A data platform where every figure traces back to its source record.
Most reporting problems are data model problems. When the CRM, billing and finance disagree, another dashboard only adds a fourth number. We design the data model first, then build the pipelines, the identity layer and the reporting on it.
What is included.
- A data model designed before any dashboard.
- Pipelines that load each source system on a schedule or as events arrive.
- An identity layer that matches records across systems with no shared customer key.
- Reconciliation checks that show where systems disagree and by how much.
- Access control and audit logs on the data itself.
- Reporting where every number links to the records behind it.
What clients use it for.
Customer identity resolution
One record per customer across systems with no shared key, matched on hard identifiers so every match can be explained.
In production: National wireless carrier →Geospatial risk scoring
Ranks areas for outreach from census and program data, and checks every ranking against the margin of error of the source.
In production: County cancer-screening program →
How it works.
Identity resolution without a shared key.
When systems share no customer key, we build the identity layer. It matches on hard identifiers captured when a record is created, not on guesses from names and addresses. Every match can be explained, which matters more than a match rate that looks good.
Reconciliation you can audit.
Before we change anything, we write down where the numbers disagree and by how much. Once the platform is in place, the same checks keep running, so a new disagreement shows up when it happens instead of at month end.
Output checked against the limits of the data.
Most tests check that an answer looks right. We also check output against what the source data can support. On a public-health project, that caught a defect every test had missed: dropped margin-of-error columns were pushing tiny areas to the top of a ranking.
From first contact to billing.
Following a customer from first contact to a paid invoice is an identity and reconciliation problem, not a reporting one. We match each customer to the record where they first appeared, then to billing, so revenue can be traced to where it started.
Keep reading.
Questions about the data platform.
Not covered here? Send us your question and an engineer will answer it.
Ask a question- How do you match customer records across systems with no shared ID?
- We match on hard identifiers captured when a record is created, rather than on names and addresses. Every match can be traced to the identifier that made it.
- Do we have to replace the systems we already use?
- No. We connect the systems you already run and build the data model and reporting alongside them.
- Which tools do you use?
- Most often PostgreSQL and BigQuery for storage, Apache Spark for large jobs, and Python for pipelines.
- How long does a data platform take?
- Discovery and audit take one week. After the audit, the platform runs on your real data within 90 days at most, and a proof of concept can be ready in as little as 24 hours.