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Pharma analytics can be improved with new technologies

Pharmaceutical companies have often counted on scientific data to classify patterns, research hypotheses, and the efficiency of treatments.  Data analytics is just another phenomenon that provides people with ever greater access to information and data. Today, through modern Pharma analytics technologies, we have the capacity and resources to process and make sense of the data, which represents a tremendous opportunity for scientists and pharmaceutical companies.

Leveraging these new technologies and advanced analytics presents a real and significant opportunity for the pharmaceutical industry to accelerate drug discovery and development. Pharma Analytics offers a variety of pharmaceutical research and analytics resources and tools to help identify and evaluate competitive opportunities within the global pharmaceutical marketplace. You will have access to competitor and market access information, as well as the opportunity to compare and track with pharmaceutical data and analysis from leading industry experts in the pharmaceutical sector.

Driving the pharma analytics sector 

Many new-age healthcare technology and data analytics organizations are now starting to develop structured, standardized, and universally accessible database systems to produce clinical and commercial insights for pharmaceutical companies. Many of the global pharma industry have established the need and the value of sales force effectiveness and optimization.

Predictive analytics

Another emerging analytical technique that is rapidly growing among the pharmaceutical industry players is predictive analytics. Predictive analytics uses many techniques – data mining, statistics, modeling, machine learning, and artificial intelligence. In pharmaceuticals, predictive modeling can help identify new potential drugs with a higher probability of being successfully developed and approved.

Big data infrastructure

Pharmaceutical firms are still under pressure to embrace cutting-edge drug innovations and enterprise-wide M&A to diversify the product mix and keep revenue sources going. So implementing the Big Data infrastructure allows for faster data processing, which in turn enables organizations to support scientific analytics and to derive more focused business outcomes for next-gen research. Big Data architecture includes a radically integrated repository with scalable collaborative interfaces and advanced analytics with flexible deployment options.

AI and Machine Learning

AI and Machine Learning now take it to the next level – moving beyond providing basic insights and creating genuinely innovative enhancements for multiple pharmaceutical processes. They help in improving patient recruitment, optimizing trial design, and trial output optimization. They are also enabling predictive power in the R&D process.

These new technologies help pharmaceutical companies to be able to perform in-depth competitor analysis and monitoring and to improve in-house processes with data-backed insights. These new technologies of pharma analytics promise several cutting-edge innovations to deliver insights into pharma and formulate a fact-based strategy in the global market.

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