Last month, Amit Manghani, SAP, provided an ASUG webcast on BusinessObjects Data Quality Management for SAP Solutions. Below is a summary.
The agenda is below:
- Poor Data Quality – What is the impact? What is the cause?
- Impact of Poor Data Quality within CRM
- Solution Overview
- What’s New in 4.0
- High-level Architecture
Poor Data Quality – What is the impact? What is the cause?
Figure 1: Source, SAP
Figure 1 shows how poor data quality can impact organizations, contributing to low profits, poor customer relations and low productivity.
What are the causes and sources of bad data quality? Data can be entered via data migration, customer self service, employee front lines. Amit said organizations should seek to have a data quality firewall to prevent dirty data from entering their systems.
Impact of Poor Data Quality within CRM
Figure 2: Source: SAP
Figure 2 shows what happens when good data quality practices are not followed. Duplicate and redundant data will cause customer dissatisfaction.
Figure 3, Source: SAP
Figure 3 provides an overview of SAP Data Quality Management for SAP.
Figure 4, Source: SAP
Figure 4 shows capabilities offered in real time mode. Address cleansing will add missing elements and parse the address. It is case insensitive. Suggestion list will allow users to select the correct address. Duplication checking offers matching capabilities.
Figure 5, Source: SAP
Figure 5 shows how this would work. Data is entered incorrectly; minimal information has been entered. This SAP solution will correct the address data – it will parse it into the correct components and parse it correctly. The house and street name have been parsed correctly and the city and state have been entered.
The street name has been enhanced; the address directory will allow you to get the correct address.
Figure 6, Source: SAP
Figure 6 shows an example of duplicate checking within SAP CRM.
Figure 7: Source, SAP
Figure 7 shows an example of how this works in SAP ERP. The address data is entered, parsed, cleansed and the user has the ability to validate the address and check for duplicates.
Figure 8, Source: SAP
Figure 8 shows capabilities in batch mode.
Address data goes in decay over time so you can run the “quarterly adjustment report” and batch matching to identify duplicates.
What’s New in 4.0
4.0 of Data Quality Management is now in ramp-up. The latest solution is fully compatible with Data Services 4.0, including the improved suggestion list functionality. Security and Encryption enhancements have been added. You can encrypt the communication between RFC and Data Services, thus making this more secure.
Figure 9, Source: SAP
Figure 9 shows the improved suggest list functionality. The latest version will offer the ability to skip the secondary address.
Figure 10, Source: SAP
Figure 10 shows the additional fields added to address cleansing.
Figure 11, Source: SAP
Figure 11 shows the ease of maintenance with additional IMG nodes around region mappings, customer address review, maintain suggestion list labels.
High Level Architecture
Figure 12, Source: SAP
Figure 12 shows the 3 main areas of architecture:
1) SAP application
2) RFC component
3) Data Services 4.0
This solution plugs into the Business Service Address framework, part of SAP Netweaver.
It uses postal validation interface and duplicate error tolerance
Solution provides BAdi’s, and the RFC component to facilitate communication between SAP and Data Services.
Figure 13: Source: SAP
Figure 13 shows a summary of the benefits. Fixing the data upstream at the point of entry saves you downstream.
Figure 14: Source: SAP
Figure 14 shows where Data Quality Management resides in SAP
Question & Answer
Q: Is there a fee to upgrade from DQM 3.0 to 4.0?
A: It is part of maintenance you should have ability to upgrade without a fee
Lastly I want to thank SAP and SAP’s Ina Felsheim, who is a great SAP point of contact to ASUG, for arranging this webcast.