In the first part of this series, A Step by Step Guide to Getting the Most from Your JD Edwards Data, we walked through the process of collecting JDE data and integrating it with other data sources. In this post, we will show you how to add business logic unique to a company and host analyzable JDE data.
Adding Business Logic Unique to a Company
When working with JD Edwards, you’ll likely spend the majority of your development time defining business logic and source-to-target mapping required to create an analyzable business layer. In other words, you’ll transform the confusing and cryptic JDE metadata into something usable. So, rather than working with columns like F03012.[AIAN8] or F0101.[ABALPH], the SQL code will transform the columns into business-friendly description of the data. For example, here is a small subset of the Customer pull from the unified JDE schema:
Furthermore, you can add information from other sources. For example, if business wanted to include new customer information only stored in Salesforce, you can build the information into the new [Customer] table that exists as a subject area rather than a store of data from a specific source. Moreover, the new business layer can act as a “single source of the truth” or “operational data store” for each subject area of the organization’s structured data.
Looking for pre-built modules?
Aptitive has built out data marts for several subject areas. All tables are easily joined on natural keys, provide easy-to-interpret column names, and are “load-ready” to any visualization tool (eg: Tableau, PowerBI, Looker, Cognos) or data application (eg: Machine Learning, Data Warehouse, Reporting services). Modules already developed include:
|Account Master||Accounts Receivable||Backlog||Balance Sheet||Booking History|
|Budget||Business Unit||Cost Center||Currency Rates||Customer Date|
|Job Costing||Purchase Orders||Sales History||Tax||Territory|
Hosting Analyzable JDE Data
After creating the Data Hub, many companies prefer to warehouse their data in order to improve performance by time boxing tables, pre-aggregating important measures, and indexing based on frequently used queries. The data warehouse also provides dedicated resources to the reporting tool and splits the burden of the ETL and visualization workloads (both memory intensive operations).
By design, since the business layer is load-ready, it is relatively trivial to extract the dimensions and facts from the data hub and build a star-schema data warehouse. Using the case from above, the framework would simply capture the changed data from the previous run, generate any required keys, and update the corresponding dimension or fact table:
Evolving Approaches to JDE Analytics
To conclude, this approach to analyzing JD Edwards data allows business to vary the BI tools they use to answer their questions (not just tools specialized for JDE) and change their approach as technology advances.
We hope you found this guide helpful. Aptitive has implemented the JDE Analytics Framework both on premise and in a public cloud (Azure and AWS), as well as connected with a variety of the analysis tools including Cognos, PowerBI, Tableau, and ML Studio. We have even created API access to the different subject areas in the data hub for custom applications. In other words, this analytics platform enables your internal developers to build new business applications, reports, and visualizations with your company’s data without having to know RPG, the JDE backend, or even SQL!
If you’re looking for a strategic partner to help you get the most from your JDE data, contact us today for a JDE data assessment.
This post was originally posted on Medium.
Greg Marsh is a Data Engineer Manager at Aptitive. In his role, Greg facilitates the discovery of business insights from data. From “Big” data like IoT streams or classic relational ERP information, Greg helps companies to unlock the power of their data.