Amongst all the ERP and CRM packaged applications we support for metadata discovery it might on the surface seem surprising that the level of interest for Safyr with Oracle’s JD Edwards EnterpriseOne system is close behind SAP and Salesforce.
In fact breaking down the JD Edwards data silos is a challenge for most users of the application.
After all JD Edwards does not have that many tables, around 4,000-5,000 compared with say 90,000 in SAP or over 20,000 in Oracle EBS. There are however some additional challenges with its data model which are even more difficult to overcome than in other packages.
The table and attribute names are possibly even more impenetrable than those enjoyed by SAP users. Tables names all follow a format of letters and numbers. You can see in the diagram below that table F0014 is actually called ‘Payment Terms’ and that the fieldnames in the database are also difficult to fathom until you see how Safyr extracts and maps their business friendly names; e.g. PNNDTP is called Net Days to Pay.
You can also see the Primary and Foreign keys used by that table which are designated by the Gold key and that Green connector icon.
Another challenge presented by JD Edwards is the fact that these table relationships are not defined in the data dictionary tables. There is a construct called Business Views within JD Edwards that can be used to suggest where relationships might exist. The Business Views are a layer above the Table definitions that are the basis for building reports, and conceptually they are a lot like a Database View. In most other packages relationships are defined in the data dictionary tables so it is relatively straightforward for Safyr to find and extract them.
So how does Safyr make is easier for you to gain insight into the JD Edwards data model and what can it help you to achieve?
Once you have installed Safyr the next part of the process is to connect to the JD Edwards system and extract the metadata.
Safyr then infers relationships using three methods:
- By looking at the Business Views and picking out potential relationships
- Using an inference engine to detect potential Primary Key – Foreign Key pairs
- Using a spreadsheet of Parent and Child table names (this can be controlled by the user) to build relationships
Once you have completed this ‘reverse engineering’ task to bring the JD Edwards metadata into Safyr (this will take around 20 mins), you have a complete repository of the metadata in your JD Edwards system including any customisations which have been made to it.
Now you can use Safyr’s search and filtering functionality to quickly find the tables and associated tables which are related to the your business need at the time.
You can also use the JD Edwards System Code Hierarchy which Safyr creates. If you know which one you want to review you can select it as in the diagram below. If not you can use the search facility within the Hierarchy to find what you need.
The next step is to create a Subject Area from the results you have found. Subject Areas can be exported to a variety of different tools and technologies. In the diagram below the we have used one of Safyr pre-configured Subject Areas for Benefits Administration and exported it using Safyr’s ERDiagrammer.
So what can you do with this metadata? Well firstly by giving you and your data analysts access to hi-fidelity metadata of this kind, Safyr is helping to break down the JD Edwards data silos. Safyr also addresses this problem for other ERP and CRM packages from SAP, Oracle, Microsoft and Salesforce.
It will help you to discover the tables you need more quickly and accurately than with traditional methods and let you accelerate time to delivery for your projects. It will help you manage the cost of data discovery by reducing the need for external consultants or internal specialists.
Safyr gives you a central point of reference for your JD Edwards metadata that can be shared across different project meaning there is no need to continually reinvent the wheel each time you start a new data management project which needs JD Edwards data.
Typical projects for which Safyr is commonly used include data warehouse and ETL, data catalog and governance, data modeling, data migration, application rationalisation and consolidation.
Learn more about how Safyr works to help you break down JD Edwards data silos.