Early on in the writing process I decided to go for the business intelligence angle and not for a generic book on data virtualization. This explains the title Data Virtualization for Business Intelligence Systems. It allowed me to dive deep with respect to BI-related topics, such as data quality, data integration, effects on data marts, and the impact on data profiling.
Writing a book requires a lot of studying. It means you have to seriously structure and order your knowledge of a topic. Some of that studying leads to great insights. I got some very useful insights when studying for the chapter on design guidelines for data virtualization. In the IT industry many new technologies are introduced every year. Just think about big data, NoSQL, cloud, and so on. But what strikes me is that most of these new technologies are introduced without giving customers any design guidelines. It's up to them to use a trial and error approach to find the proper guidelines, which is an expensive and time-consuming way of discovering how to use new technology the best way.
Therefore I decided to include a chapter in the book on design guidelines when using data virtualization servers. Examples of guidelines are:
- How to handle incorrect data.
- Dealing with different users using different definitions for the same concepts.
- Retrieving data from production systems and the potential interference that results from that.
It was fun to write this book, and I hope it will help to introduce data virtualization in the BI industry, because this technology deserves more attention.
Posted September 10, 2012 12:35 AM
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