If your analyzing sales data and you have dimensions related to customer type, product classifications, and market channel and let's say a credit memo is issued to a record merely crediting the customer account; no information is provided regarding product in the line item grain of the transaction. No datacube on earth can resolve that unless you have a very good ETL methodology to catch those kinds of errors.

As the author of the webinar morton2002 included in his comments I can confirm that the biggest challenge in any BI system rollout is the quality of the underlying granular data. Tableau is a wonderful enabling tool for visualizing data graphically or in tabular form; however, it can resolve bad data in the underlying source system. Accountants reconcile general ledger data, but generally don't bother with reconciling underlying subsystem information to the final general ledger closing information. Key to success at Blastrac was developing the weekly/monthly processes to resolve those issues found in the underlying source data. Resolving that issue provided the obvious benefit of having very granular data that matched what the accounting team reported each month. The less obvious benefits came from improving processes that were causing the bad data to begin with. That saved TIME and MONEY in addition to providing great (and very granular) information over time.