What Did We Send, and When Did It Change? Keeping a History of Your ACES and PIES Data

 ACES and PIES in the Data Warehouse · Use Case 1

If you manage ACES and PIES data for an aftermarket brand, you know this email.

A distributor writes that the package weight on 40 of your SKUs is wrong, and they have been absorbing freight adjustments for two months. Or a reseller disputes a price. Or returns on one part number jumped right after a fitment update.

The first question is always the same: what did our data say on the day they pulled it? Most brands cannot answer it.

For the technical design refer our post :Automating ACES and PIES Loads: A Metadata-Driven Pipeline from SEMA Data to Oracle ADW.

Why the answer is so hard to find

Your PIM shows the current state of the catalog. That is its job, and it does it well. It was not built to show what a record looked like three exports ago.

So history lives wherever someone happened to save it. Last month's ACES and PIES export is a zip file on a laptop or a shared drive, if it was kept at all.

Answering one dispute means opening item, price, package, description and fitment files in Excel and comparing them by eye. With several brands, that is days of work for the one person who can least spare it.

CASE STUDY & PLAYBOOK: Modernizing ODI 12c to 14c on OCI Marketplace for Mid-Market Enterprises

Modernizing/Upgrading  ODI 12c to 14c PUBLISHED BY: Biz Insight Consulting Inc.

1. EXECUTIVE SUMMARY & CLIENT FOOTPRINT

A mid-market manufacturing enterprise operated a lean IT team managing a mission-critical Oracle data ecosystem: 

THE CHALLENGE:

1. Single-Point-of-Failure: Operating only one ODI and ADW instance (PROD only).
2. Expanding Footprint: Data extraction grew from core EBS modules to include SFTP flat files, REST APIs, and multi-cloud storage (OCI, AWS, SharePoint).
3. Unregulated Data Access: End-users executing custom SQL directly against production.
4. Platform Modernization: Upgrading to ODI 14c on Linux 8 while preparing for  Oracle 26ai AI/Vector capability upgrades.

Automating SEMA-to-ADW Data Integration: A Complete Case Study

The Business Challenge

In the automotive aftermarket parts industry, SEMA (Specialty Equipment Market Association) is a critical data source — publishing standardized product and vehicle-fitment data that brands rely on for accurate cataloging and compatibility information.

The data itself follows two established industry formats: PIES (Product Information Exchange Standard), covering detailed product attributes, descriptions, digital assets, and pricing; and ACES (Aftermarket Catalog Exchange Standard), covering vehicle fitment — which parts fit which vehicles.

Each brand's data is delivered as a set of files, refreshed on a recurring schedule, landing on an SFTP server. Multiply that across every brand a business tracks, and you get a real operational problem: someone has to notice the files arrived, download them, sort out which brand and which format each one belongs to, and load them correctly — every single cycle, without missing one, without loading a duplicate, and without silently corrupting last month's data if this month's file has an issue.

Doing that by hand doesn't scale, and it doesn't fail loudly when it breaks — it fails quietly, in the form of a stale report nobody noticed for three weeks.

The Solution at a Glance

Automating ACES and PIES Loads: A Metadata-Driven Pipeline from SEMA Data to Oracle ADW.

Managing automotive aftermarket catalog data across multiple product lines is a complex, data-heavy process. Standard industry deliverables arrive via two core data formats: Product Information Exchange Standard (PIES) for comprehensive part specifications and Aftermarket Catalog Exchange Standard (ACES) for vehicle fitments. When organizations handle these incoming catalog updates manually, they face significant operational challenges—including frequent human errors, a lack of audit trails, difficulty scaling across new brands, and an inability to track historical attribute or pricing changes over time.

To eliminate these inefficiencies, an automated catalog integration pipeline can be built using Oracle Data Integrator (ODI) and Oracle Autonomous Data Warehouse (ADW). Here is a high-level architectural look at how a metadata-driven pipeline ingests, cleanses, and historizes high-volume automotive catalog updates.

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For Business Use Case refer Post : What Did We Send, and When Did It Change? Keeping a History of Your ACES and PIES Data

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