Data warehouse · Snowflake data quality for AIThe warehouse looks pristine. Is it still true?
Snowflake is usually where models train: every source consolidated into tables a pipeline reads. That is exactly why its cleanliness is deceptive. Polished marts can sit on untested transformations, re-run duplicates and freshness gaps. We compare extracts from Snowflake with exports from the source systems themselves, to show whether the copy still matches the original.
Starts with a 30-minute call. Commits you to nothing. Then exports only: no login, no connected app, nothing installed.
Source of truth
What Snowflake gets right.
Every system is built to be right about something. Knowing what Snowflake owns, and what it doesn’t, is where a cross-system check starts, and where the Truth Score™ (a score out of 100 for how far your data can be trusted) begins to take shape.
- 01
Joined-up history
Data from many systems, in one place, over time.
- 02
Agreed metrics
Where a semantic layer or marts define them.
- 03
Training data
The tables feature pipelines and models read.
The original record. Every row is a copy of a source system, transformed on the way in; when the two disagree, the source usually wins.
Inside the system
Common Snowflake data problems for AI.
What goes wrong inside Snowflake itself, with the objects and fields where it shows. Each one is checkable from an export.
- 01
Schema drift with no contract
Sources add, rename or retype columns, and transformations silently null or mistype fields the model depends on.
INFORMATION_SCHEMA.COLUMNSsource schemas - 02
Duplicates from re-runs and late data
Non-idempotent loads and late-arriving rows let a model learn the same records twice.
primary keys_LOADED_ATMERGE logic - 03
Untested transformations
dbt models ship without unique, not_null or relationships tests, so logic errors reach the marts.
dbt tests: unique, not_null, relationships - 04
Mixed grain
One table per order, another per line; joins fan out and totals inflate.
table grainjoin keys - 05
Freshness and abandoned tables
Tables loading late or not at all, and schemas no pipeline still maintains.
LAST_ALTEREDload schedulesunused schemas
Between systems
Snowflake looks clean on its own. The gaps are next door.
The problems that sink AI sit between Snowflake and the systems beside it: the same customer, person or transaction recorded differently in each. These are the gaps to test for each pairing.
Data warehouse
Snowflake
Source of truth forJoined-up historyAgreed metricsTraining data
Snowflake ↔ CRM sources
- Accounts in the source missing from the warehouse
- Stage values remapped on the way in
Snowflake ↔ Finance sources
- Revenue totals that differ from the ledger
- Credit memos dropped in staging
What to export
What we need from you, in plain English.
Your data team runs the queries we specify and shares results as CSV or Parquet. Because the warehouse is a copy, we also take exports from the source systems and compare the two. No warehouse credentials, no role, no share.
- Exports only, no access to your systems
- NDA and data processing agreement first
- The full list of sub-processors comes with our data processing agreement, before any data moves
- Raw exports deleted after the engagement; we keep quality metrics, not your records
AI that depends on it
AI use cases that lean on Snowflake.
If one of these is in a proposal or your plan, Snowflake is one of the systems we compare. Each page sets out what the use case needs and where it breaks.
- Operations Natural-language analytics Asking questions in plain English runs on the warehouse and its metric definitions. Assessed with your team, not from exports alone.
- Revenue Revenue forecasting Forecast models usually train on warehouse tables; duplicates and grain errors become forecast errors.
- Revenue Customer churn prediction Churn features join usage, billing and support, which may only be possible in the warehouse.
- Operations Demand forecasting Long order histories live here, once deduplicated and checked against the ERP.
- Finance Fraud detection Transaction history at scale sits in the warehouse; lineage decides whether a flag can be explained.
How a Truth Score Read tests Snowflake
From your exports to a signed verdict.
Within 5 working days of complete exports. A fixed fee, agreed before we start, that never moves with the verdict.
- 01
Export Snowflake, and one system beside it
The Snowflake exports listed above, plus at least one neighbour, such as CRM sources. Under NDA, using the field list we send.
- 02
Match across systems
Warehouse extracts matched against the source systems’ own exports, key by key, so you can see where the copy and the original have drifted apart.
- 03
Check by hand, then score
Every red flag is checked by hand against sample records before sign-off. Then the Truth Score: each pillar shows what was measured and what wasn’t, and pillars with too little signal are marked, never guessed.
- 04
Get the verdict in writing
A signed memo: the red flags between Snowflake and the other systems, a Leverability read on the AI you’re considering (which AI ideas you can use now, after fixes, or not at all), a verdict (sign, fix first or walk away) and a short fix list your own team can work from.
What you get back
See what comes back: a real sample memo.
One fictional B2B software company, Example Co: 72,837 records in 7 exports from its CRM, finance and HR systems, run through the same engine and the same checks as a paid Truth Score Read. Each system passes its own checks. Matched across systems, only 13% of deals tie to an invoice.
Questions
Snowflake and AI, answered plainly.
Anything else, ask on the 30-minute call. It commits you to nothing.
Book the callCan you assess Snowflake without credentials?
Yes. Your team runs the queries we specify and shares the results, under NDA. We never need a role, a user or a data share.
Our warehouse is the single source of truth. Why look at the source systems?
Because the warehouse is a copy. Filters, joins and failed loads change what arrives. Matching warehouse tables against the source exports is how you know the copy is still true.
We have dbt tests. Is that enough?
Tests catch what someone thought to test. We look at coverage, and at whether the output still agrees with the systems it came from.
Do you build data pipelines?
No. We sell no implementation and never fix what we find. Your data team, or any firm you choose, takes the fix list.
Find out what your Snowflake data can carry. Before the AI does.
Your Snowflake export and one other system in. The gaps between them in writing, within 5 working days of complete exports. Starts with a 30-minute call. Commits you to nothing.
Starts with a 30-minute call. Commits you to nothing.