ACES and PIES in the Data Warehouse · Use Case 1
What Did We Send, and When Did It Change? Keeping a History of Your ACES and PIES Data
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.
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.
What changes when every export is kept
Load each ACES and PIES export into a database automatically, and keep every change as a dated version instead of overwriting it. The catalog now has a memory.
What did part X look like on March 15: price, dimensions, weight, descriptions, fitment?
What changed between the last two exports, by brand and by PIES segment?
Which SKUs had a price or package change in the last 90 days?
Did anything change that nobody meant to change?
What the business gets
- Disputes settled in minutes. A dated record answers the distributor quickly, whichever side made the mistake.
A check before the damage spreads. A "what changed this month" report catches an accidental bulk edit before every receiver has loaded it.
Self-service for other teams. Sales, customer service and pricing look up history themselves instead of queuing requests with the catalog team.
An audit trail. When a price or a compliance attribute is questioned a year later, the record is already there.
How it works
Nothing changes for the catalog team. They keep maintaining product data in SEMA Data exactly as they do today.
Exports arrive on a schedule by SFTP, one set per brand.
An automated pipeline loads them into Oracle Autonomous Data Warehouse.
Each record is compared with its previous version, and changes are stored with start and end dates.
Dashboards sit on top, open to anyone in the company who needs them.
The same approach works on other databases. The value is in the history, not the brand of warehouse.
What else this opens up
Change history is the first payoff, not the only one. Once your product data sits in a warehouse, it can be put to work in ways an export file never can.
Analytics across systems. Join the catalog to sales, returns and inventory. Which top-selling parts are missing images or dimensions? Which fitment changes were followed by a rise in returns?
Questions in plain English. AI assistants can sit on top of the same data, so a sales rep can ask "which SKUs changed price last quarter?" without learning a reporting tool.
Your rules on where AI runs. Some companies do not allow AI tools to connect to the warehouse. The same pipeline can deliver selected datasets to whichever platform the company has approved, so the policy stays intact.
Each of these is a use case of its own, and later posts in this series will cover them.
Your catalog is an asset worth remembering
Brands spend years building their product data, then keep only the latest copy. A history of that data costs little to keep and pays back the first time someone asks what changed.
We have built this pipeline for a multi-brand aftermarket manufacturer. If the email at the top of this post sounds familiar, I would be glad to show you how it works.
