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ADMS Upgrade — Data SME

Validated future-state schema against five downstream consumers without engaging external contractors.

Role Data SME
Period 2023 – 2024
Headline $49,031 documented cost avoidance · zero-disruption cutover
Stack Oracle OMS / CAD · SAP HANA → Snowflake (meter data; migration in flight) · iDashboard / PowerBI (legacy BI) · Nighthawk platform · Schema validation, data-flow verification

Problem

An ADMS (Advanced Distribution Management System) upgrade in a regulated utility is a high-stakes data event. The new schema doesn’t just affect the ADMS itself — it propagates downstream to every system that reads from it. At OG&E, that meant five separate consumers (Oracle, SAP HANA, iDashboard, PowerBI, Nighthawk) all needed their data contracts re-validated against the future-state ADMS model before cutover.

The default option was to engage external contractors for the schema validation work. That was the path of least resistance — and the most expensive.

What I Did

Stepped in as the internal data SME and ran the validation in-house:

  • Mapped future-state ADMS tables against every downstream consumer’s read patterns.
  • Identified breaking changes in schema, data types, and field semantics — specifically the ones that would have silently corrupted reports if not addressed.
  • Coordinated remediation across the five downstream system owners.
  • Verified end-to-end data flow in the staging environment before cutover.

The work was technical, but the value was financial: keeping the validation in-house meant the contractor scope evaporated.

Results

OutcomeValue
External contractor scope avoided$49,031 (documented cost avoidance)
Downstream system disruption at cutoverZero
Downstream consumers validated5 (Oracle, SAP HANA, iDashboard, PowerBI, Nighthawk)
Reports broken at go-live0

Why this matters beyond the dollar number

The $49K is the headline, but the more interesting story is the pattern. Operations-heavy organizations default to engaging external contractors for schema-validation work because it’s specialized, time-bound, and seen as a one-off. An internal data SME with deep operational-data knowledge can almost always do this work better, faster, and with continuity — because they understand both the source schema and every downstream report that depends on it.

This is the kind of work an in-house engineer with cross-system context delivers that an external contractor cannot.

Some specifics are abstracted for confidentiality. Happy to go deeper on the technical approach, the failure modes we worked through, or the operational outcomes — jose@macias-tech.com.