// DEEP DIVE | PRODUCT DATA ENRICHMENT
Data Ingest: catch supplier data before it does damage
Every supplier sends data their own way. One ships an Excel file with 80 columns, the next a PDF spec sheet, the third an EDI stream. Data ingest captures all of it automatically, standardizes it, and checks it before it ever reaches your PIM. Clean data at the entry point is the precondition for every enrichment step that follows.
THE PROBLEM
Why supplier data rarely lines up
Product data is not born inside your company. It comes from hundreds of suppliers, and each one follows its own logic. Column names differ, units are metric in one file and imperial in the next, dimensions sit in a dedicated field here and buried in the description there. On top of that come formats never meant for machines at all: scanned spec sheets, catalogs as PDFs, tables with merged cells.
As long as this is handled by hand, every new supplier format is a special case. With ten suppliers that works. With three hundred, onboarding becomes the bottleneck, and bad data slips through because no one can review every row. The error does not surface at the entry point. It surfaces in the shop, in the filter, in the search.
The fix is a dedicated entry layer. Data ingest captures data from any source and format, converts it into one consistent schema, and validates it against rules before it ever lands in the PIM. A record moves forward only once it is clean.
What is data ingest?
Data ingest is the automated capture and normalization step at the start of the data pipeline. It accepts raw data in any format (Excel, CSV, PDF, JSON, EDI), detects its structure, maps it into a defined target schema, and validates it before it reaches the PIM or enrichment. Technically it has three parts: connectors for the sources, parsing and mapping logic for standardization, and a rule set for entry validation.
Four steps from raw format to PIM-ready record
/ OUR APPROACH
01
Step 01
Capture through connectors
We pick up data wherever it lives, in whatever format it arrives. SFTP, email attachments, REST, marketplace feeds. Excel, CSV, JSON, and EDIFACT are read directly, while PDFs and scanned spec sheets are opened up through layout recognition and OCR. The supplier changes nothing.
02
Step 02
Parsing and normalization
The raw format becomes one consistent record. We map foreign column names to the correct target fields and standardize units, date formats, and character sets. A new format is configured once and then runs automatically.
03
Step 03
Validation at the entry point
Before a record moves on, a rule set checks completeness, data types, and plausibility. Whatever does not fit is not waved through. It is quarantined and flagged. That keeps broken data out of the PIM.
04
Step 04
Handover to PIM and enrichment
The validated, normalized records go to the PIM or straight into enrichment, along with a log of where they came from and what happened to them. That is the foundation for data governance and clean golden records.
We read CSV, Excel, JSON, XML, and EDIFACT directly, and combine layout parsing with OCR for unstructured sources. Rule-based and transparent, no black-box model, no vendor lock-in.
The goods-receiving analogy
Picture a well-run warehouse. No item goes straight to the shelf. It arrives at goods receiving, gets checked, labeled, and only then stored. Data ingest is goods receiving for your product data. Whatever does not pass the entry point cleanly has no business in the warehouse.
Hours
instead of weeks to bring a new supplier into production
Once a format is configured as a connector, onboarding another supplier takes hours instead of days or weeks of manual mapping. How big the jump is depends on the complexity of the format. A clean CSV connects fast, a nested PDF needs more groundwork.
Why an entry layer beats a direct import
Attribute
Direct import into the PIM
Data ingest as an entry layer
New supplier format
Manual mapping, days to weeks
Configured connector, automatic after that
Faulty data
Surfaces only in shop or filter
Caught and flagged at the entry point
Format variety
Every format is a special case
Excel, CSV, PDF, JSON, EDI unified
Scaling
Grows with headcount
New sources without new maintenance
Traceability
Barely documented
Every source and transformation logged
Three questions CDOs and AI architects ask
No. The adaptation happens on our side. Your suppliers keep delivering as before, and the connector handles capture and normalization.
It is caught at the entry point, quarantined, and flagged, instead of slipping unnoticed into the PIM.
Once the format is configured as a connector, every further delivery runs automatically. The initial connection takes anywhere from hours to a few days, depending on the complexity.
Related deep dives
Golden Records and Master Data
After ingest, sources are consolidated. Golden records turn the captured data into the one record you can rely on.
Data Governance
Entry validation is the first lever for data quality. Data governance keeps it stable in operations.
// DATA INGEST
Let us talk through your
data onboarding.
Show us two or three supplier formats that give you trouble today. We will sketch how capture and normalization would work for them, show you on your real files where the pitfalls are, and tell you what the connection costs. Within a week.