From siloed tools to a single source of truth: a data platform and self-serve BI for a pharmacy group

Summary

A growing multi-location pharmacy group ran sales, marketing and operations on disconnected systems — a CRM, a pharmacy management platform, a call platform and advertising platforms — with no single source of truth and reporting trapped in spreadsheets and email. Quarry built an ingestion layer, a modeled cloud data warehouse and a Looker BI layer that gave three departments self-serve dashboards, plus activation loops that pushed insight back into day-to-day operations.

Team
Data Architecture & Engineering
Team members / roles
Evgeniy Kulish / Principal Architect, Commerce & Data
Vishnu Sankar / Senior Software Engineer
Tanya Doiun / Data Engineer
Simon Rahr Mortensen / Data Engineer
Puneet Maloo / Principal Engineer
Released
October 2025
Duration
~14 months
Data05.17.26

Photo by Árpád Czapp on Unsplash

The Challenge

A multi-location pharmacy group ran sales, marketing and operations on four disconnected systems: a CRM, a pharmacy management platform, a calling tool, and ad platforms like Meta. Critical reports arrived only as CSV files in shared inboxes. There was no single source of truth.

Every cross-functional question turned into a manual spreadsheet exercise: which providers had stopped ordering, whether ad spend was moving prescription volume, where operations were leaking time. Decisions waited on whoever could assemble the latest export. Each team worked from a partial, stale view of the same business. And as one member of a larger, growing healthcare group, the pharmacy could least afford to fly blind.

The Solution

Quarry built an end-to-end data platform that turned those scattered systems into one trustworthy foundation.

First, we built ingestion services that connect to each of those sources and continuously pull their records into a managed PostgreSQL warehouse — including the report files once trapped in email.

On top of it, we modeled a clean reporting layer: prescription history, customer activity, and order patterns refreshed daily, weekly and monthly. Patterns that were invisible in any single tool became something the business could see: who was slowing down, who was about to churn. Every team works from the same data instead of raw exports.

From there, we put the data to work in two directions. For analytics, a Looker layer gave sales, marketing and operations their own dashboards — each team answering its own questions from one governed source. For operations, we closed the loop: business-logic models we built flagged the accounts that mattered — who had gone quiet, who never ordered, who was slipping — and wrote those signals back into the CRM. A rep now starts the day with that list, instead of a report nobody opened.

For the first time, every team is looking at the same numbers. Questions that used to take a day in spreadsheets now take a few clicks — and the system tells us which accounts to chase before we even ask.

Operations Lead multi-location pharmacy group

The Results

The group went from spreadsheet-bound guesswork to one source of truth every department trusts. Sales, marketing and operations each have self-serve Looker dashboards in place of manual export-and-stitch cycles.

More important, the data became operational: the platform doesn’t just describe the business, it prompts action inside it. And because the foundation was centralized rather than tied to any single tool, it scaled as the wider healthcare group grew around the pharmacy — without the reporting layer cracking.

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