A couple of weeks ago, I book-swapped with author Donna Burbank. She has a new book entitled Data Modeling for Business. Donna, an experienced consultant by trade, has teamed up with Steve Hoberman, a previous published author and technologist and Chris Bradley, also a consultant, for an excellent exploration of the process of creating a data model. With a subtitle like “A handbook for Aligning the Business with IT using a High-Level Data Model” I knew I was going to find some value in the swap.

The book describes in plain English the proper way to create a data model, but that simple description doesn’t do it justice. The book is designed for those who are learning from scratch – those who only vaguely understand what a data model is. It uses commonly understood concepts to describe data model concepts. The book describes the impact of the data model to the project’s success and digs into setting up data definitions and the levels of detail necessary for them to be effective. All of this is accomplished in a very plain-talk, straight-forward tone without the pretentiousness you sometimes get in books about data modeling.

We often talk about the need for business and IT to work together to build a data governance initiative. But many, including myself, have pointed to the communication gap that can exist in a cross-functional team. In order to bridge the gap, a couple of things need to happen. First, IT teams need to expand their knowledge of business processes, budgets and corporate politics. Second, business team members need to expand their knowledge of metadata and data modeling. This book provides an insightful education for the latter. In my book, the Data Governance Imperative, the goal was the former.

The book is well-written and complete. It’s a perfect companion for those who are trying to build a knowledgeable, cross-function team for data warehouse, MDM or data governance projects. Therefore, I’ve added it to my recommended reading list on my blog.

Covering the world of data integration, data governance, and data quality from the perspective of an industry insider.

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