OLAP and Analytics
May 17, 2008
an·a·lyt·ics (n-ltks)
n. (used with a sing. or pl. verb)
The branch of logic dealing with analysis.
Taken from http://searchcio.techtarget.com/
“OLAP (online analytical processing) is computer processing that enables a user to easily and selectively extract and view data from different points-of-view. For example, a user can request that data be analyzed to display a spreadsheet showing all of a company’s beach ball products sold in Florida in the month of July, compare revenue figures with those for the same products in September, and then see a comparison of other product sales in Florida in the same time period. To facilitate this kind of analysis, OLAP data is stored in a multidimensional database. Whereas a relational database can be thought of as two-dimensional, a multidimensional database considers each data attribute (such as product, geographic sales region, and time period) as a separate “dimension.” OLAP software can locate the intersection of dimensions (all products sold in the Eastern region above a certain price during a certain time period) and display them. Attributes such as time periods can be broken down into subattributes.
OLAP can be used for data mining or the discovery of previously undiscerned relationships between data items.”
An important element of any Business Intelligence deployment, OLAP cubes deliver vital packages of data in a rich and readily consumable form. OLAP has suffered from a lack of standards and remains proprietary technology for most vendors.
The advent of MDX the multi-dimensional query language at the heart of SQL Server SSAS has opened this up slightly, this standard has also been adopted by Cognos in it’s Series 7 and 8 product lines and by the open source project Pentaho.
As hardware capabilities constantly improve and specialised data warehouse ready databases come to the fore the jury is out on the future of OLAP technologies.
OLAP however, for now offers unique functionality which enables rapid analysis of multiple fact tables at different levels of granularity. MDX queries also allow otherwise complex queries to be implemented quickly and easily with a few lines of code. An example of this capability would be the comparison of year to-date sales figures for this year vs. last.
The stand out OLAP implementation in today’s marketplace is Microsoft’s SQL Server SSAS and the best way to extend this capability inside and outside your organisation is by deploying Logi OLAP.
