GW
HomeWorkAboutNotesStackLab
Experience atOmnicom Group

Driving financial systems and automation across global teams.

Builder ofEnterprise AI Systems

AI agents, audit workflows, and decision intelligence at scale.

Based inNew York City

Building systems for enterprises around the world.

HomeNotesWorkAboutStackLabPartners
XLinkedInGitHubEmailllms.txt
© 2026 · New York City
All enterprise work

Platform & data case study

Finance platform connectivity.

A governed data foundation connecting ERP and analytical layers for reliable finance reporting and analysis.

Microsoft Dynamics 365 logoDynamics 365SAP logoSAP HANADatabricks logoDatabricks
Ask the project copilotGrounded in this public-safe case study.

Business problem

What people needed to solve.

Finance and operations lose time when teams work from disconnected extracts, inconsistent mappings, and unclear ownership across ERP and analytical platforms. Without an authoritative source and reconciled mappings, every report becomes a negotiation about whose numbers are right.

Decision frame

Questions the work needed to answer.

  • Which source is authoritative?
  • How do mappings reconcile across platforms?
  • What curated view is safe for analysis?
Interactive operating flowSelect any node for context or ask the AI guide.

Selected Operating layer

Curated finance layer

Designed governed finance-data flows across ERP and analytical layers, including source mappings, reconciliation checkpoints, and curated consumption views for reporting and analysis.

Implementation

From the business problem to a working operating model.

Designed governed finance-data flows across ERP and analytical layers, including source mappings, reconciliation checkpoints, and curated consumption views for reporting and analysis.

  1. 01

    Mapped ERP and analytical data flows around finance use cases.

  2. 02

    Built reconciliation and quality checkpoints before curated consumption.

  3. 03

    Created controlled views for reporting and analytical use.

Controls, approvals, and delivery constraints

The work around the work.

Enterprise systems change only when data, access, testing, ownership, and evidence move together.

  • Source ownership and mapping logic are explicit.
  • Curated views limit the risk of disconnected or unapproved extracts.
  • Internal objects, data fields, and volumes are not published.

Prior art

How leading teams approach this.

Public references for the same class of problem, so this work can be read against how other organizations are building it.

Dentsu

Dentsu democratizes analytics with Fabric, delivering 55% faster data replication

A global advertising group consolidated fragmented regional data systems and Dynamics 365 ERP data into one governed analytics platform with Power BI semantic models, cutting replication from over 45 minutes to under 20 and lowering licensing and maintenance cost.

Microsoft

Microsoft Finance delivers insights over 60% faster with Microsoft Fabric

Microsoft's finance data team moved billions of rows of revenue data onto a centralized lake with governed business logic and Power BI reporting, cutting back-end processing by two thirds and data-generation cost by half while widening access beyond finance.

Shell

Shell builds a unified data and AI platform to scale analytics

Shell replaced siloed data infrastructure with one governed platform on Databricks, giving hundreds of analysts and citizen data scientists consistent access to petabyte-scale data for simulation, optimization, and reporting.

Public-safe outcome

Finance and operations work from controlled data products with explicit ownership instead of disconnected exports, and analysts spend less time reconciling and more time interpreting.