Overview
Industry: Medical Devices & Healthcare Manufacturing
Geography: Global
Technologies: AWS Data Lake, Tableau, DataOps, AWS Analytics Services
The Situation
A leading medical products manufacturer was operating with data spread across ERP, CRM, distribution, and HR systems each serving its operational purpose, but none connected for reporting. Business teams needed consolidated visibility across sales, inventory, finance, HR, and production, but getting it meant manual extraction, repeated reconciliation, and heavy reliance on technical teams.
Dashboard preparation was slow and labour-intensive. KPI definitions were inconsistent across departments. Leadership lacked timely access to the operational performance data needed for confident, data-driven decisions. And with no governed data foundation, future AI and analytics initiatives had nowhere reliable to build.
The organisation needed more than better dashboards. It needed a scalable, trusted data platform one that could serve current reporting needs and grow into advanced analytics and AI decision support over time.
What Codincity Did
Codincity designed and implemented a cloud-native AWS data platform built on a medallion-style architecture, supported by DataOps practices for operational reliability and governed analytics delivery.
Built a scalable AWS data lake with distinct raw ingestion, cleansed and standardised, and business-ready Gold mart layers creating clear separation between data acquisition, transformation, and consumption.
Integrated key operational sources including ERP, CRM, distributor management, and HR, with ingestion schedules tailored to the refresh requirements of each business domain.
Established governed KPI and Gold mart layers with standardised business definitions and consistent calculation logic, ensuring dashboards are trusted and comparable across teams.
Modernised business dashboards in Tableau, improving executive visibility into operational metrics and enabling drill-down analysis across sales, finance, inventory, and distribution.
Embedded DataOps practices pipeline monitoring, data quality checks, alerting, and recovery mechanisms so the platform operates reliably without manual oversight.
Designed the architecture for future AI and GenAI capabilities, including natural-language querying, anomaly detection, and role-based business insight generation.
Business Impact
Consolidated operational visibility across sales, inventory, finance, HR, and distribution replacing manual reporting with governed, near-real-time data flows.
Significantly reduced dependency on manual report preparation and cross-system reconciliation.
Reduced manual reporting effort by up to 80% while eliminating repetitive cross-system reconciliation activities..
A scalable AWS data lake foundation that supports current dashboards and future analytics expansion domain by domain, source by source.
A DataOps operating model with pipeline monitoring, quality validation, and automated recovery delivering reliability as a continuous capability, not a one-time build.
An AI-ready data foundation enabling the organisation to pursue intelligent decision support, natural-language analytics, and automated alerting as the next step.
What It Means Going Forward
The transformation is not limited to dashboard delivery. The larger value is the creation of a repeatable data and analytics foundation. With Codincity’s implementation approach, the manufacturer can now expand the platform source by source, domain by domain, and KPI by KPI. New dashboards can be built on governed data marts instead of isolated extracts. Data quality issues can be detected earlier. Business users can work from a shared version of truth. Leadership can make decisions based on timely, structured, and reliable insights.
The platform also opens the door for intelligent decision-making capabilities such as AI-assisted KPI interpretation, anomaly detection, business alerts, natural-language data exploration, and role-based performance summaries.
Conclusion
By establishing a modern AWS-based data platform and DataOps foundation, Codincity helped the manufacturer transform fragmented operational data into a governed, analytics-ready asset. Standardized KPI definitions, modernized Tableau dashboards, and reliable data pipelines have improved operational visibility while reducing dependence on manual reporting. With a scalable architecture designed for future AI and GenAI use cases, the organization now has a trusted foundation for faster insights, stronger governance, and more intelligent decision-making.
