Case study · Pharmaceutical

Building a Unified Data Platform for a Pharmaceutical Company

Connecting manufacturing, inventory, distribution, sales, pharmacies, hospitals and commercial operations through a modern enterprise data platform.

Industry: Pharmaceutical / medical products
Project: Enterprise data engineering & analytics
Delivered by: Gouthaman · Data Engineer, Coderzon

Cloud Data Lake
ETL / ELT
Spark / PySpark
Data Warehouse
Lakehouse
Master Data Management
Data Governance
BI & Reporting

The business challenge

From fragmented systems to one trusted view

A pharmaceutical business can have ERP, CRM, manufacturing, inventory, distributor, pharmacy, hospital, e-commerce and finance data spread across disconnected systems.

Fragmented data

Operational data is distributed across multiple applications and teams.

Inconsistent reporting

Manual reconciliation slows down decision-making and creates conflicting numbers.

Limited visibility

Inventory, sales and supply-chain performance are difficult to monitor end-to-end.

Scalable analytics

The business needs governed, reusable data for BI and future AI/ML.

End-to-end business value chain

Six stages, one platform underneath

The platform sits under every stage of the chain, from the production line to the person collecting a prescription.

Manufacturing

Quality production

Production planning
Batch manufacturing
Quality control
Regulatory compliance

Inventory

Smart inventory management

Batch tracking
Barcode / RFID
Expiry management
Stock optimization

Distribution

Efficient distribution network

Order fulfillment
Real-time tracking
Temperature control
On-time delivery

Sales

Data-driven sales operations

CRM & SFA
Order management
Sales analytics
Performance tracking

Pharmacies / hospitals

Reliable supply to care points

Order management
Stock availability
Fast fulfillment
Returns management

Customers

Better health outcomes

Product availability
Trust & satisfaction
Better compliance
Improved outcomes

Integrated data platform

Data integration
Data quality
Master data management
Data governance
Secure & compliant
Real-time visibility

Solution architecture

A modern cloud data engineering foundation

The architecture ingests data from business systems, applies quality and transformation rules, and publishes curated data products for analytics and AI/ML.

Pharmaceutical data engineering architecture. Nine source systems — ERP, CRM, manufacturing, inventory, distributor, pharmacy and hospital, e-commerce, finance and external data — feed an ingestion layer of batch pipelines, APIs and streaming. That lands in a cloud data lake zoned into landing, raw, curated and reference. Processing applies validation, business rules, master data management and lineage, publishing to an enterprise warehouse of sales, inventory, finance and supply chain marts, and out to BI reporting, AI/ML analytics and data APIs. Governance, security and monitoring run across every layer. — The architecture as drawn, from data sources to insights.Open full size

Layer by layer

Data sources

The systems the business already runs

ERP — finance, procurement, HR, master data

CRM — leads, sales reps, activities, targets

Manufacturing — production, batch, quality, equipment

Inventory — stock, warehouse, batch, expiry

Distributor — orders, shipments, returns

Pharmacy & hospital — orders, sales, inventory

E-commerce — online orders, customers, payments

Finance & billing — invoices, payments, receivables

External — market, regulatory, weather, demographics

Ingestion layer

Batch, API and streaming paths into the lake

Batch ingestion

ETL / ELT pipelines
Scheduled jobs
File ingestion (CSV, Excel, JSON)

APIs & connectors

REST / SOAP APIs
Database connectors
Third-party connectors

Streaming ingestion

Event streams
Message queues
IoT / devices

Ingestion monitoring & error handling

Cloud data lake

Zoned storage, raw through curated

Landing zone — raw data as-is

Raw zone — immutable raw data

Curated zone — cleaned & enriched data

Reference zone — reference & master data

Scalable storage — secure, redundant, cost effective

Processing & transformation

Where the rules are applied

Data processing frameworks (Spark, Databricks, Flink)

ETL / ELT pipelines

Data validation & standardization, cleansing & deduplication

Business rules & enrichment

Master data management

Metadata management & data lineage

Warehouse / lakehouse

Curated and modelled, optimised for analytics

Sales data mart

Inventory data mart

Finance data mart

Supply chain data mart

Reference data

High-performance analytics layer

Curated & modelled data

Analytics & consumption

What the business actually opens

BI & reporting

Executive dashboards
Operational reports
Ad-hoc analysis

AI / ML analytics

Demand forecasting
Sales prediction
Customer segmentation
Anomaly detection

Data API services

APIs for applications & business systems

Data governance & security

Across all layers

Data governance policies
Data quality monitoring
Data lineage & catalog
Access control (RBAC)
Data privacy & compliance
Audit & logging

Monitoring & operations

Keeping it running

Pipeline monitoring
Performance monitoring
Alerts & notifications
Cost monitoring
Backup & recovery
Disaster recovery

Operations & supply chain

Connecting production to delivery

Manufacturing analytics

Production intelligence

Production & batch visibility
Quality and efficiency metrics
Raw-material consumption
Equipment and process monitoring

Supply chain intelligence

Warehouse to pharmacy / hospital

Shipment and route tracking
Inventory visibility
Order fulfillment
Regional demand and delivery performance

Commercial analytics

From CRM and sales activity to executive insight

Sales & distributor analytics

Where the revenue is made

Territory and representative performance
Distributor and customer accounts
Product and regional sales
Orders, targets and achievement

Executive business intelligence

The view from the top

Revenue and growth KPIs
Inventory health and expiry
Order fulfillment and supply chain
Regional and product performance

Technical implementation

Engineered for scale, governance and reuse

Data sources

ERP
CRM
Manufacturing
Inventory
Distributor
Pharmacy / hospital
E-commerce
Finance

Ingestion

Batch pipelines
APIs
Database connectors
Files
Event streams
IoT

Storage

Cloud data lake
Landing
Raw
Curated
Reference zones

Processing

ETL / ELT
SQL
Spark / PySpark
Data validation
Standardization
Deduplication

Data products

Enterprise warehouse / lakehouse
Sales mart
Inventory mart
Finance mart
Supply chain mart

Consumption

BI dashboards
Operational reports
Forecasting
Optimization
AI/ML-ready datasets

Key business outcomes

What the business got

Unified operational view

Faster & more accurate reporting

Improved inventory and supply-chain visibility

Data-driven sales decisions

Governed foundation for advanced analytics

Turning pharmaceutical data into business intelligence.

A centralized data platform creates the foundation for reliable reporting, stronger operational visibility and scalable analytics across the pharmaceutical value chain.

Delivered by

Gouthaman

Data Engineer · Coderzon Technologies Pvt. Ltd.