Following the pandemic, analytics and AI adoption rose drastically in the life sciences industry. The industry was at the epicenter of the COVID-19 crisis; therefore, pharma enterprises had to scale their businesses and rethink their operational methodologies to navigate the pandemic.
Primary enterprise objectives for analytics adoption are improving productivity, increasing cost efficiencies, and enhancing stakeholder experience. Increasingly, enterprises are realizing the need to modernize their operations to build a resilient supply chain and sustainable business, which has driven thematic analytics investments in areas such as digital therapeutics, Internet of Medical Things (IoMT), and Real World Evidence (RWE).
In this report, we analyze the current state of analytics adoption in the life sciences industry; the impact of analytics on the emergence of new industry-specific themes, advanced analytics and AI use cases, and the characteristics of analytics-focused deals. We also discuss how the pandemic has influenced the traditional workflow of pharma enterprises and how they can harness the value that analytics delivers going forward.
Scope
Industry: life sciences
Geography: global
Contents
This report examines:
The current state of analytics and deal characteristics in the life sciences market
Emerging themes in life sciences industry driving analytics and AI adoption
Analytics and AI adoption use case analysis by value chain segment
Impact of COVID-19 and enterprise mitigation strategies
Advances in data management and analytics use cases are helping enterprises across industries focus on business outcome-oriented Data & Analytics (D&A) initiatives. Process improvement, asset management, cost efficiencies, and better customer experie…
