The pharmaceutical industry is currently under growing pressure from a stagnant R&D pipeline due to declining clinical trial success rates. Per the McKinsey Global Institute, applying big-data strategies can better support decision-making and generate nearly USD 100 billion annually across the US healthcare system. By improving the efficiency of research and clinical trials, optimizing innovation, and building new tools for physicians, insurers, consumers, and regulators, big data strategies can certainly make this possible.
Value chain digitization with increasing cloud-based services and mobility solutions, proliferation of social media, and innovative and customized delivery are the major drivers for increasing demand for analytics in the pharma industry. In the healthcare domain, data growth is generated from various sources like the R&D process, pharma retailers/distributors, and caregivers. Effective utilization of this data helps pharma companies identify new potential drug candidates and quickly develop them into approved and reimbursed medicines.
There has been a rise in demand for outsourcing (manufacturing, R&D, etc.) in the pharmaceutical and life-sciences industry. For R&D, there is an increasing need for superior innovation and lower costs. This can only happen with a comprehensive pharmacological understanding at a molecular level, better grasp of the side effects and intricacies caused by the drugs, and greater collaboration among the industry, regulators, governments, academia, and healthcare providers. With the help of analytics, the R&D divisions per phase, drug, and disease are benchmarked to identify the best and the worst performing units, resulting in the significant reduction of the outsourcing costs. These analytical solutions are expected to provide around 40% savings.
Firms like MU Sigma and ZS Associates are already providing analytical services to some pharma companies. Their analytical expertise and in-depth industry knowledge empowers leading pharmaceutical organizations with better decision-making capabilities through licensing evaluation, competitive landscape, pricing analysis, forecasting, and market entry analysis. Pfizer is delivering a program called “Precision Medicine Analytics Ecosystem,” which connects the dots among genomic, clinical trial, and electronic medical recorded data. This helps in quickly delivering new drugs for specific patient populations.
While adoption of analytics is still at a nascent stage in the pharmaceutical industry, third-party data analytics services are growing rapidly and are expected to increase by more than five times their current size by 2020. Leading pharmaceutical companies are anticipating taking a step ahead of their competitors, or at least a giant leap from stagnation, with implementation of data analytics into their management systems. Big data implementation helps pharmaceutical companies integrate all data, employ IT-enabled portfolio decision support to ensure appropriate allocation of scarce R&D funds, raise clinical-trial efficiency, and improve safety and risk management.
Predictive analytics is the emerging analytical technique for real-time management of the environmental challenges faced by the industry players. 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.
In the changing healthcare environment, pharmaceutical companies and healthcare organizations are challenged by pressures to reduce costs, be more patient-centric, and improve coordination and outcomes. Traditional marketing approaches (e.g. segmentation and promotion) have evolved over time, but the combination of new regulations and emerging digital applications has increased the demand for innovative solutions. The shift in favor of patient-centric healthcare services requires a new approach to data analysis and marketing.
Pharmaceutical companies are continually looking for new ways to better understand their customers and sales trends. To achieve a competitive edge, proactive communication, immediate solution driving capabilities, quick identification of weaknesses, and streamlining of business processes are the major reasons why pharmaceutical companies are investing in predictive analytics.
Pharmaceutical companies are continually looking for new ways to better understand their customers and sales trends. Applying big-data strategies can better support decision-making with improved efficiency of clinical research, optimized innovation, and building new tools for physicians, insurers, consumers, and regulators. Pharmaceutical companies are also investing in predictive modeling, an emerging analytical technique, which can help identify new potential drugs with a higher probability of being successfully developed and approved.