The clinical development landscape is rapidly evolving, with hybrid trial approaches and the growing use of RWD/RWE. Clinical trial sponsors are increasingly challenged to manage the vast volume of data generated during multiple, simultaneously running trials. This data explosion, combined with heightened regulatory scrutiny from bodies like the US FDA, is pushing sponsors to take greater ownership in trial oversight, risk management, and patient safety. The challenge is exacerbated when clinical trials are outsourced, as sponsors lose real-time access to trial data, delaying critical decision-making and risk management. The solution lies in an end-to-end unified clinical Data and Analytics (D&A) platform, which provides a single source of truth by consolidating trial data across multiple solutions, ensuring quality and standardization and delivering real-time trial performance analytics. This report addresses the key challenges sponsors face in managing clinical data, the benefits of deploying an end-to-end unified clinical D&A platform, and implementation considerations. It is tailored to assist biopharma and MedTech enterprises, as well as contract research organizations, in streamlining their clinical data management, enhancing trial oversight, and improving decision-making. Scope Industry: life sciences Geography: global Contents In this report, we outline: The business challenges that sponsors face in managing clinical data An end-to-end unified clinical D&A platform’s benefits Key implementation considerations for clinical D&A platforms Membership(s) Clinical Development Technology Life Sciences Information Technology Sourcing and Vendor Management

In the rapidly evolving life sciences landscape, where the pursuit of innovative therapies and life-saving drugs never ceases, science and technology are increasingly playing a vital role. High-performance Computing (HPC) stands at the forefront of this technology revolution, empowering pharmaceutical and medical device enterprises, as well as research and academia, to unravel the complexities of biology, medicine, and multi-omics like never before. HPC is no longer confined to successful pilot phases but is transitioning to wider adoption, addressing essential business needs across industries, including life sciences. The pharmaceutical industry, dealing with vast volumes of complex and heterogenous data, is an ideal sector for harnessing the benefits of HPC – particularly around intricate biological interactions, complex data analysis, and sophisticated simulations. In this viewpoint, we study HPC’s high-potential use cases in life sciences and how pharmaceutical companies can derive success from their HPC initiatives. Scope Industry: life sciences Geography: global Contents In this report, we explore: HPC’s evolution, including the examination of an HPC-enabled system and the catalysts driving its adoption across industries The current supply ecosystem, highlighting prominent pharmaceutical and medical device HPC use cases The drivers and barriers to HPC adoption and important enterprise considerations before transitioning to HPC A step-based adoption roadmap for life sciences enterprises and a peek into HPC’s future in life sciences Membership(s) Life Sciences Information Technology Outsourcing Excellence

The COVID-19 outbreak compelled the life sciences industry to innovate and transform digitally. However, the current global macroeconomic and socio-political uncertainty, coupled with increasing research and development IT expenditure, have created immense pressure on the pharmaceutical industry to expedite the drug discovery and development process while reducing resource consumption. As a result, pharmaceutical companies are prioritizing investments with the potential for quicker return on investment over moonshot investments. Legacy technologies lack the real-time or data integration capabilities that AI and analytics solutions offer, leading to a lack of visibility for pharmaceutical enterprises into the interplay among stakeholders in the life sciences technology landscape. This has caused pharmaceutical companies to partner with product and IT service providers to improve their in-house talent, reduce time-to-insights, replace obsolete models, and understand fast-evolving customer behavior. Although AI offers significant benefits, it presents unique challenges such as the availability and quality of training datasets from multiple sources, a demand-supply mismatch for talent with industry and technology expertise, and infrastructure and complex integration issues. Therefore, pharmaceutical companies need to prioritize use cases based on specific market requirements and business needs. In this report, we discuss how a blueprint for success can enable enterprises to maximize the value of their AI-empowered initiatives and investments. Scope Industry: life sciences Geography: global Contents In this report, we examine: The current state of AI in the pharmaceutical industry Trends driving the adoption of AI across the pharmaceutical value chain Roadblocks and controversies around AI in the pharmaceutical industry Prominent AI use cases across the pharmaceutical value chain Sourcing considerations for AI solutions and suppliers Membership(s) Life Sciences Information Technology Sourcing and Vendor Management

Accelerated digital transformation is driving up data volumes exponentially for every industry, and life sciences is no exception. In fact, the pandemic has demonstrated that data is a vital asset for the industry to respond to market changes. Data-led initiatives helped reduce COVID-19 vaccine trial timelines and scale up manufacturing. As life sciences organizations come to terms with this new reality, it is important for them to think about the value that can be unlocked from data. This study discusses the current state of data and analytics in life sciences and how it has shaped during the pandemic. We explain how life sciences firms can get started and build momentum to maximize the outcomes from their data and analytics initiatives. The life sciences industry has been committed to making a difference in the lives of patients and communities globally. Data and analytics can prove to be an ally that can help accelerate efforts to stay committed to the goal and make a difference. Scope Industry: life sciences Geography: global Contents In this viewpoint, we: Focus on challenges related to data and analytics in life sciences Identify data and analytics use cases across the life sciences value chain Guide enterprises on getting started on their data and analytics initiatives and building momentum to maximize the outcomes Help enterprises build a vision and strategy for long-term success Membership(s) Life Sciences Information Technology Data & Analytics Sourcing and Vendor Management

This viewpoint examines the digitization of the life sciences manufacturing value chain through the Industrial Internet of Things (IIoT). The life sciences industry is leveraging IIoT to deliver an interconnected asset ecosystem. IIoT platform vendors or service providers are making investments in enabling technologies such as RPA, analytics platforms, and cloud computing, most of which are already mature in other industries. Predictive maintenance has proved to be a leading IIoT use case in the life sciences industry. With the launch of COVID-19 vaccines, real-time tracking and tracing of vaccines using IIoT has emerged as another leading use case. In this research, we also look at IIOT implementation-related challenges due to stringent regulatory and data integrity standards in the life sciences industry. Additionally, the complex nature of the interconnected manufacturing set-up requires skilled labor and a developed infrastructure to bring about rapid changes in the industry. Only those service providers that can quickly identify and leverage IIoT initiatives will be able to reap substantial benefits. Scope Industry: life sciences Geography: global Contents In this viewpoint, we discuss: The digitization and evolution of life sciences manufacturing leveraging IIoT IIoT implementation approach, framework, and current scenario based on real-life use cases Challenges in IIoT implementation Membership(s) Life Sciences Information Technology Sourcing and Vendor Management