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Business Intelligence or Data Warehouse


Business Intelligence or Data Warehouse

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VincentRainardi
VincentRainardi
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Comments posted to this topic are about the content posted at http://www.sqlservercentral.com/columnists/vRainardi/businessintelligenceordatawarehouse.asp
Henrik Bertelsen
Henrik Bertelsen
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I agree !

You could add the terms MOLAP when BI applications take data from cubes, and ROLAP when they take data from DDS. Or HOLAP when they take data from both.


Dave Doyle
Dave Doyle
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I wish there were more articles like this, clearly delineating all of the differences between terms. More often than not, we use incorrect terms to describe the IS business and in too many cases I find that the miscommunication sometimes wreaks havoc. I thought this was a great article!


Regards,

Dave Doyle
Alexandre Langlois
Alexandre Langlois
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Very Interesting!!! Thanks a lot for making it that clear (drawings always help).

Regards,

aL.


DavidSimpson
DavidSimpson
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Great job of explaining the distinctions between these terms and great use of examples and links.

David





rduniway
rduniway
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A very nice clear presentation of an often (and sometimes deliberately) confusing set of terms. I have one other suggestion, though. The term business intelligence system is correctly defined in this article. The term business intelligence applications is also correctly defined. But business intelligence is something that either exists or doesn't exist in the brains of managers and executives in a business. The systems and applications are only useful if they are serving someone who is willing to take the information delivered and use it to make intelligent decisions.



Loner
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I got a compilation of definitions of data warehouse glossary by the experts some time ago.

Business Intelligence - The capability to perform in-depth analysis and possibly data mining, of detailed business data, providing real and significant information to business users. Business Intelligence usually makes use of tools designed to easily access data warehouse data.

Data warehouse - A collection of integrated, subject-oriented databases designed to support the decision support system (DSS) function, where each unit of data is relevant to some moment in time. The data warehouse contains atomic data and lightly summarized data.


Stephen Hirsch
Stephen Hirsch
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It was a very good article, for sure, but I want to rain on everyone's parade by proposing a more generic definition of data warehouse. To me, a data warehouse is a holding area for data from multiple source systems, used for reporting purposes only. This isn't just pickayune. I created one of the first, if not the only clinical data warehouses; I found that the preconceptions caused by the more specific definitions really got in my way.

First of all, for clinical data, there is no hierarchy. There is no drill down. Reports are output as SAS datasets. Each clinical trial is its own source system, with heterogeneously structured tables. We had 250 trials in production, and added one a week. Each trial had about 30-40 distinct tables. You do the math.

I constantly was fighting with the DW consultant who tried to force things into the OLAP/BI framework. That was one of the main reasons for the project's failure.


ThomasLN
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Yes, this indeed is an excellent, detailed overview look at data warehousing.

However, as others have suggested here, the data warehousing concept would not exist except for its value to the business and management. In that regard, it is imperative to remember that the central reason for DW at all is to provide pre-calculated and massaged data to management and the business. In this manner, all calculations for a particular count or equation can be managed and, hopefully, the entire enterprise will be looking at the same number calculated in the same way at a given time.

As indicated in your diagram, there are usually two distinct types of data that can be reported on and which require entirely different processing methods. OLAP (cubes) can do an excellent job of calculating based on historical records kept in a cube structure but is not as efficient at state data. State data is not usually historical and benefits from the data warehouse's ability to summarize data across the enterprise, joining data from probably very disparate systems.

Also, it is important for the enterprise to pay close attention to data warehouse metadata as depicted in your very accurate diagram. Metadata is the prime tool in the data warehouse for managing data quality. In this manner, all incorrect and inappropriate data can be automatically categorized and attended to by the data quality team through use of metadata reporting functions. Another aspect of this feature is the transparency of data quality. With proper data quality reporting, it should be patently obvious which data source is providing good data and which is not.


Stephen Hirsch
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"hopefully, the entire enterprise will be looking at the same number calculated in the same way at a given time"

Sounds great in theory, but it never works out in real life. I've found that the biggest difficulties with DW are emotional. When you bring data sources from different places, something is always wrong. Plus, you look under all the rocks and find really nasty stuff.

Finally, there is never just one version of the truth. Again, sounds nice in theory, but there is a reason why organizations break down into silos.


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