Clinical Research Organization : From Raw Data to Insights Through a Robust Data Pipeline

### **Objective** The goal of this project is to create a smooth, reliable, and scalable way to bring all clinical and operational data into one unified platform. This will ensure that study data is consistently clean, organized, and ready for analysis without manual effort or delays.​ By modernizing how data is collected, prepared, and validated, we will make it easier for teams to access accurate information, reduce errors, and accelerate reporting. This solution will improve oversight, strengthen data quality, and enable faster, more confident decision-making across studies, sites, and stakeholders.​ **Implement a workflow that sends the required study and billing updates back into Veeva after processing.** ![]() ### **Technology** **1. Pipelines** – Automated flows that bring data from different systems into one place. **2. Processing (Notebooks)** – Tools that clean and prepare the data for use. **3. Lakehouse** – A central storage layer for both raw and refined data. **4. Warehouse** – A structured layer built for fast reporting and analysis. **5. Model** – A simplified view of the data that makes reporting easier. **6. Dashboards** – Visual reports that show insights and updates in real-time. **Goals** **1. Centralize All Study Data** – Bring data from diverse data sources into one unified platform instead of multiple scattered sources. **2. Improve Data Quality** – Clean, standardize, and validate all incoming data so it becomes accurate, consistent, and ready for reporting. **3. Enable Faster, Reliable Reporting** – Make [Power BI dashboards](https://dreamitcs.com/blogs/top-5-reasons-to-combine-power-bi-with-microsoft-fabric-for-seamless-analytics/) run faster by using Lakehouse storage, incremental loads, and optimized data models. **4. Reduce Manual Work** – Automate data ingestion, transformation, and push-back processes to remove manual file handling and repeated work. **5. Support Study-Level Billing** – Ensure correct and updated information is pushed back into Veeva to help the billing team generate accurate invoices. **6. Build a Scalable Architecture** – Create a system that can easily handle more studies, more APIs, and new requirements in the future without major rework. **Solution** **1. Connected All Sources** – Integrated iMednet, Medidata, and Veeva into a single Lakehouse using secure API and SFTP connections. **2. Built Automated Pipelines** – Set up Microsoft Fabric Pipelines to fetch, load, and refresh data automatically at scheduled intervals. **3. Structured the Data for Use** – Organized the data into a clear, consistent format so teams can use it directly for reports and analysis. **4. Enabled Veeva Push-Back** – Implemented a workflow that sends required study and billing updates back into Veeva after processing. **5. Used Fabric-Only Architecture** – Designed using Microsoft Fabric tools, keeping the solution lightweight, simple, and cost-effective. **Pre-Fabric Architecture Overview** ![]() **Before Fabric Implementation:** 1\. [Power BI](https://dreamitcs.com/services/advanced-analytics/) was directly taking data from APIs, making everything slow and dependent on API speed. 2\. Reports took **5–6 hours to refresh** because all data was pulled every time. 3\. No centralized storage — no organized or reusable dataset. 4\. No incremental load — system downloaded all data instead of only new/updated records. **Post-Fabric Architecture Overview** ![]() **After Fabric Implementation:** 1\. All data now lands first in the Lakehouse through automated pipelines, giving us one clean and organized place to store everything.​ 2\. The data is processed through the Medallion Architecture (Bronze → Silver → Gold), which makes it structured, standardized, and ready for reliable reporting.​ 3\. Power BI connects directly to the Lakehouse using Direct Lake, allowing dashboards to load instantly without depending on API calls.​ 4\. Performance has improved significantly because we no longer hit APIs repeatedly; API load is reduced and system stability has increased due to incremental load.​ 5\. Report refreshes are now stable and fast, completing in just 3–4 minutes instead of several hours.​ **Previous Manual Process** ![]() 1\. Files sent to Veeva through manual upload like Excel,csv etc​ 2\. High chances of human error​ 3\. No automated checks or validations​ 4\. No audit trail or version control​ 5\. Slow and inconsistent billing updates.​ 6\. It can range from minutes to several hours or days for large-scale data **Automated Push Pipeline** ![]() 1\. Fabric prepares and validates study data. ​ 2\. IMednet and Medidata Data is pushed directly back into Veeva via Pipeline​ 3\. Consistent, error-free updates​ 4\. Complete logging + audit trail​ 5\. Faster and accurate billing cycles​ 6\. Bulk processing enabled \~30,000 records processed in under 2 minutes.​ 7\. Maintain Configuration ​ 8\. A complete logging framework is implemented that tracks every step of the push pipeline including successes, validations, warnings, and errors ensuring full transparency and traceability.​