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Business Problems
Following a strategic acquisition in the pharmaceutical sector, a large parent company took over a smaller, regionally operational pharmaceutical firm. While the merger opened new global markets and enhanced R&D capabilities, it also introduced a critical operational challenge—data Unification across two legacy SQL Server databases.
Project Plan
1. Data Mapping Strategy
Inputiv began by analyzing both databases’ schemas in detail. Key deliverables at this stage included:
- Entity and Attribute Mapping: Every field (e.g., composition formulas, ingredient sources, shelf life durations) was aligned from the child schema to the parent schema.
- Custom Data Dictionary: Created to serve as a reference layer between both systems.
2. ETL Layer Formation
A custom ETL (Extract,Transform, Load) layer was engineered using Python, with the following structure:
- Extraction: Data was securely pulled from the child company’s SQL Server instance.
- Transformation: Python scripts normalized, cleaned, and matched fields to the parent company’s schema.
- Loading: Transformed data was then injected into the parent database, using INSERT/UPDATE strategies while ensuring no data duplication.
3. Middleware Bridge
To prevent operational disruption for the parent company’s users:
- A middleware abstraction layer was created.
- This layer pulled data from the child database and staged it for ingestion, allowing the parent system to consume child data in real-time.
- Users continued working in the parent company’s system without needing to toggle between interfaces.
Challenge 1: System Fragmentation
Issue: Parent company users were averse to working with multiple systems.
Solution: The middleware and Python-based unification layer abstracted away the complexity. Post-migration, users accessed consolidated data through a single interface—the parent company’s system.
Challenge 2: Data Integrity & Validation
Issue: Over 17,000 ingredients across various countries had inconsistencies and formatting issues.
Solution: Inputiv implemented multiple validation checkpoints within the Python script to ensure:
- Correct data types
- Referential integrity
- Shelf life computations and expirations were recalculated and validated
Audit logs and error reports were generated after every batch load.
Challenge 3: Schema Differences
Issue: Minor but critical differences in how similar entities were represented.
Solution: A dedicated mapping layer was built, allowing future transformations to adapt to schema changes dynamically, without rewriting the whole script.
Benefits
- Successful Data Migration: All records from the child company were mapped and inserted into the parent’s system with zero data loss.
- Consolidated Interface: Parent company users accessed the combined data without needing to learn or switch to a new system.
- Regulatory Compliance: By maintaining accurate data about ingredient origins and shelf lives, the migration complied with pharmaceutical industry regulations.
- Future-Ready Architecture: The Python-based data bridge can be reused for future acquisitions or system upgrades.
This case exemplifies how Inputiv enabled smooth operational continuity and strategic scalability through precise data engineering. By focusing on accuracy, minimal user disruption, and robust middleware solutions, Inputiv transformed a complex data merger into a seamless business win.
