Data QualityAI ValidationPipelines

Implementing Data Quality Checks to Catch AI Hallucinations in Analytics Outputs

aanalyses

2026-02-05

11 min read

Prevent AI hallucinations in dashboards with a practical checklist and SQL tests to validate AI-generated analytics before publication.

Stop the Cleanup: A Practical Guide to Catch AI Hallucinations in Analytics

Hook: Your analytics dashboards are only as credible as the data and models that feed them. In 2026, marketing teams increasingly depend on AI to summarize trends, create insights, and automate decisions — and with that power comes a new failure mode: AI hallucinations that pollute dashboards, trigger bad campaigns, or mislead stakeholders. This guide gives a concrete checklist plus hands-on SQL and data-test examples you can add to your pipelines today to block hallucinated insights before they reach decision systems.

Why this matters now (2026 context)

By late 2025 and into 2026, RAG (retrieval-augmented generation) and vectorized retrieval are mainstream in analytics tooling. Teams use LLMs to turn event-level data into human-friendly insights and automated recommendations. Observability vendors and MLOps frameworks added AI-aware checks in 2025, but many organizations still ship LLM outputs to dashboards without robust validation. The result: plausible but incorrect statements — from misattributed conversions to invented segmentation insights — appear in reports. The cost: misallocated ad spend, wrong product prioritization, and lost trust.

Where AI hallucinations show up in analytics

Summarized natural-language insights (e.g., "Conversions from Channel X rose 40% this month")
Derived KPIs created by LLMs (e.g., synthesized metrics or causal claims)
Classifications or labels generated by AI (e.g., product categories, intent segments)
Automated alerts and recommendations (e.g., recommend pausing a campaign)

High-level strategy: Block, validate, monitor

Preventing hallucinations is a pipeline problem and an organizational problem. Practically, do three things:

Block — add pre-publication gates: deterministic checks that must pass before LLM outputs reach dashboards or actions.
Validate — run statistical, referential and semantic tests that compare AI outputs to source truth.
Monitor — continuously observe drift and establish alerts, human-review loops and governance.

Concrete pre-publish checklist (copy into a CI or orchestration job)

Use this checklist as a mandatory gate in your Airflow/Prefect/dbt Cloud job or CI pipeline that publishes insights.

Source Referential Integrity: Every record referenced by an AI insight (IDs, event timestamps, product SKUs) must exist in source tables. For data storage and serverless patterns see Serverless Mongo Patterns.
Numeric Reconciliation: Numeric claims (counts, rates, revenue) must reconcile with aggregates from raw event tables within a tolerance.
Null / Missing Rate: Feature and metric null rates must be below SLA thresholds.
Cardinality and Uniqueness: Check primary keys, dedupe counts and cardinality shifts.
Confidence & Provenance: Require provenance metadata (retrieval docs, source IDs) and minimum confidence scores for generated labels — privacy-aware provenance approaches are discussed in Privacy-First Browsing.
Semantic Consistency: Compare LLM-generated labels with deterministic heuristics or lookup tables.
Distribution / Drift Test: Ensure feature distributions do not deviate beyond defined bounds vs. baseline.
Spike / Z-score Test: Ensure sudden metric spikes get flagged and require human approval.
Policy & Governance Check: Ensure no outputs violate policy rules (e.g., personally identifiable information, unverified claims).

SQL and data tests you can run today

Below are practical SQL queries and test patterns you can drop into dbt, Great Expectations, or a CI job. They assume conventional analytics tables like events.events, agg.daily_metrics, ai_insights and products.

1) Referential integrity: Ensure referenced IDs exist

Use this to verify any entity IDs mentioned in AI outputs match source tables.

-- Find AI insights that reference non-existent events
SELECT ai.id AS ai_id, ai.event_id
FROM analytics.ai_insights ai
LEFT JOIN events.events ev ON ai.event_id = ev.id
WHERE ai.event_id IS NOT NULL
  AND ev.id IS NULL;

2) Numeric reconciliation: cross-check claims vs raw aggregates

If the LLM reports "Channel X had 2,400 conversions this week," compare to event-level counts.

-- Weekly conversions by channel from raw events
WITH raw_week AS (
  SELECT channel, COUNT(*) AS conversions
  FROM events.events
  WHERE event_name = 'purchase'
    AND event_timestamp >= date_trunc('week', current_date - interval '1 week')
    AND event_timestamp < date_trunc('week', current_date)
  GROUP BY channel
)
SELECT ai.id, ai.claimed_conversions, r.conversions,
       ROUND((ai.claimed_conversions::numeric - r.conversions)/NULLIF(r.conversions,0),4) AS pct_diff
FROM analytics.ai_insights ai
JOIN raw_week r ON ai.channel = r.channel
WHERE ai.generated_at >= date_trunc('week', current_date - interval '1 week')
  AND ABS((ai.claimed_conversions::numeric - r.conversions)/NULLIF(r.conversions,0)) > 0.05; -- flag >5% mismatch

3) Null-rate check (feature & metric completeness)

-- Null rate for key metrics in daily_metrics
SELECT
  SUM(CASE WHEN conversions IS NULL THEN 1 ELSE 0 END)::float / COUNT(*) AS conversions_null_rate,
  SUM(CASE WHEN revenue IS NULL THEN 1 ELSE 0 END)::float / COUNT(*) AS revenue_null_rate
FROM agg.daily_metrics
WHERE date >= current_date - interval '30 day';

4) Duplicate detection

-- Duplicate insight id check
SELECT id, COUNT(*)
FROM analytics.ai_insights
GROUP BY id
HAVING COUNT(*) > 1;

5) Spike detection using z-score

Detect sudden unexpected jumps that a hallucinated statement might create.

-- Compute z-score for daily conversions vs 28-day mean
WITH stats AS (
  SELECT date,
         conversions,
         AVG(conversions) OVER (ORDER BY date ROWS BETWEEN 28 PRECEDING AND 1 PRECEDING) AS mean_28,
         STDDEV(conversions) OVER (ORDER BY date ROWS BETWEEN 28 PRECEDING AND 1 PRECEDING) AS sd_28
  FROM agg.daily_metrics
)
SELECT date, conversions, mean_28, sd_28,
       (conversions - mean_28) / NULLIF(sd_28,0) AS z_score
FROM stats
WHERE date = current_date - interval '1 day'
  AND ABS((conversions - mean_28) / NULLIF(sd_28,0)) > 4; -- flag |z| > 4

6) Cardinality drift (detect sudden new values)

-- Count distinct product_ids in last 7 days vs prior 30 days
SELECT
  SUM(CASE WHEN date >= current_date - interval '7 day' THEN 1 ELSE 0 END) AS last_7d_count,
  SUM(CASE WHEN date < current_date - interval '7 day' AND date >= current_date - interval '37 day' THEN 1 ELSE 0 END) AS prior_30d_count
FROM (
  SELECT date, product_id, ROW_NUMBER() OVER (PARTITION BY product_id ORDER BY date DESC) rn
  FROM events.events
  WHERE event_name = 'view_item'
) t;

7) Semantic validation: Compare LLM labels to deterministic rules

For example, if AI assigns product_category, cross-check with master lookup.

SELECT ai.id, ai.product_sku, ai.product_category AS ai_cat, p.master_category AS master_cat
FROM analytics.ai_insights ai
LEFT JOIN products.master p ON ai.product_sku = p.sku
WHERE ai.product_category <> p.master_category
  AND ai.generated_at >= current_date - interval '7 day';

8) Provenance & retrieval checks for RAG setups

Require at least one retrieval source and a minimum similarity score before accepting an LLM claim.

-- Example table: analytics.rag_retrievals (ai_id, doc_id, similarity)
SELECT ai_id, COUNT(*) AS retrieval_count, MIN(similarity) AS min_similarity
FROM analytics.rag_retrievals
GROUP BY ai_id
HAVING COUNT(*) = 0 OR MIN(similarity) < 0.65; -- flag missing/low-quality retrievals

9) Embedding similarity check (Postgres + pgvector)

If you store embeddings, ensure the evidence used is close enough to the query embedding. For vector and edge ingestion patterns see Serverless Data Mesh for Edge Microhubs.

-- Using pgvector, <=> is cosine distance (0 = identical)
SELECT ai_id, doc_id, 1 - (embedding <=> query_embedding) AS cosine_similarity
FROM analytics.retrieval_embeddings
WHERE ai_id = 'ai-123'
ORDER BY cosine_similarity DESC
LIMIT 5;
-- Ensure top similarity > 0.7 for trust

10) Unit-test-style CI assertion (pytest + SQL)

Wrap checks as failing tests in CI so deployment is blocked on failures.

# Example pytest pseudo-code
def test_no_missing_retrievals(db_conn):
    rows = db_conn.execute("SELECT ai_id FROM analytics.rag_retrievals GROUP BY ai_id HAVING COUNT(*) = 0;")
    assert rows.rowcount == 0, "Some AI outputs have no retrieval provenance"

def test_numeric_reconciliation(db_conn):
    rows = db_conn.execute("SELECT COUNT(*) FROM ( -- mismatch query ) t WHERE pct_diff > 0.05")
    assert rows.fetchone()[0] == 0, "Numeric mismatches above tolerance"

Integrating tests into the pipeline

Make these checks part of your automated CI/CD for data:

Implement the SQL tests as dbt & Great Expectations tests or Great Expectations suites. dbt is ideal for schema/relationships; Great Expectations is strong for statistical and semantic assertions.
Add a pre-publish job that runs tests whenever an LLM output batch is produced. If any test fails, mark the insight as "quarantine" and prevent downstream publication.
Log failures to an incident stream (Slack, Opsgenie) with short results and links to failing queries and sample records.
Store provenances (document IDs, retrieval similarity, model version, prompt used) in a persistent audit table — tie this into your decision-plane and auditability plan: Edge Auditability & Decision Planes.

Example dbt tests and Great Expectations snippets

Sample dbt schema.yml snippet for classic constraints:

version: 2

models:
  - name: ai_insights
    columns:
      - name: id
        tests:
          - unique
          - not_null
      - name: event_id
        tests:
          - relationships:
              to: ref('events')
              field: id

Great Expectations (Python) example asserting a mean within bounds:

from great_expectations.dataset import SqlAlchemyDataset

ds = SqlAlchemyDataset(sql_engine, 'agg.daily_metrics')
expectation = ds.expect_column_mean_to_be_between('conversions', min_value=100, max_value=20000)
if not expectation['success']:
    raise Exception('Conversions mean outside expected range')

Operational governance: rules beyond tests

Model & Data Versioning: Log model version, prompt template, and dataset snapshot used to produce any insight — part of a wider serverless data mesh and versioning strategy.
Human-in-the-loop for high-impact alerts: For automated actions affecting >$X or >Y users, require a human sign-off step — this is a trusted governance pattern in AI strategy.
Error Budgets and SLA: Define acceptable error rates for AI outputs and set retraining/rollback triggers when budgets exceed limits.
Explainability & Audit Trails: Store retrieval docs and similarity scores. If an AI claim is challenged, you must show the sources it used — integrate this into your audit plan from Edge Auditability & Decision Planes.
Privacy & Policy Filters: Reject outputs that include PII or unapproved speculations — privacy-first approaches are covered in Privacy-First Browsing.

"Trust but verify: if an LLM creates an insight, your pipeline must corroborate it with raw data before the business acts on it."

Case study: how a simple test prevented a bad campaign

Situation: A marketing analytics LLM summary flagged "Product SKU 987 saw a 350% week-over-week increase in conversions" and suggested tripling bids for the product. That claim reached a junior manager who scheduled an immediate budget increase.

What we implemented: A numeric reconciliation test compared the AI-claimed conversions to raw events and required the difference be under 10%. The test failed — the LLM had aggregated on a derived table that mistakenly included test purchases from an internal QA environment. The CI job quarantined the insight and notified data ops. Investigation revealed the model's retrieval included QA docs; adding a provenance filter and a retrieval-similarity threshold fixed the issue. Outcome: avoided wasted ad spend and identified a gap in data filtering.

Monitoring and alerting patterns

Make these part of your observability stack:

Alert when tests begin failing more than N times/day.
Monitor the fraction of AI insights quarantined; rising trend = model/data issues.
Track distribution metrics for key features and embeddings similarity histograms.
Expose a dashboard showing provenance coverage (percent of insights with >=1 retrieval doc) and average similarity.

Advanced strategies (2026-forward)

As vector search and RAG evolve, consider these advanced controls:

Answerability tests: Attempt to answer a numeric claim solely via SQL across raw events; if the SQL answer differs, require human review.
Counterfactual sanity checks: Re-run the prompt with perturbed retrievals; if outputs vary widely, lower confidence or quarantine.
Automated fact-checker using rules + lightweight models: Use a deterministic fact-check module to validate numeric or categorical claims before publication.
Model ensembles: Compare outputs from two LLM configurations; require agreement for high-impact claims.

Practical rollout plan — 30 / 60 / 90 days

Days 0–30: Inventory where LLM outputs enter dashboards; add provenance storage and implement the top 5 SQL checks (referential integrity, numeric reconciliation, null rate, duplicates, retrieval count).
Days 30–60: Add dbt & Great Expectations tests to CI; quarantine failing insights; build a quarantine dashboard and notification channel.
Days 60–90: Implement human-in-the-loop for high-impact outputs, add distribution drift tests and embedding similarity thresholds, and create retraining/rollback policies tied to error budgets.

Final checklist (copy-paste)

Store model version, prompt, retrieval docs, similarity scores for each insight
Run referential integrity test on IDs referenced by the AI
Numeric reconciliation: assert numeric claims within tolerance of raw aggregates
Reject outputs missing provenance (retrieval_count >= 1 and min_similarity >= 0.65)
Enforce confidence_score / label threshold for AI labels
Run z-score spike detection for key metrics
Detect and quarantine high cardinality drift and sudden new values
Log and alert failures; route to human review for high-impact items — tie your incident workflow to a template like Incident Response Template for Document Compromise and Cloud Outages
Record every test result and build dashboards for quarantine rates & test failures

Closing: trust restored by tests and governance

AI can speed up insight generation and reporting — but only if your pipelines treat AI outputs like any other data artifact: testable, versioned, and auditable. Add the checks above as pre-publish gates, fail fast in CI, and keep humans in the loop for the highest risk decisions. In 2026, organizations that pair LLM power with disciplined data quality controls will win better outcomes and avoid the trust erosion that unchecked hallucinations bring.

Call to action: Start with the pre-publish checklist above. Need a ready-to-run pack of dbt tests and SQL templates tuned for your stack (Snowflake, BigQuery, Postgres)? Contact our team or download the free 30/60/90 implementation kit and sample dbt/Great Expectations manifests to deploy in your CI in under a week.

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