The life sciences sector serves as an enabler for both innovation and economic prosperity, spearheading advancements in medicine, agriculture, and environmental preservation. Despite its vital role, the industry faces formidable obstacles that impede its ability to address critical global issues.

Life sciences organizations face challenges such as soaring R&D expenses, regulatory obstacles, hindered talent availability, and mounting competition. Yet amidst these trials, data and analytics stand as formidable instruments in the pursuit of surmounting these adversities and discovering novel avenues for advancement and ingenuity.

This blog focuses on the obstacles currently plaguing the field of life sciences and how the utilization of data can empower organizations to successfully confront these hurdles. Equipped with the potency inherent in their data, life sciences companies can garner valuable insights, ameliorating efficiency and propelling wise decision-making to ultimately achieve enhanced results for patients, farmers, and the environment.

 

The Top 7 Challenges of the Life Sciences Sector (And How Data Can Help Overcome Them)

  • Regulatory Compliance

With regulations in a constant state of flux and rigid standards, the quest to maintain compliance could prove labor-intensive and quite complicated. Armed with historical compliance data, an organization would better locate potential risks related to deviations from regulatory adherence. Such automation in data collection and analysis is aimed at smoothing out the audit process itself while minimizing the potential for human error. Increased monitoring of information will further help businesses continue to pick out the deviations in the regulatory standards in real time and thus act on time, avoiding bigger issues. This kind of proactive approach will not only enhance compliance but also establish a culture of accountability and transparency that fosters trust among stakeholders.

  • High R&D Costs

Major industry leaders, such as Johnson & Johnson and Pfizer, allocate approximately 25% of their revenues to research and development; the average expenditure for new drug development approaches $4 billion, occasionally surpassing $10 billion. These are indeed staggering figures and overwhelmingly prove the case for a more efficient approach toward R&D. Historic R&D data analysis will allow an organization to project the success rate for a new drug candidate and, therefore, focus resources on those drugs that have a higher success rate. Secondly, efficiency in clinical trials can be significantly improved with data-driven insight, such as better recruitment of patients, shorter trial time, and a high success rate.

  • Data Integration and Management

Heterogeneous sources may lead to inconsistencies and inaccuracies in data, making it hard to support reliable decision-making. Enterprises can make use of data warehousing and data lakes to ensure that information from different sources is consolidated into a single location that allows detailed analysis for developing valuable actionable insights. Furthermore, master data management (MDM) requires proper execution for consistency and accuracy of information within the firm. Properly designed data ownership combined with sound controls for accessing the data will assist in keeping confidential and sensitive information safe but available for business purposes.

  • Talent Acquisition and Retention

In Randstad Sourceright’s 2022 Life Sciences and Pharma Talent Trends Report, 33% of those C-suite and human capital leaders point to talent scarcity as a significant pain point in this sector. Second, 8 of the top 13 priority skills that the UK Association of the British Pharmaceutical Industry (ABPI) identified in its 2023 survey incorporate some level of data expertise. This has led to 80% of pharmaceutical manufacturing facilities facing skills mismatches, while half of executives argue that it is hard to recruit staff with experience. Organizations can help themselves by making use of analytical tools to deepen their understanding of employee data in a search for factors that would be driving turnover and job satisfaction levels. Of course, such a data-driven approach will inform about the current state of workforce morale and targeted talent strategies. Companies can be more precise about possible candidate identification, and attune recruitment efforts to them more effectively if they have knowledge of the dynamics of the talent market.

  • Market Access and Reimbursement Constraints

According to a Deloitte analysis on the launches of drugs in the US, success at launch has huge ramifications for the revenue trajectory of a product. Indeed, half (50%) of drug launch failures were related to low market access. Other factors that contributed to this include poor understanding of market and customer needs at 46% and poor product differentiation at 44%. In view of the above challenges, firms have to exploit their capabilities in the generation and analysis of real-world evidence (RWE) to evidentialize the value that their products bring in order to back up reimbursement decisions. Besides, with data-driven pricing strategies, life science organizations are empowered to maximize their revenues while ensuring competitiveness within markets. It is consequently fundamental for the building of effective market access strategies in firms through a deepening understanding of market dynamics and patient populations, which may indeed very well provide the path to successful product launches and growth that can be sustained.

  • Patient Engagement and Clinical Trial Recruitment

Disturbingly, estimates from Parexel research indicate that problems with enrolling enough patients delay 80% of clinical trials, while 85% eventually fall short of recruiting enough patients. This has a ripple effect right back on stalling the development process of drugs, delaying lifesaving treatments from hitting the market. Companies can use patient data to identify unmet needs that might inform their product development strategies. Moreover, engagement and retention can be significantly improved by tailoring communication using data analytics to improve quality and reduce the cost of care. By applying data-driven methodologies to the process of identification, an organization can expedite the recruitment process for clinical trials—ensuring that studies start on time, going a long way to aid in furthering the current status of medical science.

  • Keeping Pace with Technological Advancements

Looking ahead, the greatest challenge facing the life sciences sector is to keep pace with technological progress. At the same time, it is now having to harness the burgeoning promise of digital solutions, which have only just begun transforming this sector. While the digital and analytics (DnA) leaders in these companies estimate their digital initiatives to have firmed up bottom-line performance modestly by 5 to 15% over the past five years—translating into a compelling global impact of $6 billion to $9 billion—this is just the tip of the iceberg. The true potential for harnessing digital solutions and innovative technologies all the way up the value chain of life sciences is staggering, with estimates pointing toward an economic benefit that could reach $130 billion to $190 billion (McKinsey). This will require organizations, however, to act on data-driven insights regarding the strategic adoption of new technologies that improve operations, identify training needs, and equip workers effectively for these developments.

 

Conclusion

The life sciences sector has a plethora of challenges, from compliance with regulations to talent acquisition. With data and analytics, though, these may be overcome in pursuit of further driving innovation. Data-driven insight empowers increased efficiency in R&D, execution of clinical trials, better patient engagement, and strategic decision-making all up and down the value chain.

Unlock the potential of your data to overcome life sciences challenges. Leverage the expertise of Trinus to deliver comprehensive data management—spanning data integration, data warehousing, data architecture & modeling, data governance, and more. Our team will evaluate your current data environment against the stated business objectives and work out customized solutions for actionable data-driven insight generation. Let us help you steer through the intricacies of life sciences and realize improved business outcomes. Get in touch with Trinus to embark on your journey of making data-driven decisions for success in the life sciences.