Introduction
As a Technical Product Manager specializing in document processing solutions, I've led a team of technologists to achieve significant efficiency gains in digital products. My focus has been on leveraging cutting-edge technologies to streamline document extraction, transformation, and loading processes, resulting in substantial cost savings and automation.
The Challenge
Our team was tasked with developing a solution to process a high volume of unstructured documents, extracting relevant information, and integrating it into our digital product ecosystem. The existing manual process was time-consuming, error-prone, and costly, processing only 800 documents per day with an accuracy rate of 45%.
Product Management Process
- Discovery and Research (2 weeks)
- Conducted stakeholder interviews across 3 departments (Case Intake, Document Processors, Reviewers)
- Analyzed 8,000 historical documents to understand content patterns
- Identified key pain points:
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Slow processing (6 minutes per document)
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Low accuracy rates (55% error rate)
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Scalability issues
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- Strategy and Planning (3 weeks)
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Defined key objectives of the project:
- Increase processing speed by 4x
- Improve accuracy to over 80%
- Reduce operational costs by 40%
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Created a product roadmap with 3 major milestones over 6 months
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Prioritized features using the MoSCoW method
- Design and Prototyping (4 weeks)
- Collaborated with UX designers to create wireframes for the user interface
- Developed a technical architecture leveraging AWS services
- Created a proof of concept using AWS Textract and basic NLP models
- Development and Testing (16 weeks)
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Implemented Agile methodology with 2-week sprints
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Integrated AWS Textract for initial document processing
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Helped development of custom NLP models using John Snow Labs' NLP library by translating Business need to Tech need
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Helped the Tech Team Built an API using AWS API Gateway for seamless integration Conducted weekly demos with stakeholders for continuous feedback
- Launch and Iteration (8 weeks)
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Performed phased rollout, starting with 10% of document volume
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Monitored key metrics daily and made rapid adjustments Gradually increased processing volume, reaching 100% after 6 weeks
Key Technologies Used and Interactions
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AWS Textract: For efficient extraction of text, forms, and tables from documents
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AWS API Gateway: To create a scalable and secure API for our document processing pipeline
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John Snow Labs NLP: Utilized for NLP pre-training and processing of unstructured text
Efficiency Gains and Results
Processing Speed
- Before: 800 documents per day (6 minutes per document)
- After: 4,000 documents per day (72 seconds per document)
Speed Improvement
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4x increase in processing speed
Accuracy
- Before: 45% accuracy in information extraction
- After: 83% accuracy in information extraction Improvement: 84% increase in accuracy
Scalability
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Before: Linear scaling (more documents = more staff)
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After: Improved scaling (can handle 5x volume with 2x cost increase)
Lessons Learned and Best Practices
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Stakeholder Engagement: Regular demos and feedback sessions were crucial for alignment and buy-in.
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Iterative Development: Starting with a MVP and iterating based on real-world usage led to a more robust final product.
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Data-Driven Decision-Making: Continuous monitoring of key metrics allowed for rapid adjustments and optimizations.
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Technology Selection: Careful evaluation of AWS services and NLP libraries ensured we chose the right tools for our specific needs.
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Change Management: Implementing a phased rollout and providing comprehensive training minimized disruption and maximized adoption.
Conclusion By following a structured product management process and leveraging cutting-edge technologies, we transformed a manual, inefficient document processing system into a highly automated, more accurate, and scalable solution. The significant improvements in processing speed, accuracy, and cost-efficiency demonstrate the power of combining strategic product management with innovative technology. This project not only solved an immediate business need but also positioned the company for future growth and adaptability in handling increasing document volumes.