// DEEP DIVE | PRODUCT DATA
Data Governance: Trust and Control for Your AI
Data governance is the safety net that keeps AI from amplifying existing data problems instead of solving them. Garbage in, garbage out hits AI harder than classic software: what the AI does not know, it makes up. What is broken in the source data gets reproduced at ten times the volume. Clear gatekeepers, validation rules, and a human-in-the-loop process are the answer.
THE PROBLEM
When AI produces errors at ten times the speed
AI is an amplifier. Set up a pipeline for 10,000 product descriptions, and if the material classification in the source is inconsistent, you do not end up with 10,000 clean descriptions. You end up with 10,000 descriptions carrying the same classification problem. The scale lands on the error side, not the value side.
Then there is the LLM-specific issue of hallucinations: when the AI cannot derive a piece of information cleanly from the data foundation, it invents it. In marketing copy this might still be tolerable. In technical datasheets, compliance documents, or regulated industries, it is a knockout criterion.
Deploying AI without governance means trading control for speed. Deploying AI with governance means keeping control and getting speed on top. That is exactly the difference a well-designed governance setup makes.
What does data governance mean in an AI context?
Data governance in an AI context means clear rules for which data may be processed, when an AI output gets approved, and which checks sit between input and production. Compared to classic data processing, three additional safety layers come into play: pre-validation checks whether a record may enter the pipeline at all. Confidence scores during processing stop the AI when it is unsure. Post-validation tests the result against defined quality rules. Only when all three layers turn green does the record go live.
Our safety net for your data
/ OUR APPROACH
01
Step 01
Pre-validation
Before any AI processing, we check: may this record even be processed? Does it contain sensitive data (PII) that needs to be masked? Is the source trusted? Is the format as expected? Anything that fails the check gets routed out before the AI ever sees it. Clean intake control is always cheaper than after-the-fact correction.
02
Step 02
In-process monitoring through confidence scores
Every AI decision gets a confidence score between 0 and 100. If the value falls below the defined threshold (typically 80 %), the AI stops processing and marks the record as uncertain. That is the key difference from naive AI deployment: the AI admits when it is unsure instead of hallucinating.
03
Step 03
Post-validation against schema
After processing, a rule engine checks the result against your data schema. Are all required fields filled? Are units consistent (mm vs. millimeters)? Are values within expected ranges? Records that fail go to the human-in-the-loop. Records that pass go live.
04
Step 04
Human-in-the-loop and feedback
The uncertain 10 to 20 percent land with a data steward, not with an unknown bulk worker. In the review interface, the steward sees only the red cases, can approve, correct, or reject them. Every correction flows back as feedback into the model and sharpens confidence scoring the next time around.
Preventing hallucinations through grounding
The most effective tool against hallucinations is grounding: the AI may answer only on the basis of your stored documents and data sources, not from its general model knowledge. If it wants to make a statement that cannot be derived from the sources, it has to flag that transparently. On top of that, a second AI instance checks the output against the sources (fact check). Only then is the record allowed to continue.
98 %
completeness rate on automatically enriched product data
Starting points on manually maintained records typically range between 40 and 60 percent. With the three-layer governance setup (pre, in-process, post), we reach over 98 percent in production without the AI making up values. What is missing gets clearly marked as "missing" and gets enriched through the human-in-the-loop.
How we check data quality
Dimension
Explanation
Xanevo check
Completeness
Are all required fields filled?
Auto-check against schema, missing values marked as "missing"
Consistency
Is it "mm" or "millimeters" everywhere?
Normalization via glossary, units enforced uniformly
Recency
Is the data stale?
Timestamp check, alerts when data is older than threshold
Plausibility
Are values within the expected range?
Range checks, outlier detection against historical patterns
Traceability
Who changed what when?
Audit trail with user ID, timestamp, before-after comparison
Compliance and GDPR: data protection is non-negotiable
Wherever personal data is processed, automatic PII masking kicks in. Names, addresses, and identifiers get redacted before the text reaches an LLM. The audit trail documents every change with user ID and timestamp so audits can immediately show who changed what when. Processing runs on EU servers, ISO 27001 compliant, with a GDPR-compliant data processing agreement signed before project start.
Three questions data officers ask
Through two mechanisms: first, grounding, which forces the AI to answer only on the basis of your stored documents and data sources. Second, confidence scores that automatically flag uncertain statements for review instead of shipping them as confident answers. Where the AI does not know, it admits it instead of making something up.
You do. We use enterprise instances of the AI providers. Your data does not feed into the training of public models. Contractually governed in the data processing agreement, technically enforced by using API endpoints with an explicit no-train flag.
As an approval workflow before the publish status. Records that have passed all three safety layers go live directly. Records in the human-in-the-loop land in a defined status ("review needed") and get routed to the right stewards through your existing permission model.
Related deep dives
Taxonomy and Classification
How structured taxonomies (ETIM, ECLASS, GS1) lay the foundation for clean AI classification.
Golden Records
How multiple sources become a consolidated single source of truth that governance can validate against.
// DATA GOVERNANCE
Where does your data quality stand today?
In the governance audit we review your current data policies, identify the critical gaps, and deliver a concrete recommendation for your safety model. Structured, documented, with clear next steps. Not a generic audit report.