Why Data Warehouse Projects Fail ???
The overriding reasons for many decision support projects failure is not that the projects were technically unfeasible. On the contrary, many of the technological challenges of data warehousing have proven answers. The most common cause of failure is that the warehouse did not meet the business objectives of the organization.
Measurement of clear business objectives is critical. Because once the data warehouse project is completed, the management team will have to justify the expenditure. The users always dictate the success or failure of the warehouse. They therefore need to be heavily involved throughout the data warehousing project.
Any substantial project lacking executive management participation has a high probability of failure.
Like any other large information systems project, data warehouse development can get bogged down if the scope is too broad and the number of people involved is too large. A clear purpose and scope are necessary to manage the application of information systems resources, as well as the expectations of potential data warehouse users.
Many large warehouse projects have failed because of an inability of the organization to handle the size and scope of the project. It is tempting to think of a single repository where all of the enterprise's data problems can be solved in one fell swoop.
For the initial data mart, which usually provides the data warehouse proof-of-concept, the scope must be sufficient to provide real, immediate, and high profile benefits.
Blindly copying dirty source data into the data warehouse is extremely dangerous. Dirty data can lead business analysts to make erroneous business decisions and rapidly causes end users to lose confidence in the quality and integrity of the data warehouse. Lack of data integrity in the data warehouse is a common source of failure of many data warehouses and analytical applications.
Disaster recovery is a particular challenge, given the large volumes of data found in today's warehouses. Many organizations just do not take disaster recovery seriously until they had some catastrophic experience.
Unpredictable or unreliable systems management functions will raise user questions over the quality of the information and the usefulness of the data warehouse. In a worst-case scenario, poorly planned and executed system management functions can easily lead users to discontinue using their existing data warehouse applications and halt the funding of new applications.
Measurement of clear business objectives is critical. Because once the data warehouse project is completed, the management team will have to justify the expenditure. The users always dictate the success or failure of the warehouse. They therefore need to be heavily involved throughout the data warehousing project.
Any substantial project lacking executive management participation has a high probability of failure.
Like any other large information systems project, data warehouse development can get bogged down if the scope is too broad and the number of people involved is too large. A clear purpose and scope are necessary to manage the application of information systems resources, as well as the expectations of potential data warehouse users.
Many large warehouse projects have failed because of an inability of the organization to handle the size and scope of the project. It is tempting to think of a single repository where all of the enterprise's data problems can be solved in one fell swoop.
For the initial data mart, which usually provides the data warehouse proof-of-concept, the scope must be sufficient to provide real, immediate, and high profile benefits.
Blindly copying dirty source data into the data warehouse is extremely dangerous. Dirty data can lead business analysts to make erroneous business decisions and rapidly causes end users to lose confidence in the quality and integrity of the data warehouse. Lack of data integrity in the data warehouse is a common source of failure of many data warehouses and analytical applications.
Disaster recovery is a particular challenge, given the large volumes of data found in today's warehouses. Many organizations just do not take disaster recovery seriously until they had some catastrophic experience.
Unpredictable or unreliable systems management functions will raise user questions over the quality of the information and the usefulness of the data warehouse. In a worst-case scenario, poorly planned and executed system management functions can easily lead users to discontinue using their existing data warehouse applications and halt the funding of new applications.
What is OBIEE ??
OBIEE stands for Oracle Business Intelligence Enterprise edition (Formerly known as siebel analytics). Siebel Analytics is a Data Warehouse that were coming out of the box with pre configured ETL mappings from source (siebel crm) to target (DW)with its reporting component DASHBOARD (Siebel Answers, Disconnect analytics and Administration tool to build dashboard reports => .rpd file)
When Oracle Corp. has acquired Siebel System and decided to make siebel analytics their flagship analytic engine and renamed it OBIEE (obiee server + ETL engine+ Reporting). Now this product is coming with preconfigured ETL mappings and DW schema available for Siebel CRM, Oracle E Business Suite (Financials, HR etc.), JD Edwards, and SAP.
So keep in mind OBIEE is a complete Data Warehouse technology
(server+application+ ETL+report)where Business Object (BO) is reporting component which is one of the best reporting product available in the market. BO can also access and deliver reports that can be build based on OBIEE DW tables
When Oracle Corp. has acquired Siebel System and decided to make siebel analytics their flagship analytic engine and renamed it OBIEE (obiee server + ETL engine+ Reporting). Now this product is coming with preconfigured ETL mappings and DW schema available for Siebel CRM, Oracle E Business Suite (Financials, HR etc.), JD Edwards, and SAP.
So keep in mind OBIEE is a complete Data Warehouse technology
(server+application+ ETL+report)where Business Object (BO) is reporting component which is one of the best reporting product available in the market. BO can also access and deliver reports that can be build based on OBIEE DW tables
How and where to DownLoad OBIEE
1.Please log on http://edelivery.oracle.com/
2.Select English, and click on Continue
3.Give the details, Even dummy will do
4.Please check the Check Boxes
5.Click on Continue
6.Select Busines Intelligence
7.Click on GO
9. Click on Oracle Business Intelligence (10.1.3) Media Pack for Microsoft Windows (32-bit)
10. Download Oracle Business Intelligence Suite Enterprise Edition 10.1.3.4.0 for Microsoft Windows1 of 3) B50964-01 Part 1 of 3 217M
12Oracle Business Intelligence Suite Enterprise Edition 10.1.3.4.0 for Microsoft Windows (Part 2 of 3) B50964-01 Part 2 of 3 692M
13.Oracle Business Intelligence Suite Enterprise Edition 10.1.3.4.0 for Microsoft Windows (Part 3 of 3)
Please unzip the downloaded contents into one Folder And follow the Instructions for Installing in Next Blog
for questions Please email training@escalglobal.com, I will be happy to help with installation
