Data Catalog Tagging and Classification
A data catalog is only useful if it's actually filled in, and manually tagging every table and column in a warehouse with hundreds or thousands of objects — which ones contain personally identifiable information, which business domain they belong to, which team owns them — is a project big enough that most catalogs launch with an initial tagging pass and then fall behind the moment new tables start getting created faster than anyone can classify them by hand, leaving a growing share of the warehouse permanently unclassified and a compliance team unable to confidently answer 'where does customer PII live' without a manual search.
STARTING PRICE
From €299
Standard tier · Multi-step workflow with AI extraction/decisioning and 2-3 integrations.
Get a quote →Saves roughly 10-20 hrs/month of manual tagging effort plus a catalog that actually keeps pace with a growing warehouse instead of falling permanently behind.
How the automation works
We scan warehouse schema and sample column data to classify tables and columns automatically — flagging likely PII columns by pattern matching against known formats (email addresses, phone numbers, national ID formats) and column naming conventions, tagging tables by business domain based on their relationships to already-classified tables, and assigning a confidence score to each automatic classification so low-confidence tags get routed for human confirmation instead of silently treated as certain. New tables get classified as part of the same pipeline that creates them, so the catalog keeps pace with warehouse growth instead of falling permanently behind the way a manual-only tagging effort inevitably does.
Process flow
- 01
Scan warehouse schema and sample data integration
Table and column schemas are scanned, along with sampled (not full) column data, to gather the signal needed for automatic classification.
- 02
Classify likely PII columns ai
Columns are checked against known PII patterns — email formats, phone numbers, national ID formats — and naming conventions to flag likely sensitive data.
- 03
Classify tables by business domain ai
Tables are tagged by business domain based on naming, schema relationships, and proximity to already-classified tables in the dependency graph.
- 04
Score classification confidence ai
Each automatic tag gets a confidence score, with low-confidence classifications routed to a human reviewer rather than accepted silently.
- 05
Classify new tables automatically trigger
New tables entering the warehouse are classified as part of the same pipeline, keeping catalog coverage current with warehouse growth rather than falling behind.
Inputs
- Warehouse schema metadata
- Sampled column-level data for pattern matching
- Existing classified-table dependency graph
- Data catalog platform (Collibra, Alation, or native)
Outputs
- Auto-tagged PII and sensitivity classifications
- Business domain tags per table
- Confidence-scored classification queue for review
- Ongoing coverage report of classified vs. unclassified tables
Works with
Prefer a fully custom build instead of an off-the-shelf integration? We scope both options during your free consultation — most jobs like this one work fine on standard connectors, but higher-volume or non-standard systems sometimes need bespoke API work, reflected in the complex tier.
Where this goes wrong if you get it wrong
- Pattern-matching for PII catches common, well-structured formats like standard email addresses reliably, but misses PII embedded in free-text fields — a customer name mentioned inside a support ticket comment column — where the sensitive data isn't isolated in its own clearly-typed column, so classification coverage needs to be honest about this gap rather than implying complete PII discovery.
- Sampling column data for classification, rather than scanning every row, is necessary for performance at warehouse scale but means a rare sensitive value that only appears in a small fraction of rows can be missed by the sample — sensitivity classification should default to conservative (flag for review) rather than confidently clearing a column based on a sample that happened not to contain the risky value.
- Business domain tagging based on relationships to already-classified tables can propagate an earlier misclassification forward — if a foundational table was tagged wrong early on, everything downstream that inherits its classification through the dependency graph inherits the error too, so periodic spot-checks against the foundational tags matter more than checking newer, derived tags.
- A confidence score that's miscalibrated — consistently overconfident on a particular data pattern that happens to produce false positives — erodes trust in the whole classification system if reviewers keep finding the same type of error in supposedly high-confidence tags; the confidence scoring itself needs periodic validation against actual reviewer corrections, not just trusted as accurate once configured.
Frequently asked questions
Does this guarantee every sensitive column gets found?
No, pattern-matching against sampled data catches common, well-structured PII reliably but can miss sensitive data embedded in free-text fields or rare values outside the sample — high-risk domains should still include periodic manual spot-checks alongside the automated classification.
How confident should I be in the automatic tags?
Every tag carries a confidence score, and low-confidence classifications are routed for human review rather than treated as certain, so the catalog distinguishes between high-confidence automated tags and ones that still need a person to confirm.
Does the catalog stay current as we add new tables?
Yes, new tables are classified as part of the same pipeline that creates them, which is the main advantage over a manual tagging effort that inevitably falls behind as the warehouse grows.
What happens if an early classification error affects everything downstream?
Since domain tagging can propagate through dependency relationships, periodic spot-checks on foundational, frequently-referenced tables are worth prioritizing, since an error there is more consequential than an error on a rarely-referenced derived table.