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 descriptions of the data. For example, here is a small subset of the customer pull from the unified JDE schema:

Adding Business Logic Unique to a Company
Furthermore, you can add information from other sources. For example, if a 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.

operational data store

Looking for Pre-built Modules?

2nd Watch 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 (e.g., Tableau, Power BI, Looker) or data application (e.g., machine learning, data warehouse, reporting services). Modules already developed include the following:

Account Master Accounts Receivable Backlog Balance Sheet Booking History
Budget Business Unit Cost Center Currency Rates Customer Date
Employee General Ledger Inventory Organization Product
Purchase Orders Sales History Tax Territory Vendor

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, because the business layer is load-ready, it’s 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:

Hosting Analyzable JDE Data

Simple Star Schema

Evolving Approaches to JDE Analytics

This approach to analyzing JD Edwards data allows businesses to vary the BI tools they use to answer their questions (not just tools specialized for JDE) and change their approach as technology advances. 2nd Watch 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 analysis tools, including Cognos, Power BI, 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!

Evolving Approaches to JDE Analytics

High-level JDE Data Flow

Looking for more data and analytics insights? Download our eBook, “Advanced Data Insights: An End-to-End Guide for Digital Analytics Transformation.”

rss
Facebooktwitterlinkedinmail