Data Warehouse & OLAP http://www.beyeblogs.com/rajanguptaBIBlog/ Data Warehouse & OLAP is key to fulfilling information needs for an enterprise. Its part science and part art. This blog will provide regular tips around this subject. Most of the content here is from my knowledge portal BiPMinstitute.com. en Copyright 2008 Wed, 02 Apr 2008 02:30:00 -0700 http://www.movabletype.org/?v=3.33 http://blogs.law.harvard.edu/tech/rss Non-Additive Measures in OLAP Additivity and correct aggregation methods application is fundamental to the success of Business Intelligence. The most common mistakes the modelers and designers make is on - Setting the Right Hierarchies AND Establishing Right Additivity and aggregation rules. You need to go through the chapter of business dimensional hierarchies, before you go through this chapter. Additivity of a measure is when you are able to apply the sum operator across all the dimensions. Other aggregations on measures-facts are when you use operators like Average, Maximum and Minimum. The topics in this chapter are focused on the areas which have additivity or aggregation constraints. For measures, which do not fall into these constraints are considered Additive.

The non-additive measures are:

  • Ratios & Percentages
  • Intensity Measures (like temperature)
  • Grades
  • Averages/Maximums/Minimums


The Semi-Additive Measures(Where additivity is over certain dimensions and not on all dimensions):

  • Dirty Data
  • Historical Data
  • Category Data
  • Periodic Snap-shots


The details on this subject can be seen on Additivity of Measures

]]> http://www.beyeblogs.com/rajanguptaBIBlog/archive/2008/04/nonadditive_measures_in_olap.php http://www.beyeblogs.com/rajanguptaBIBlog/archive/2008/04/nonadditive_measures_in_olap.php Wed, 02 Apr 2008 02:30:00 -0700 Data Warehouse is not only for strategic or analytics Data Warehouse as a term has been often used as an integral component of high-end decision support and back-room analytics. The fact is that Data warehouses can be a universal source for any information requirement.

Data Warehouse is a repository of data, whereby the application of that data is ‘limited only’ by the detail and the way data is stored. Query and analysis, business modeling , data mining are the buzzword use of data warehouses. However, there are more fundamental and bigger usage possible for Data Warehouse. For example enterprise reporting , which can provide you capability to report on Summary level as well as transaction level data, one can drive operational management benefits. You not only can use data warehouse to find the sales trends, but also to generate the list of all 5000 sales officers, along with the list of every sales order which they have booked in last six months.

It all depends on the granularity of the data which is stored in the data warehouse.

Essentially, Data Warehouse is a repository of data, which can be used for unlimited number of applications, as long as you have right tools sitting on top of it, and detailed enough information sitting inside it. To understand more on the possible applications of Data Warehouse, refer Why is Data Warehouse needed

]]> http://www.beyeblogs.com/rajanguptaBIBlog/archive/2008/03/data_warehouse_is_not_only_for.php http://www.beyeblogs.com/rajanguptaBIBlog/archive/2008/03/data_warehouse_is_not_only_for.php Mon, 31 Mar 2008 20:45:00 -0700