Own Your Data Outside the ERP
A canonical, history-preserving data warehouse you own – inside your boundary
– plus generated, tested dbt models for your warehouse, with
reconciliation and month-end evidence packets. Written June 2026.
The offer in one sentence – Your ERP holds your data; it doesn’t let you
own it. We stand up a canonical warehouse in your environment that keeps
every version of every record (SCD Type 2, with as-of time travel and
per-source provenance), lay generated, tested dbt models over it,
reconcile it against your other systems at the field level,
and close your month behind a computed checklist with an evidence packet
– read-only against your ERP, with the write-back roadmap stated honestly
below.
Evaluating warehouse or BI vendors? Start with the seven questions to ask any
of them – including us: What to Demand From an ERP Data
Warehouse.
Why own data outside the ERP
- The ERP answers “what is true now.” Half your real questions are time
questions – what did this PO look like at cutover, when did these systems
start disagreeing, prove the migration moved everything. Only retained
version history answers those. - History you own is ERP-change insurance. The canonical store is
schema-independent of any vendor; an eventual ERP change becomes a connector
swap plus a verified migration – not a history-loss event. - Cross-system truth has no owner. When the ERP and a second system (shop
floor, CRM, e-commerce, field service) disagree, neither vendor sells the
reconciled middle. Today that layer is an analyst and a spreadsheet; this
makes it an asset. (The full argument: When Your ERP and Your Second
System Don’t Talk.)
What you get
The canonical store (yours, in your boundary)
Read-only, idempotent, re-runnable ingestion lands every record in one
canonical shape with provenance – which source, which run, as of when. A
changed record closes the prior version and appends the new one; an unchanged
record is a no-op. History accumulates instead of being overwritten, and the
store can answer any “as of” question from retained versions.
Generated dbt models, with tests included
A tested dbt package that models your warehouse – staging views, marts, and
generated structural tests – built for the exact platform version you run,
and fully reproducible. The structural tests fail loud when a key goes missing
or a uniqueness assumption breaks, so a modeling regression surfaces before it
reaches a report.
Honest validation status, as of June 2026: the package builds green on a
real dbt adapter – dbt build, models and generated tests – over typed
stub sources derived from its own metadata. What that proves: the emitted SQL
parses, binds, materializes, and runs, and the package is internally
consistent. What it deliberately does not prove: column semantics against
your actual data, and behavior on your own adapter (SQL Server / Postgres /
Snowflake) – the dbt build on your warehouse is a required engagement step,
and domain assertions (balances, statuses, reconciliations) are built per
engagement. The generated tests are structural; we say so rather than letting
you assume more.
Reconciliation with evidence
Field-level divergence reports between your systems – not “the totals are
off,” but this record, this field, this value here, that value there – plus
exception reports like PO commitments vs. paid AP invoices with a variance
tolerance, so what surfaces is worth a human’s time.
Month-end with an evidence packet, not a blind close
- Period close as a computed checklist: pending events, drift, orphans,
duplicates, ledger integrity, and a per-period trial balance – the close
only proceeds when the checklist is green or every warning is explicitly
acknowledged, and reopening is an explicit, recorded act. - AP and AR aging sub-ledgers (current / 1–30 / 31–60 / 61–90 / 90+),
with credits never netted away in presentation and a GL tie-out that
fails loudly instead of shipping a number that doesn’t tie. - A double-entry ledger guard that refuses any entry violating its posting
invariants and can re-verify its own integrity on demand. - For migrations and cutovers, a completeness-and-correctness packet –
evidence that everything that should have moved did move, and matches.
Optional: ask it in plain language, with citations
A gated natural-language layer can translate a question into a deterministic,
re-runnable query spec – the model never writes SQL, every answer carries the
spec it ran, and the system abstains rather than guesses. Status, honestly:
validated in our lab against a sandbox on synthetic data, as of June 2026.
It is an option to evaluate inside your engagement, not a headline claim.
What this is not
Not a BI dashboard (a view over what one system says right now), not
point-to-point sync (moving data is not establishing truth), not vendor
middleware (the entire point is that you own the layer and the history in
it) – and not an ERP replacement. The ERP keeps doing finance and billing;
this layer makes its data yours, provable, and reconciled.
The write-back roadmap, stated plainly
The layer described above performs no writes to your ERP – it reads,
reconciles, reports, and proves. That is the deliverable, and the read side
is built and test-covered.
Governed write-back is a roadmap item. The design is specific –
approval-gated posting, dead-letter queue with replay, idempotent
watermark-incremental sync, a ledger guard, full per-write audit – and as of
June 2026 it has been validated against simulators only, end-to-end on a
purchase-order slice. It has never run against a live ERP, and we will not
describe it as a live capability until it has – validated first on a sandbox,
never on anyone’s production. Warehouse engagements never write to your
production systems. If write-back matters to your roadmap, it is a design
conversation with that validation gate named in the open.
What you own when the engagement ends
The store, the data, and the entire version history – provisioned in your
environment, they stay with you. The tooling that builds and reconciles the
layer is ours; your reconciled record is yours. You are never renting access
to your own data.
Next step
Book a consultation
or email [email protected]
– a warehouse engagement starts with a bounded inventory of your systems and
the questions you can’t currently answer with evidence.
Contact
A bounded data-layer assessment – you end up owning the store, whatever you decide about the ERP.
Or email [email protected].