The term Analytics mean a branch of logic dealing with analysis. So we can safely assume that Siebel Analytics means branch of Siebel dealing with Analysis. Siebel has always been transactional application and it is very difficult to do analysis of data that is residing in Siebel. Just to give you an example of what I mean.
Suppose a sales manager wants to know that:
How many opportunities in the last 3 months, from US Region for Product A, have a sales figure of over 3 million dollars?
I don’t think there is an easy way to get this kind of data in Siebel easily and this is just very small requirement that a sales manager might have it can get very complex easily.
This is where Siebel Analytics comes into picture. It is a wrapper over Siebel Application.
Siebel Analytics allow an enterprise to measure and evaluate business performance across customers. It helps in analyzing past, present and future opportunities with the help of Dashboard Reports to determine actions required to meet the sales targets. With the help of Dashboard reports we can determine which products and customers are generating most revenue. For understanding Siebel Analytics in more depth one has to know the basic difference between OLAP and OLTP. OLTP stands for On Line Transaction Processing:OLAP stands for On Line Analytical Processing
The data available at transaction side (Siebel Application) is OLTP and when that data is moved from transaction side for analyzing (Siebel Analytics) that becomes OLAP data.
OLAP brings into picture the concept of Data warehouse.
Data warehouse is a Relational /Multidimensional database that is designed for query and analysis rather for transaction processing. A data warehouse usually contains historical data that is derived from transaction data. Another important concept when we are talking about to Siebel Analytics is ETL.
ETL stands for Extract, Transform, and Load.
ETL is a concept that enables businesses to consolidate their disparate data while moving it from OLTP to OLAP and it doesn’t really matter that that data sources are in different forms or formats. The data can come from any source such as Oracle, SQL server, flat files, CSV etc One important function of ETL is “Cleansing” data. ETL consolidation protocols also include the elimination of duplicate or fragmentary data, so that what passes from the ‘E’ portion of the process to the ‘L’ portion is easier to assimilate and/or store.
Such cleansing operations can also include eliminating certain kinds of data from the process. If you don’t want to include certain information, you can customize your ETL to eliminate that kind of information from your transformation. The ‘T’ portion of the equation, of course, is the most powerful. ETL can transform data from different sources. For Example: - Data in an Oracle CRM could be transformed right along with data from an SAP Marketing application, with the result being a common data from both the application.
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