One of the most important aspects of a BI project is the underlying data warehouse model. This may seem like a no-brainer, right?
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In practice, I don't believe the data model is always given the proper time or expertise necessary for a successful implementation. Often, seemingly small flaws or shortcuts in the design of data warehouses or marts can cause much larger problems down the road in terms of functionality, performance, and flexibility. It's imperative that any organization faced with the task of designing their own data warehouse has a project team that understands the importance of a well-planned dimensional model. Changes can always be made, but often at an exponentially higher cost when issues are uncovered late in the game.
Anyone involved with a BI project at any level without a sound understanding of data warehouse modeling techniques should consider doing some research on the topic. One book in particular which I highly recommend is The Data Warehouse Toolkit, 2nd Ed. by Ralph Kimball and Margy Ross. If you're unfamiliar with Ralph Kimball, he's considered a pioneer in the field of data warehousing.
Another highly regarded data warehouse guru is BIll Inmon, who has also written several books on the subject. Most experts in the field strongly side with either Kimball or Inmon. There have been many arguments around whose philosophy on the subject is superior, but of course I'd hate to go picking a fight in my first post by endorsing one over the other here. Maybe we'll hit the topic of their fundamental differences next time. In actuality, their methodologies are very similar and have become more so over time. Anyone interested in learning more about warehouse design should pick up a book on the subject by either or both of them.
An understanding of their techniques and a well designed warehouse won't guarantee a successful implementation, but it's a great step in the right direction. Obviously, we'll never be able to foresee every issue to arise or every request thrown at us, but a strong warehouse design will allow us to deal with both much more easily.
In practice, I don't believe the data model is always given the proper time or expertise necessary for a successful implementation. Often, seemingly small flaws or shortcuts in the design of data warehouses or marts can cause much larger problems down the road in terms of functionality, performance, and flexibility. It's imperative that any organization faced with the task of designing their own data warehouse has a project team that understands the importance of a well-planned dimensional model. Changes can always be made, but often at an exponentially higher cost when issues are uncovered late in the game.
Anyone involved with a BI project at any level without a sound understanding of data warehouse modeling techniques should consider doing some research on the topic. One book in particular which I highly recommend is The Data Warehouse Toolkit, 2nd Ed. by Ralph Kimball and Margy Ross. If you're unfamiliar with Ralph Kimball, he's considered a pioneer in the field of data warehousing.
Another highly regarded data warehouse guru is BIll Inmon, who has also written several books on the subject. Most experts in the field strongly side with either Kimball or Inmon. There have been many arguments around whose philosophy on the subject is superior, but of course I'd hate to go picking a fight in my first post by endorsing one over the other here. Maybe we'll hit the topic of their fundamental differences next time. In actuality, their methodologies are very similar and have become more so over time. Anyone interested in learning more about warehouse design should pick up a book on the subject by either or both of them.
An understanding of their techniques and a well designed warehouse won't guarantee a successful implementation, but it's a great step in the right direction. Obviously, we'll never be able to foresee every issue to arise or every request thrown at us, but a strong warehouse design will allow us to deal with both much more easily.
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