Data Quality and Enrichment services are fundamental to ensuring data reliability. Improved Data Quality help businesses make better data-driven decisions with augmented, accurate and complete HCP/HCO information.
Pharmaceutical organizations achieve better productivity from Sales Force, reach better prospects, and perform more accurate analysis, thereby design superior marketing strategies.
Benefits for Pharma companies:
Incedo offers Data Quality and Enrichment services to enable clients support their Data Governance framework and monitor data quality on a regular basis. Using a combination of manual as well as automated services, Incedo helps in adding value, improving quality and creating meaningful insights for clients.
Incedo Tools & Accelerators
Incedo accelerators and robotic process automations for Data quality improvement expedite the data cleansing and enrichment process and help improve productivity for Sales force & Sales Operations teams.
Data Quality Measurement
Incedo’s Data Quality System uses public and commercial databases for data validation and for calculating the accuracy score of data based on the measures like Accuracy, Completeness and Consistency. The output is a Data Quality score which can be used as a threshold to measure the quality of data coming in from various sources.
Robotic Process Automation (RPA)
Incedo’s RPA brings forth the implementation of rule-based workflows and pattern recognition by “digital” robots coded to perform high-volume, repeatable tasks which previously required substantial human intervention. RPA is an enabler for organizations to drive cost efficiencies (in the range of 20%-30%) in addition to optimized & outsourced processes.
Repeatable tasks like Data Entry, Data De-duplication and Data Standardization can be converted to rule-based workflows which can be in turn be fed into an RPA engine. This can reduce the effort by more than 30 %, improving efficiency and productivity. RPA solutions can also operate on structured data, rules & workflow-based processes to more complex solutions that can learn and provide decision making by blending elements of Artificial Intelligence (AI), Natural Language Processing (NLP), and Machine Learning (ML) for repetitive usage, without any human intervention. Output of one RPA can be input for another process as well and they can be linked together to provide end-to-end automation coverage.