April 11, 2024
Updated May 24, 2025
18 minute read
Embarking on a Career as an Analytics Manager
An Analytics Manager is a pivotal figure in any data-driven organization, responsible for guiding teams that transform raw data into actionable insights. They oversee the collection, analysis, and interpretation of data to help businesses make informed decisions, optimize operations, and achieve strategic goals. This role requires a unique blend of technical expertise, leadership acumen, and strong communication skills to bridge the gap between complex data findings and business strategy.
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Find a path to becoming a Analytics Manager. Learn more at:
OpenCourser.com/career/unjbsg/analytics
Reading list
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This is widely considered the foundational text on dimensional modeling. It provides a comprehensive guide to designing, developing, and deploying dimensional data warehouses and business intelligence systems. Essential for gaining a broad understanding and must-read for anyone entering the field.
This set includes the three core Kimball Toolkit books, offering a comprehensive library of his foundational work on dimensional modeling, the data warehouse lifecycle, and ETL. Owning this set provides access to the most authoritative guides in the field and must-have for serious practitioners. These are considered classics and must-reads.
Building upon the modeling concepts from the Toolkit, this book details the entire data warehouse project lifecycle. It's invaluable for understanding the practical steps involved in implementing a dimensional model from requirements gathering to deployment and maintenance. useful reference tool for project planning.
Focusing specifically on the Extract, Transform, Load (ETL) process, this book provides essential techniques for populating a dimensional data warehouse. It's a critical companion to the primary Toolkit book for anyone involved in the data integration aspects of dimensional modeling. useful reference for ETL developers.
Offers a deep dive into the design and implementation of star schemas, a core component of dimensional modeling. It covers various design patterns and addresses common challenges. It's an excellent resource for those looking to deepen their understanding beyond the basics presented in introductory texts.
A recent publication focusing on building analytical data models using SQL and dbt, a popular tool in modern data stacks. is highly relevant for understanding contemporary practices in creating and managing dimensional-like models in cloud-based data warehouses. It dives into contemporary topics and tools.
Provides a comprehensive overview of online marketing, including attribution modeling. It covers the different types of attribution models, how to implement them, and how to interpret the results. The book is written by two leading experts in the field, and it is full of practical advice and insights.
Provides a comprehensive overview of data warehousing, covering all aspects of the process from data modeling to data warehousing. It is written by Paulraj Ponniah, a leading expert in data warehousing, and is considered a valuable resource for practitioners.
Provides a comprehensive overview of data analytics for marketing, including attribution modeling. It covers the different types of attribution models, how to implement them, and how to interpret the results. The book is written by a leading expert in the field, and it is full of practical advice and insights.
Provides a comprehensive overview of e-marketing metrics, including attribution modeling. It covers the different types of attribution models, how to implement them, and how to interpret the results. The book is written by two leading experts in the field, and it is full of practical advice and insights.
Provides a comprehensive overview of digital marketing analytics, including attribution modeling. It covers the different types of attribution models, how to implement them, and how to interpret the results. The book is written by two leading experts in the field, and it is full of practical advice and insights.
Introduces an agile approach to dimensional modeling, emphasizing collaboration with business stakeholders. It provides practical techniques for gathering requirements and iteratively developing dimensional models. Relevant for contemporary data warehousing practices that prioritize flexibility and speed.
Provides a comprehensive overview of marketing data science, including attribution modeling. It covers the different types of attribution models, how to implement them in R, and how to interpret the results. The book is written by a leading expert in the field, and it is full of practical advice and insights.
Presents the Unified Star Schema, a hybrid approach combining aspects of Inmon's atomic data warehouse and Kimball's dimensional modeling. It offers a perspective on creating flexible and scalable data warehouse designs in contemporary environments. It is relevant for exploring contemporary topics and deepening understanding of design patterns.
Offers a collection of data warehouse designs for various business areas, providing practical examples of how dimensional modeling can be applied to solve real-world business problems. It's a useful reference for seeing dimensional modeling in action across different industries and scenarios.
Authored by the 'Father of the Data Warehouse,' this book presents the Corporate Information Factory architecture, a different approach compared to Kimball's dimensional modeling. Reading this provides a broader understanding of data warehousing concepts and alternative designs, offering valuable context for architectural decisions. It is considered a classic in the field.
This guidebook covers the end-to-end process of delivering business intelligence solutions, from data integration to analytics. It helps connect the dots between dimensional modeling and its ultimate purpose of enabling effective business analysis and decision-making. It provides a broader business context for dimensional modeling.
Provides a comprehensive overview of digital analytics, including attribution modeling. It covers the different types of attribution models, how to implement them, and how to interpret the results. The book is written by a leading expert in the field, and it is full of practical advice and insights.
Provides a practical guide to dimensional modeling, focusing on the techniques for building a data warehouse. It is written by Michael Blaha and Bill Inmon, two leading experts in data warehousing, and is considered a valuable resource for practitioners.
This handbook emphasizes the importance of involving business stakeholders in the data modeling process. It focuses on creating high-level data models that align with business requirements, a crucial aspect of successful dimensional modeling projects. It provides valuable context for the business側 of data modeling.
Provides a broad overview of the data engineering landscape, placing data warehousing and modeling within that context. It covers contemporary practices and technologies relevant to building modern data platforms. It adds breadth by showing how dimensional modeling fits into the larger data ecosystem.
Explores both relational and dimensional design techniques for data warehouses. It provides a balanced view of the two approaches and helps readers understand when to apply each. It can help deepen understanding by comparing and contrasting different design philosophies.
Provides a deep understanding of the fundamental concepts behind data systems, including storage, processing, and distributed systems. While not directly about dimensional modeling, the knowledge gained is highly valuable for anyone designing and implementing data warehouses and data pipelines that feed them. It provides essential background knowledge for advanced practitioners.
A continuation of Volume 1, this book provides data models tailored to specific industries. This is particularly useful for understanding how dimensional modeling principles can be applied within the context of different business domains. It valuable reference for industry-specific data warehousing.
For more information about how these books relate to this course, visit:
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