White Papers

Sapiex experts have written many white papers on various topics within SAP NetWeaver and SAP organizational culture. 

Many of these papers have been modified and published in BI Expert Magazine (published by Wellesley Information Services).

  • "Strategic Options in SAP BW Data Storage Compared" (1 Nov 2003)

  • "21 Tips for Managing a Multi-Project BW Implementation" (1 Oct 2004)

  • "Load Data into Two BW Systems from One R/3 Source with Zero Downtime" (1 Feb 2005)

  • "7 Principles for Creating Organizational Commitment to EDW Data Quality" (1 Oct 2007)

  • "7 Keys to a Successful SAP NetWeaver BI Implementation or Upgrade" (1 Feb 2008)


7 Keys to a Successful SAP NetWeaver BI Implementation or Upgrade

Review these seven key steps to ensure that your SAP NetWeaver BI 7.0 implementation or upgrade project goes as smoothly as possible. For new implementations, find out what you need to consider as you migrate to SAP NetWeaver BI. For upgrades from BW 3.5 or earlier, understand how you can apply lessons learned with your current implementation;to your SAP NetWeaver BI upgrade. Read More

Add A BW System to An R/3 Source With No Down Time!

Some companies use more than one SAP BW system connected to a lone R/3 instance to satisfy their organizational reporting requirements. Right from the start, there are logistical difficulties deploying and synchronizing data in multiple BW systems and the ERP source. And there is an ever-present risk that users will be forced to endure significant system outages to ensure that the BW systems remain in synch with the transactional system. Read More

BW Query Creation Process & Controls

In most BW implementations, queries are created in the Production system directly, where they are tested and delivered to end-users. The reason for this is that most user communities expect and demand quick turnaround of report;requests into executable reports. Read More

The Six Principles of BW Data Validation

By their nature, data warehouses store large volumes of data. For analytical purposes, the final dataset is frequently aggregated and the transactional detail is lost. When users look at the analytical reports generated out of the BW system, the results they see can not be easily verified. As a result, creating user confidence in data quality becomes difficult and user trust is eroded, which can ultimately undermine the success of the EDW initiative. Read More