Big data has become a pillar in modern pharmaceutical practices. The accuracy and accessibility of the insights provided by data have led to breakthroughs, specifically within the rigorous drug discovery and development phases.
Pharmaceutical companies have also leveraged big data in third-party collaborations, resulting in more efficient product deployment and customer experiences. The precision of big data enables decision-makers to expedite pharma processes across multiple insightful analytics, including predictive, diagnostic, and prescriptive solutions.
Industry experts predict the pharmaceutical analytical testing market could total an estimated USD 8.98 billion in 2024. These findings forecast the market segment to grow steadily at a CAGR of 8.41%, reaching USD 13.43 billion by 2029.
The increasing demand for data analytics in pharma could suggest a prioritized recruitment of talent with the relevant hard skills and experience to help organizations leverage these technologies. MRINetwork highlights the latest big data trends in pharma and the benefits of attracting the most qualified analysts.
Understanding The Latest Trends in Big Data in Pharmaceuticals
The accessibility and interoperability of big data enable pharmaceutical companies to compile and consult multiple insights from various online sources. Companies can apply the integrated approach to derive critical insights from raw data. By doing so, pharma organizations can expedite clinical trials, enhance services, and update products with greater cost-effectiveness.
Pharmaceutical researchers can also mine customer data to gauge the effectiveness of a product launch. These intuitive data-backed technologies enable teams to avoid costly product recalls and PR crises so companies can focus on developing impactful pharma solutions faster while avoiding customer satisfaction pitfalls.
The widespread implementation of cloud data management, artificial intelligence (AI), and machine learning (ML) has led to additional big data usage in pharma. These technologies enable data managers to categorize, distribute, secure, and interpret data with greater consistency and scalability. As such, a dynamic, data-based, digital approach to pharma research has gradually replaced traditional paper trails and manual monitoring in driving cost-effective outcomes.
The Rising Need for Big Data Roles in Pharma
Rather than identifying the industry’s most popular data management roles, pharmaceutical leaders should consider the ongoing challenges in adopting the technology. Recognizing these market challenges and talent gaps enables recruiting teams to refine their strategies for long-term success as big data becomes a mainstay factor.
Avoiding Reactive Strategies
The cost of pharma crisis management has become increasingly challenging with the recovering post-pandemic economy. Reactive crisis management strategies, which were the traditional solutions, may no longer prove practical in the long term.
Industry research has shown a significant dip in pharma R&D ROI in the post-pandemic landscape. These figures include longer cycle times, where the average cost of developing a new drug rose by $298m to $2.3bn in 2022. The market survey based on the productivity of 20 leading biopharma companies also revealed a decline in the peak sales per asset (the annual amount generated by a released product).
The trends suggest an increasing need for pharma companies to redefine clinical trial standards. For example, decision-makers should consider adopting a proactive approach to clinical trials and product development. Big data and intuitive technologies, such as advanced AI algorithms, can help organizations achieve more patient-centric solutions within a shorter period.
Notable Use Cases for Data in Pharma
We identified the following data analytics advantages. These implementations optimize pharma company performances through a methodical, time-bound, and measurable approach to drug research and development.
- More efficient supply chains – Big data can help pharma organizations efficiently handle demand forecasting and inventory management. For example, pharmaceutical company Merck leveraged AI analytics in augmenting supply chain practices, resulting in 95% on-time in full (OTIF) for their product deliveries.
- Accelerated trials with stratification – The comprehensive and updated insights from big data analytics enable pharma companies to optimize and personalize clinical trials for various patient demographics. Trained pharma teams can apply technologies such as electronic data capture (EDC) to standardize real-time data collection. Drug researchers can also cross-reference this real-time information with historical clinical trial results while comparing them with synthetic control group data for greater validation of study results.
- Improved quality control and compliance – Pharma decision-makers can continuously improve product quality throughout the R&D cycle by integrating analytical software synced with the latest industry compliance guidelines. Pharma companies can effectively capitalize on new targets/market demands while aligning with updated regulations.
Successful Recruiting in the Pharma Big Data Landscape
The shifting paradigms in pharma practices could require reviewing existing hiring and recruiting practices to account for the technological demands needed to manage proactive drug management protocols.
Your company can improve the right job fit by focusing on data specialization aspects in talent acquisition and on skills that enable your team to predict and cater to the ever-changing demands of patients, such as maintaining the most suitable pricing. Recruiting with a priority on data analytical skills could also significantly enhance critical science-based decisions across delicate processes such as clinical trials, ultimately influencing R&D timelines and overall revenue.