling Driven Design (AMDD) and Test-Driven Design (TDD) approaches, database refactoring, database encapsulation strategies, and tools that support evolutionary techniques.

Brackett, a veteran Data Architect and recipient of DAMA's Lifetime Achievement Award, wrote this book that will definitely help you produce high quality data resources. After 50 years in the Data Management field, Brackett retired from working as the Data Resource Coordinator for the state government of Washington.

His book provides information on how to formally design an organization's data system to meet current and future demand. While most public and private sector organizations do not consistently follow a formal data resource design process, from Conceptual to Logical to Physical Model, readers will avoid the potential downfalls associated with poor planning.

Along with Marco's other book, Building and Managing the Meta Data Repository: A Full Lifecycle Guide, this is an excellent resource for any data professional. The core of the book presents a comprehensive collection of models that will serve a majority of essential business usecases within an organization. These encompass universal metadata models applicable to enterprise-wide systems, business metadata, data stewardship, portfolio management, business rules, as well as XML, messaging, and transactions. By directly applying these models, developers can streamline the adaptation process for their own businesses, ultimately saving significant development time. Constructing efficient metadata repositories is a complex and time-intensive endeavor, and this book is poised to provide the requisite guidance to help.

A book that wastes no time in teaching you how to optimize information assets in the networked enterprise. The abundant graphics in this book help make things easier to understand.

Data reverse engineering (DRE) is a method for addressing a broad range of data disintegration issues. It is focused on retrieving knowledge about an application from data and its schema. This technique combines structured data analysis tools with stringent data control measures.

This book, and the following, comes recommended by CDMP Study Group member Mayukh Datta Roy, who used it to ace the Specialist Exam on this topic. Another reviewer called Andy Graham 'the Plato of Data Architecture.' It's certain that this book offers a wise perspective based on real project experience in MDM.

The book begins with a basic review of why MDM is necessary and a discussion of its origins. The concept of the “golden record” is core to Graham's treatise on Master Data. Readers will receive a solid education on this concept, particularly how to identify the data sources that comprise the golden record. Finally, the book examines the business drivers and foundations of the business case.

If you're seeking a business-focused perspective on MDM, then this book is a must read. Loshin teaches us that the key to a successful MDM endeavor isn't technology or processes. In fact, it's people: the organizational stakeholders and their ownership of the data that will be affected by the MDM initiative. This book offers a very practical, business-focused approach to MDM. Readers can expect to substantially improve their ability to engage with stakeholders and gain their support. Loshin offers specific guidelines for deciding which data to 'master.' Moreover, a key feature of this book is the thorough roadmap that can be applied to any MDM project.

This book's author is the primary contributor to the DMBOK on the topic of Data Integration & Interoperability, and she's also a VP on the DAMA leadership team. If this topic is of interest to you, you won't want to miss her writing on this important subject. Leveraging over two decades of expertise, Reeve puts forward a vendor-neutral strategy for transporting data between computational environments and data systems. Readers will learn approaches for significantly decreasing the complexity of managing system interfaces and promoting scalable designs.

As part of a data-centric organization, you need to be able to deliver data services that are easy to use, reliable, and scalable. That's where this guide comes in. Pushpak Sarkar outlines how state-of-the-art principles can be used to acquire and distribute data across heterogeneous platforms.

This guide discusses various topics related to technology, personnel training, cloud portfolio, architecture, access controls, and the value of cloud systems. It emphasizes the importance of asking the right questions and provides a self-assessment tool for Integration Platform as a Service (iPaaS) challenges, helping organizations identify areas for improvement in iPaaS projects and processes.

The authoritative guide to dimensional modeling. Kimball and Ross are leading experts and world-renowned data warehouse consultants. This book covers new and enhanced star schema dimensional modeling patterns, provides fundamental knowledge of ETL techniques, and includes twelve case studies across a variety of industries. The book begins with fundamental design recommendations and progresses through increasingly complex scenarios. It presents unique modeling techniques for business applications such as inventory management, procurement, invoicing, accounting,customer relationship management, big data analytics, and more.

A step-by-step guide for capturing requirements and utilizing them to create high performance dimensional models. Agile Data Warehouse Design refers to the method transforming DW & BI into high performance data systems through 'modelstorming,' a portmanteau of data modeling and brainstorming. Modelstorming should take place with business stakeholders, which include technical and nontechnical members of the organization. This approach is consistently quicker, more inclusive, more productive, and frankly more fun than your standard Data Warehouse setup process.

Authors Rhonda Delmater and Monte Hancock Jr. describe how data mining drives next generation customer relationship strategies. This book explains technical concepts in a way that makes them easy for today's technology leaders to understand. It's a must-have for any business that aims to use data mining to drive customer-centric Business Intelligence.