Contracted — countersigned (EPC-reported; documents private)First-pass editionA medical device manufacturer (client not named) · 2025 · Project ID EPC-2025-ANON-MEDDEV-AZDWPBI

Last updated October 25, 2026 by Errin O'Connor, Founder & Chief AI Architect, EPC Group

Opening answer. In January 2025 a medical device manufacturer's VP of Finance countersigned a statement of work with EPC Group under a master services agreement. The work: an Azure data platform — Data Factory pipelines, Data Lake Storage for staging and Synapse Analytics for reporting — that loads SQL Server and Excel data daily. It also covers dimensional models for the company's KPIs and four to five Power BI dashboards with row-level security. By July 2025 the client had shared its source workbooks with EPC Group: sales against quota, daily and international sales, product-line gross margin, repair orders and a pricing and inflation calculator.

Disclosure. This is an EPC-reported anonymized engagement summary. The client is not named. The countersigned statement of work, the master services agreement and the shared source files are retained privately and are not published in the Evidence Center, so this account is outside the Evidence Center's documented-engagement counts. Every engagement fact below is taken from those documents.

Verification class

Contracted — countersigned (EPC-reported; documents private). The statement of work was signed by EPC Group and countersigned by the client's VP of Finance in January 2025. The client shared its source workbooks with EPC Group in a shared folder dated July 2025. No dashboard acceptance, go-live record or outcome measurement is cited or claimed.

Buyer problem and scope

Item	What the documents state
Buyer	A medical device manufacturer (client not named); finance-led
Situation	Sales and finance reporting held in SQL Server and large Excel workbooks
Objective	Integrated, transformed data on Azure that supports business-critical reporting, with self-service analytics and near-real-time reporting through Power BI
Architecture	Azure Data Factory for ingestion and data flows, Azure Data Lake Storage for staging, Azure Synapse Analytics for transformation and reporting
Data	SQL Server and Excel sources, loaded by daily incremental schedules
Modelling	Star and snowflake dimensional models for the company's KPIs, with data quality, consistency and integrity validated through the pipeline and transformation logic documented
Reporting	Four to five interactive Power BI dashboards on Synapse, with row-level security and self-service analytics
Close-out	End-to-end and performance testing; architecture designs, data definitions and operational runbooks; knowledge transfer; production deployment on automated schedules; hyper-care after go-live

Unknowns, not stated in the documents cited: data volumes, the number of report users, the go-live date and any measured change in reporting time or accuracy.

Approach and artifacts

Assess the sources and the compliance needs first. Discovery evaluates the existing SQL Server and Excel sources: architecture, data quality, constraints, and an inventory of tables, schemas and file structures to size the work. It checks the readiness of the Azure services and sets data-security and compliance protocols for handling sensitive data, including any regulatory requirements that affect the design.

A roadmap before a build. The first deliverable is a data-warehouse roadmap tailored to the company: goals and technical requirements, challenges and opportunities in the current data, and a step-by-step plan for preparing both the source systems and Azure.

Pipelines, models, then dashboards. Ingestion pipelines land data in the lake and Synapse on a daily incremental schedule. Cleansing and transformation run in Synapse and Data Factory data flows. Dimensional models carry the KPIs, and the Power BI dashboards sit on top, secured by role.

Leave the team able to run it. Runbooks, data definitions and transformation logic are documented. Hands-on sessions train the finance and technical teams to use the dashboards, use Synapse for self-service analysis, and maintain and troubleshoot the pipelines.

Documents (dated, held privately):

Document	Date
Master services agreement and statement of work (countersigned)	January 2025
Source workbooks shared by the client — sales against quota, daily and international sales, product-line gross margin, repair orders, pricing and inflation calculator	July 2025

Results and limitations

Claim	Basis	Status
The company contracted EPC Group for an Azure data warehouse and Power BI dashboards	Countersigned statement of work, January 2025	Contracted (EPC-reported; documents private)
The client supplied its source workbooks for the build	Shared folder, July 2025	On file (EPC-reported)
Dashboards delivered, accepted, or in production; any measured result	No document cited	Not claimed

Provenance

Sources: the master services agreement and countersigned statement of work (January 2025) and the client's shared source files (July 2025), held privately. Engagement ID: EPC-2025-ANON-MEDDEV-AZDWPBI, an alias unrelated to the client's name.
Anonymization: industry only, under EPC Group's anonymized-case rule; the client's name, products, location, staff names, figures in the shared files and commercial terms are not published.
Not claimed: client endorsement, delivery, adoption, or any measured result.
Author: Errin O'Connor, Founder & Chief AI Architect, EPC Group, who signed the statement of work for EPC Group.

Revision history

2026-10-25: anonymized engagement summary published from the engagement's countersigned statement of work and shared files.

Author: Errin O'Connor, Founder & Chief AI Architect, EPC Group · Published: 2026-10-25.

Anonymized engagement summary: Azure data warehouse and Power BI dashboards for a medical device manufacturer's sales and finance reporting (2025)