Autonomous Pharma Data Extraction at Scale
MMIT · Manual Scraping to Autonomous Platform
How we replaced a costly hybrid of automated and manual data collection with a scalable autonomous extraction platform for the pharmaceutical intelligence market.
When data company MMIT sought to revolutionize its data collection landscape and web scraping processes, it called on Concept to Cloud. We didn't just help them modernize their operations; we laid the foundation for autonomous, scalable data extraction systems.
Seeking to push the boundaries of what MMIT could bring to the data industry, particularly regarding efficiency and reliability, required us to look beyond the company's current processes and far into the future.
Project Scope
A comprehensive transformation across multiple dimensions of MMIT's data operations
MVP Development
Rapid prototype to validate autonomous data extraction concepts
Prototype Building
Proof-of-concept systems demonstrating feasibility
Data Processing
Transform raw web data into actionable insights
Pipeline Design
Reusable workflows for efficient data transformation
Cloud Integration
Databricks platform deployment for scalable operations
Crawler Enhancement
DARPA-inspired web scraping capabilities
Team Training
Knowledge transfer and capability building initiatives to ensure long-term success and independence
MMIT Faced a Dual Challenge
Costly and Problematic Processes
As a company heavily reliant on web-scraped data, their existing processes were increasingly costly and problematic. MMIT's data collection methods ranged from automated scraping across various platforms to manual extraction by teams in India. As the company grew, so did the complexity of maintaining and optimizing its technology across multiple platforms.
Scalability Issues
MMIT also faced scalability issues. Adding new data sources was time-consuming and required extensive testing and validation. Quality assurance was labor-intensive, and the occasional blocking of their crawlers led to inconsistent access to crucial datasets.
Charting a New Course
MMIT decided to tackle these challenges head-on, relying on Concept to Cloud to shift the core of their operations from manual to automated, from fragmented to unified. What MMIT came to understand was that the future would likely involve autonomous, scalable data extraction systems.
Automated Operations
Shifted from manual to automated data collection, eliminating human intervention and reducing operational costs while increasing efficiency.
Unified Platform
Consolidated fragmented systems into a single, cohesive platform managing all data extraction and processing workflows.
Cutting-Edge Technology
Leveraged Databricks and DARPA-inspired crawler technology to deliver an MVP that captured complex data extraction requirements.
Scalable Architecture
Built with future expansion in mind, enabling rapid addition of new data sources without extensive testing overhead.
Data, Tech, Teams
Because they endeavored to overhaul their organization and become a modern data company, cross-team unification was critical. We created a state-of-the-art system leveraging cutting-edge technology to drive autonomous data extraction.
Enhanced Crawler
Added custom libraries for extended functionality and capabilities, enabling more sophisticated data extraction patterns
Reusable Pipelines
Designed HTML data processing pipelines for efficiency and reusability across multiple data sources
Databricks Integration
Integrated crawler and crawl process into Databricks platform for large-scale cloud data processing
Scalable Architecture
Built with scalability and future expansion in mind, supporting rapid growth and adaptation
This work created a system to ingest web-based content and transform it into tabular data, all managed through a unified platform. And we were doing it on an accelerated schedule, driving toward a robust solution to revolutionize MMIT's data operations.
A Data-Driven Success
Though the industry seems light years (or at least just plain years) from fully-automated data operations, this project is already an achievement.
Currently, web-based content is ingested into the system, transformed into tabular data, and delivered to MMIT's data warehouse, all while feeding the machine learning engine in the background for continual improvement
We formed an early vision of autonomy and created a data extraction solution delivered in a remarkably short time frame. In turn, MMIT has a workable solution much faster than anticipated
They were able to pivot from a partially manual, fragmented data collection organization into an automated, unified data company, allowing for continuous innovation at velocity and scale
The MMIT team significantly reduced their manual data collection workload and QA timeline while increasing data frequency and refresh rates for customers
Transformative Results
This project demonstrates that companies can transform themselves from the ground up and help usher their industry into the future. It is also proved that extraordinary transformations can happen when daring visions meet intelligent processes, talented teams, and rigorous research data preparation.
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