Harnessing Machine Learning for Optimized Data Matching
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About
Teaming up with Strongbytes, an IPGE platform aimed to smoothly source, de-identify, and match 330 million patient data entries with top-tier precision. Ensuring HIPPA compliance, the initiative reshaped the landscape of data handling in healthcare. Read more
CASE STUDY DETAILS
Under MNDA
Enterprise/Corporation
Team augmentation
1 - 5 people
Ongoing for 9 months
€50 - 250K
Scrum
FOCUS AREA
Challenge, approach, and impact
Varied and Unstructured Data
The vast majority of incoming data was inconsistent, appearing in multiple forms, and predominantly unstructured. Achieving accurate data ingestion presented a formidable challenge due to these disparities.
High Volume Transactions
Handling 1.5 billion transactions from diverse sources and converting this data into standardized sink tables was an enormous task. Ensuring the precision and efficiency of this process was crucial.
Testimonials
“Our biggest challenge in this project was the fact that we were handling a massive amount of data, coming from multiple sources and we needed to find a way to operate with this, in a compliant and secure way. Matching data to standardized tables then came after sufficient research and analysis using NLP and Deep Learning Classification.“ Read more
Anonymous
Delivery Manager
VERIFIED
“Our biggest challenge in this project was the fact that we were handling a massive amount of data, coming from multiple sources and we needed to find a way to operate with this, in a compliant and secure way. Matching data to standardized tables then came after sufficient research and analysis using NLP and Deep Learning Classification.“ Read more
Anonymous
Delivery Manager
VERIFIED
“Our biggest challenge in this project was the fact that we were handling a massive amount of data, coming from multiple sources and we needed to find a way to operate with this, in a compliant and secure way. Matching data to standardized tables then came after sufficient research and analysis using NLP and Deep Learning Classification.“ Read more
Anonymous
Delivery Manager
VERIFIED
“Our biggest challenge in this project was the fact that we were handling a massive amount of data, coming from multiple sources and we needed to find a way to operate with this, in a compliant and secure way. Matching data to standardized tables then came after sufficient research and analysis using NLP and Deep Learning Classification.“ Read more
Anonymous
Delivery Manager
VERIFIED
“Our biggest challenge in this project was the fact that we were handling a massive amount of data, coming from multiple sources and we needed to find a way to operate with this, in a compliant and secure way. Matching data to standardized tables then came after sufficient research and analysis using NLP and Deep Learning Classification.“ Read more
Anonymous
Delivery Manager
VERIFIED
“Our biggest challenge in this project was the fact that we were handling a massive amount of data, coming from multiple sources and we needed to find a way to operate with this, in a compliant and secure way. Matching data to standardized tables then came after sufficient research and analysis using NLP and Deep Learning Classification.“ Read more
Anonymous
Delivery Manager
VERIFIED
Team Structure
Client Team
Daily point of contact
Product Owner
Agency Team
Production
2 x Machine Learning Engineer
Production
1 x Frontend Developer
Governance
1 x Architect
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