In this article, I’m sharing my impressions of Data Modeling with Microsoft Excel. In one sentence: this book is exceptionally well written and will be extremely useful for anyone who uses Excel for data analysis and reporting.
Let’s get into the details.
Technical Details
Full title: Data Modeling with Microsoft Excel: Model and analyze data using Power Pivot, DAX, and Cube functions
Author: Bernard Obeng Boateng
Publisher: Packt Publishing
Link: https://www.packtpub.com/en-us/product/data-modeling-with-microsoft-excel-9781803240282
Published: February 2025
Pages: 316
Price (October 2025): $25 for the eBook (PDF and EPUB formats) on the Packt website.
Audience: Excel users looking for practical and efficient methods to manage and analyze large datasets using Power Pivot.
Who will benefit the most:
In my opinion, the book will be most valuable for professionals who regularly create reports and data analyses in Excel but have limited theoretical background in data modeling.
That said, even advanced users will find plenty of useful insights inside.
Part 1: Introduction to Data Modeling in Excel
This section lays the foundation for understanding how to build a proper data model in Excel.
Readers will learn key concepts such as:
- What a data model is and why it’s better than the traditional “one big table + pivot” approach.
- What Power Pivot is, how to activate it, and what advantages it offers.
- Introduction to DAX.
- Data normalization — explained clearly with three golden rules to follow.
- Data redundancy — what it means and how it causes problems.
- Relationships between tables: one-to-many, many-to-many, one-to-one — and how to create them in Power Pivot.
- Fact and dimension tables — arguably the most important concept, explained with practical examples.
- Primary and foreign keys — how they work and why they matter.
- Data preparation with Power Query — a chapter dedicated to cleaning and loading data into the model. The author covers the basics without overwhelming details, showing how to use menus, transformations, and examples step by step.
- Star and snowflake schemas — the most common data model architectures.
Part 2: DAX and Cube Functions — The Real Power of Excel
Once the data is structured and loaded, it’s time for analysis.
This part focuses on the two main languages used for analytical calculations: DAX and MDX (Cube functions).
Chapter 5 – DAX Fundamentals
The author explains what every aspiring data modeler should know:
- The difference between measures and calculated columns — and when to use each.
- Popular DAX functions like CALCULATE, FILTER, ALL, SELECTEDVALUE, RELATED, RELATEDTABLE, USERELATIONSHIP, DIVIDE, MROUND, GROUPBY, and others — all presented with clear examples and screenshots.
- Row and filter context — what they are and how they differ.
Chapter 6 – Cube Functions in Excel
This is one of the best, most concise explanations of MDX and Cube functions I’ve seen.
It’s especially valuable for experienced users who may already be familiar with DAX but haven’t explored this part of Excel deeply.
The author not only explains how to write, edit, and use Cube functions but also shows how to build reports that behave like PivotTables — complete with slicers and filters — without actually being PivotTables.
The chapter ends with a fascinating section on combining Cube functions with dynamic ranges for even greater flexibility.
Part 3: From Model to Dashboard
This section shifts focus from technical implementation to presentation — how to make data look good and communicate insights effectively.
It’s relevant not only for modelers but for anyone who builds reports, dashboards, or presentations.
Topics covered include:
- Key factors to consider when designing reports and dashboards.
- The main elements of a good dashboard.
- Best practices for charts and visuals.
- How to use various visualization tools — not just charts, but also sparklines, shapes, icons, colors, and conditional formatting.
- A full step-by-step example of building a dashboard from scratch.
- How to publish and share your finished report.
My Verdict
I consider Data Modeling with Microsoft Excel an excellent, well-structured book that covers everything a data analyst truly needs.
It’s written in plain language, full of examples and visuals, and provides knowledge that’s relevant beyond Excel — these principles apply to any analytical environment.
Bernard Obeng Boateng has done a great job crafting a complete guide — from preparing and connecting data tables to analyzing with DAX and MDX and finally presenting results in a clear, meaningful way.
This book is, in my view, an excellent investment for anyone working in this field.
If I had to point out a drawback, it would be the absence of downloadable sample files (or at least I couldn’t find them). Some topics are repeated a bit more than necessary — but that might actually help beginners reinforce key concepts.
These minor flaws don’t affect the overall quality.
I highly recommend this book because if Excel is part of your daily routine, it will save you countless trial-and-error hours.