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In my last blog post, I introduced our GitHub sample for working with SAP Datasphere and SAP Data Quality Management, microservices. Today we are announcing our new sample to show how you might create a data quality dashboard from your address cleansing and geocoding results. If you are not familiar with our GitHub sample structure, please refer to the previous article first.
Dashboard sample using the result from SAP Data Quality Management, microservices for location data
Our sample can be found in the SAP-samples / cloud-dqm-sample-payloads repository.
This sample uses the additional details generated during the address cleansing and geocoding process to create a statistical visualization using SAP Datasphere and SAP Analytics Cloud.
Data Quality Dashboard
In SAP Analytics Cloud, you can create a dashboard to visualize the total number of records for various informational codes received from SAP Data Quality Management microservices for location data. In our example, we used the pie and bar chart representations as shown below.
Output Field Properties
SAP Data Quality Management, microservices for location data generates several Informational codes that can be used to create reports. These codes indicate how well the address data was matched to the reference data and provide additional information if the best possible results are not achieved. To generate these codes, you must include the informational codes as output field properties in each request to the service.
The sample dashboard above was created using some of these output fields, but you can create your own reports using any output field properties as described in the documentation: Assignment Codes and Identify an Invalid Address.
Address Validation Summary
The output field addr_asmt_info provides the information about the validity of the address. If it is V (Valid) or C (Corrected), it is considered that the address returned is validated to the full depth in the reference data. If it is I (Invalid) or B (Blank), you may be able to remedy these addresses by enabling the Suggestion Lists properties.
Valid
V (Valid) | Address was validated based on the depth of reference data and received no changes or only minor changes |
C (Corrected) | Address was corrected during the validation process and was validated based on the depth of reference data |
Invalid
I (Invalid) | Address did not not match to the full depth of reference data |
B (Blank) | The request had blank values in all address input field properties |
Address Validation Level
The output field addr_asmt_level provides the level that the address matches to the address reference data.
Premise
S (Secondary) | Address was validated to the secondary address information such as a floor and unit number |
PR (Primary Range) | Address was validated to the house number |
Street
PN (Street) | Address was validated to the street |
City
L4 (Locality4) | Address was validated to the fourth level of city |
L3 (Locality3) | Address was validated to the third level of city |
L2 (Locality4) | Address was validated to the second level of city |
L1 (Locality1) | Address was validated to the first level of city |
Region
R (Region) | Address was validated to the region |
C (Country) | Address was validated to the country |
Unassigned
X (Unassigned) | Address was not validated to any level of data |
Geocode Validation Level
The output field geo_asmt_level provides the level that the address matches to the geocode reference data.
Premise
PRE (Primary Range Exact) | Address was geocoded to the exact house location |
PRI (Primary Range Interpolated) | Address was geocoded to the interpolated location based on the house number range |
Street
PN (Primary Name) | Address was geocoded to the midpoint of a street segment |
Postcode
PF (Postcode Full) | Address was geocoded to the center of a full postcode area |
P2P (Postcode2 Partial) | Address was geocoded to the center of a partial postcode2 area |
P1 (Postcode1) | Address was geocoded to the center of a postcode1 area |
City
L3 (Locality3) | Address was geocoded to the center of a third-level city area |
L2 (Locality2) | Address was geocoded to the center of a second-level city area |
L1 (Locality1) | Address was geocoded to the center of a first-level city area |
Unassigned
X (Unassigned) | Address was not geocoded to any level of data |
Reasons for Invalid Addresses
The output fields addr_info_code and addr_info_code_msg provide the potential reasons why an address was not fully validated to the full depth in the reference data. You can find the list of codes and associated messages in the documentation: Information Codes for Address Cleanse. You may be able to remedy these addresses by enabling the Suggestion Lists properties.
Information Code
1000’s | Potential problems related to the input country or script |
2000’s | Potential problems related to the input city, region, or postcode |
3000’s | Potential problems related to the input street, building, house number, or postbox number |
4000’s | Potential problems related to the input secondary address information or organization name |
5000’s | Miscellaneous scenarios encountered when matching the input address to reference data. Not all of these scenarios are necessarily problems |
We Value Your Feedback!
In this blog post, we showed an example of interpreting the output data generated by SAP Data Quality Management, microservices for location data, creating the data model in SAP Datasphere, and visualizing the data to create a data quality dashboard.
If you like what we did in our sample, we would appreciate your feedback on it. Is there any additional information you would like to get from the service? Do you want to see similar reports from the Data Quality Service UI? If you would like to send us an enhancement request, please use the following channel to contact us.
- Enhancement requests to SAP Data Quality Management, microservices for location data
- Enhancement requests to SAP Datasphere
- Enhancement requests to SAP Analytics Cloud
- Enhancement requests to SAP S/4HANA Cloud for MDM
