Big Data Science
Balancing volume, velocity and variety in big data science
Is your organisation getting trapped in the quicksand of big data? Big data in pharma is set to revolutionise R&D, but only if a balance across volume, velocity and variety can be achieved. We work mostly with structured data generated from different sources – patients, physicians, clinical trials, sales and distribution systems – so it is critical to utilise the wealth of information effectively. Some organisations struggle with legacy systems that hold heterogenous and disparate data. This is where Prospection’s data analytics capabilities make a difference. With our data science expertise and machine learning combined with rich data visualisation, we are able to assess the data volume for quality, relevance, accelerate the speed of analysis and deliver the insights to facilitate decision making.
Leverage Big Data Science to:
Dramatically decrease the cost and time spent in planning for access and product launch preparation. Machine learning can process huge amounts of data to identify patient cohorts and opportunities for the greatest impact to patients, payors and your business.
Provide well-grounded insight to guide government bodies and health providers in making important decisions in drug approvals.
Enable real world evidence to be available smarter, faster, and more effectively by using structured public information sources, health databases and more to better qualify and target the right patient for the right treatment.
Make sense of patterns in data to improve disease prediction and management, for instance, by predicting where and when a patient could experience disease progression, with better accuracy.