Data quality is a key word in Life Sciences, especially due to the numerous regulatory requirements and business integrations in the industry.
Many companies are finding themselves dealing with reporting where poor data quality causes misinformation and countless hours spent on investigating data issues. When migrating from legacy systems to new solutions issues and concerns regarding data quality and data readiness often occur. On top of that, it is not uncommon to experience data issues when bridging the gap from documents to structured data.
In this webinar we will go through our standardization method when working on improving data quality and increasing information correctness and availability. The three phases that we will go through are; data profiling, data enhancement and data migration & interfacing. In NNIT we call this Intelligent Data Quality.
In our solution we leverage a standardized transformation engine and AI Document Mining that makes it possible to extract structured data from documents. The latter part will be presented through a case from the large Danish pharmaceutical company, LEO Pharma.
• The importance of Data Quality
• The three phases of Intelligent Data Quality
• Data profiling
• Data enhancement
• Data migration & interfacing
• Case example: AI Document Mining at LEO Pharma
Peter Smedegaard Andersen, Advanced Advisory Consultant, Life Sciences, NNIT
Sille Lie Barrett, Consultant, Life Sciences, NNIT
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