Source-system profiling
Identify what each system represents, which fields matter, and whether it should be treated as truth, context, or sales reporting.
Business Systems Truth Audit
Find out why your business numbers do not agree.
I diagnose the source-system, identity, join, and reconciliation problems causing CRM, finance, product, and customer-success data to disagree — before you waste more money on dashboards.
The real issue
The dashboard is usually not the real problem.
CRM says one thing. Finance says another. Customer-success tools show a different account state. Product databases contain real usage behavior, but the schemas are messy and inconsistent.
Then spreadsheets become the unofficial reconciliation layer. Executives lose trust because no one can explain the mismatch clearly enough to make a decision.
Most companies do not need another prettier chart first. They need to know whether the systems feeding the chart are modeled correctly.
What I diagnose
Find the broken assumptions before they become executive reporting.
Unsafe joins
Surface joins that silently drop records, duplicate revenue, or merge different customers into the same reporting entity.
Identity/entity fragmentation
Diagnose emails, account names, station names, customer IDs, and company records that represent the same entity differently across tools.
Revenue/metric reconciliation
Compare finance truth against CRM, product, and customer-success context so teams can explain the delta instead of arguing over exports.
Pipeline and connector failure points
Review where ingestion, connector schemas, sync timing, and transformation assumptions can create reporting drift.
Executive fix roadmap
Translate technical findings into a practical sequence of fixes leadership can approve and teams can execute.
Concrete examples
Small formatting differences can become large reporting failures.
Station/account names
WMMS-FMWMMS-fmWMMSFMwmms fm
These may be the same account operationally, but raw joins may treat them as four different entities.
Email identity
EricBaranowski@gmail.comericbaranowski@gmail.comERICBARANOWSKI@GMAIL.COM
Without normalization, casing and whitespace can fragment the same customer across systems.
System disagreement
HubSpot, NetSuite, Totango, product databases, and spreadsheets may all describe the same customer journey from different angles.
Finance may be the revenue source of truth while CRM remains the reporting and pipeline context layer.
The offer
Business Systems Truth Audit
A focused diagnostic engagement that explains why your business systems disagree and what needs to be fixed before you invest more money into dashboards, BI tooling, or automation.
Source-system profile
Field and join-risk map
Identity/entity matching diagnostics
Reconciliation failure report
Source-of-truth recommendation
Executive fix roadmap
Optional prototype truth dashboard/API
Public education
Good analytics is not magic. It is disciplined data plumbing.
The internet is not reading your mind. It is reading your behavior through data pipelines.
The same pipelines that make ads feel predictive are the pipelines businesses need to understand their own customers.
Revenue Truth Engine makes those pipelines visible, testable, and explainable.
Proof of concept
Live proof of concept
The current demo uses simulated systems, but the architecture mirrors real messy business systems: payments, product events, CRM reporting, identity resolution, reconciliation, and executive-facing diagnostics.
dbt creates the trusted transformation layer. FastAPI exposes governed data contracts. The dashboard and Streamlit app consume trusted outputs.
The point is not fake revenue data. The point is the reconciliation architecture.
Open the DemoMessy source systems
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Trusted dbt models
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FastAPI contracts
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Executive diagnostics
Contact
Want to know why your systems disagree?
Start with a focused systems truth audit before committing to another BI rebuild, dashboard project, or connector migration.