Audit-First Analytics Consulting
We find what's broken, fix it, and hand you the infrastructure to keep it right.
How We Work
A repeatable, six-stage pipeline. Every engagement follows the same sequence so nothing gets missed and every finding is traceable back to source data.
- 01
Ingest
Ingest
Connect to your warehouse and load datasets. Read-only access, no production changes.
- 02
Profile
Profile
Scan schemas, column types, null rates, cardinality, and distributions across every table.
- 03
Check
Check
Run 40+ automated quality checks: uniqueness, referential integrity, accepted values, freshness, near-duplicate detection.
- 04
Validate
Validate
Recompute KPIs from source data and compare against production dashboards. Flag every metric that diverges.
- 05
Score
Score
Assign a 0-100 reliability score based on check results, weighted by severity and business impact.
- 06
Generate
Generate
Produce the audit report, dbt project, metric contracts, and interactive dashboards from validated data.
Engagement Tiers
Start with a one-week diagnostic. The sprint fee is 100% credited toward a full engagement.
Diagnostic Sprint
1 week
A fast, low-friction entry point. We connect to your warehouse, run our automated pipeline, and deliver a severity-ranked scorecard of the top issues in your data systems.
- Scored audit report (0-100)
- Top findings ranked by severity
- Remediation priorities
- Executive summary + technical detail
Full Engagement
4-6 weeks
End-to-end: from warehouse connection to validated dashboards. We audit your data, formalize metric definitions, build the fix, and hand it off to your team.
- Everything in Diagnostic Sprint
- ASSUMPTIONS.md (data contracts)
- METRICS.md (canonical definitions)
- dbt project with 40+ tests
- Interactive Streamlit dashboards
- Drift detection and monitoring
Continuous Monitoring
Ongoing
After the engagement ends, automated monitoring keeps your metrics honest. Drift detection, freshness alerts, and quarterly reviews ensure regressions are caught early.
- Automated drift detection
- Freshness and volume alerting
- Quarterly audit reviews
- Priority support for new metrics
What You Get
Concrete artifacts, not a slide deck. Every engagement produces working infrastructure your team can use from day one.
Scored Audit Report (0-100)
A quantified evaluation of your pipeline across completeness, freshness, schema conformance, and metric consistency. Findings ranked by severity and business impact.
Dataset Profile
Schema scan covering column types, null rates, cardinality, and distributions across every table in your warehouse.
Quality Checks Report
Results from 40+ automated checks: uniqueness, referential integrity, accepted values, freshness, and near-duplicate detection.
ASSUMPTIONS.md
A plain-language document capturing every assumption the pipeline makes. Client sign-off before anything is built.
METRICS.md
One canonical definition per KPI with formulas, dimensions, and validation rules. Aligned across product, finance, and growth.
dbt Project (40+ tests)
Auto-generated staging-to-mart pipeline with deduplication, canonicalization, and mart models. 40+ schema tests covering nulls, uniqueness, range validation, and freshness.
Streamlit Dashboard
Interactive dashboards with sidebar filters, metric charts, and data quality views. Built on validated staging models.
Canonicalization Mapping
Maps raw column names and inconsistent values to clean, canonical forms. The translation layer between messy source data and governed metrics.
Frequently Asked Questions
Most teams we work with have strong data engineers. The issue is not skill but time and focus. Internal teams are busy building pipelines and dashboards. They rarely get dedicated time to audit telemetry and metric correctness end-to-end. We compress months of ad-hoc debugging into a structured 1-2 week engagement. Your team keeps building while we find what is broken.
Read-only access to your data warehouse. No write access, no production system access, no PII required. We run queries inside your environment and only export aggregated findings. We operate under NDA with time-limited access scoped to the engagement period. We need about 1-2 hours per week from your data team for context.
Those tools are observability platforms that detect anomalies in data freshness, volume, and schema. That is monitoring. We do something different: we audit whether the business metrics are correct. That means recomputing KPIs from source events, validating telemetry against logging specs at the field level, and tracing root causes through pipeline logic. Monitoring tells you something changed. We verify whether the number is correct.
Good, that means you have infrastructure to build on. dbt tests catch surface-level issues: nulls, uniqueness, accepted values, freshness. They do not catch metric definition drift, telemetry gaps, or pipeline logic errors that silently produce wrong numbers. dbt tests verify schema. We verify that the business numbers are actually right.
Most companies think that until a board question forces someone to reconcile numbers across dashboards. The issues we find do not look like obvious failures. The pipeline runs, the dashboard updates, nothing alerts. But the number is wrong because a join duplicates records, a filter excludes a subset of users, or two services log the same action differently. Typical engagements uncover 5-10 issues, with 1-3 materially affecting decision-making metrics.
A Diagnostic Sprint takes 1 week and delivers a severity-ranked scorecard of the top issues in your data systems. A Full Engagement takes 4-6 weeks and includes the audit, metric contracts, a dbt project with 40+ tests, interactive dashboards, and drift monitoring. The sprint fee is 100% credited toward a full engagement if you continue.
Warehouses: Snowflake, BigQuery, Redshift |Transform: dbt preferred, any stack |Dashboards: Looker, Tableau, Mode, Metabase |Access: Read-only
Ready to see what's hiding in your data?
Book a one-week diagnostic to score your data pipeline and surface the issues that matter most.
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