Q:  

We are currently evaluating several BI solution options and realized that there are solutions that manage by data kept in data warehouses and those that manage by data kept in analysis cubes. Could you advise which is a better approach of managing data?

A:  

Larissa Moss' Answer: That depends on the requirements. If I understand the question correctly, you are asking about the "better approach" for storing data in a detailed, atomic manner in a data warehouse as opposed to a lightly or highly summarized manner in MOLAP cubes (proprietary multidimensional databases). The architecture of an enterprise data warehouse accommodates all users and all access/analysis requirements and is, therefore, usually based on an entity-relationship schema, which is commonly referred to as "normalized" or "relational" for maximum flexibility. The architecture of MOLAP analysis cubes accommodates a subset of users and a specific set of analysis/reporting requirements and is therefore usually based on a multidimensional schema for maximum performance. Many organizations include both of these architectures in their data warehouse environment because they need the flexibility to run any query at any level of detail from any user for any business process and the performance to run predefined, repeatable queries and reports in the most efficient and expedient way.

Adrienne Tannenbaum's Answer: Actually neither. Managing data is something that should be done outside of the BI world. For example, the consistency of sales data may be hard to track when Product Names are not consistent and could in fact be redundant (PR457 vs Manufacturing Measurement Machine vs. Machine No. 34). In general, "corporate" or "enterprise" data is standardized and managed by a data management group, then made available to applications such as a BI package. Good data works well in either format ... bad data doesn't work in either format.

Chuck Kelley's Answer:I tend toward data kept in the data warehouse feeding the analysis cubes. The reason is manageability of the data. However, many analysis cube vendors will say that is no longer a concern. I don't believe them yet.

Les Barbusinski's Answer: Each option has its pros and cons. There is no single "right" solution. DMReview.com has a variety of white papers available in its BI Resource Portal that describe the difference between MOLAP (i.e., cube-based) vs. ROLAP (i.e., star schema-based) BI tools. Also, you may want to check out Nigel Pendse's OLAP tool survey. He does a reasonable job of differentiating the different technologies embedded in various BI tools and describing the strengths and weaknesses of each tool. You can subscribe to the survey at http://www.olapreport.com/. Hope this helps.

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