The life sciences sector has undergone significant evolution in recent years, driven by advances in technology and an increased focus on personalized medicine. This has led to the development of innovative research tactics, new therapies, diagnostic tools, and medical approaches that are more targeted and more effective than ever before. Overall, the life sciences sector is becoming increasingly innovative and patient-centric, with the potential to significantly improve health outcomes for people around the world.
For more than a decade, ONTOFORCE has been supporting life sciences organizations get the most out of their data. We’ve witnessed how the industry has transformed over the years as we’ve assisted our customers in breaking down data silos to harmonize their data into one easy-to-use platform. This data harmonization has fueled research breakthroughs, supported data-driven decision making, improved clinical outcomes, and sped up drug discovery timelines.
As a partner to life sciences companies, ONTOFORCE is vested in the growth and evolution of the industry and its players. We know that 2023 will be another year full of complexity for many in the life sciences space amidst a shifting economic backdrop. As the winds continue to change, we’re keeping our eye on a few key trends that we think will greatly impact the year the industry has and its future. Read on to hear more.
Digital transformation has been a phrase on the industry’s lips over the past few years now. The trend’s long shelf life perhaps speaks to the struggle many pharmaceutical and biotech companies are facing when it comes to developing and adhering to a digital transformation strategy, as we know the biopharma industry has been historically slow to adopt new digital practices. So, while the trend isn’t exactly new for the year, it will maintain its position on the industry’s agenda throughout 2023, as many biopharma companies look to ramp up investment in their digital transformation.
With many biopharma companies looking to enable truly data-driven decision making, ramp up on digital transformation will be especially crucial this year as securing digitalization on an organizational level is key for optimal data governance and management. With a strong data governance and management structure in process, data-driven decision making is facilitated more efficiently, with greater accuracy, and at scale.
The data mesh approach, for example, is being increasingly adopted throughout the industry to harness the power of data and to fuel data-centricity, including data-driven decision making. However, the approach requires that the right digital infrastructure is in place. The outcomes a data mesh approach far outweigh the effort needed to implement the architecture:
“We are making sure that the data products owned and maintained by individuals and teams can be used by the entire company. We have found that this leads to faster results with more scalability,” said Angeli Möller, Head of Data and Integrations Generating Insights at Roche. Hear more about the data mesh approach, along with other interesting insights about driving data-centricity in life sciences by watching this panel discussion featuring three other colleagues from the industry:
THE DANGERS OF NOT ADOPTING DIGITALIZATION
Despite the major returns that digital transformation promises for an organization, such as enabling data-driven decision making, improving workflows, driving efficiencies, cutting costs, and facilitating better patient outcomes, getting the necessary means in place to secure said transformation can be difficult due to an organization’s historic practices or hesitancy to adopt change.
It’s becoming clear that organizations either opting not to embark on a digital transformation journey or who are struggling to fully adopt digital processes along their journey, may risk developing data integrity issues. Such issues, including impaired data governance and the creation of data silos, will ultimately spur further issues and challenges that will be harder to overcome as the industry advances. Taking on these data integrity issues due to inadequate digitalization processes also challenges an organization’s maintenance of the FAIR data principles. The FAIR (findable, accessible, interoperable, reusable) data principles are a set of guidelines that have been adopted by researchers worldwide since their inception in 2016. They enable organizations to properly store and manage data so it can easily be accessed and reused when needed. For life sciences companies specifically, upholding the FAIR principles boosts research efficiency and ultimately supports business growth.
To sum this trend up, adopting digitalization processes can help organizations combat data integrity issues while at the same time ensuring their data can be properly leveraged across business units, driving efficiencies and synergies. On a more general scale, digitalization can help companies better meet the evolving needs of patients and stay ahead of the competition in a rapidly changing industry.
Life sciences companies are placing an increased focus on their research and development (R&D) departments to bring new, innovative treatments to market to drive better health and patient outcomes. Companies are realizing that investing heavily in R&D is essential in order to stay competitive and meet the changing needs of the world and patients. For example, with the growing focus on personalized medicine, R&D departments are working to develop targeted therapies that are tailored to the specific genetic makeup of individual patients. This personalized approach can lead to more effective and safer treatments, and is becoming increasingly important as the healthcare industry evolves.
However, resourcing for developing personalized medicine or other innovative treatments can be costly. Companies need to consider new R&D approaches that can be implemented efficiently and when possible, scale across the business so they can be maximally leveraged from multiple angles. In refining their R&D practices, life sciences companies are turning to innovative technologies and tools to support their goals and evolutions. Ideally, these technologies and tools should easily plug into existing workflows without having to implement new enterprise solutions.
Data scientists are becoming increasingly important for companies across all sectors, as more and more organizations shift towards data-driven decision making. In the life science sector, there are vast amounts of data generated by high-throughput technologies, electronic lab notebooks, electronic health records, clinical trials, and many other sources. Data scientists are needed to analyze and interpret these large amounts of data, and to help extract insights and knowledge from it.
In the pharmaceutical industry specifically, data scientists can use data analytics to identify trends and patterns in drug usage and effectiveness, and use that information to inform the development of new drugs and treatment strategies. Additionally, data scientists can analyze clinical trial data to help predict outcomes, which can help companies make informed decisions about how to best set up a clinical trial for success. Overall, the use of data science can help pharmaceutical companies make more informed and data-driven decisions, so it’s not surprising then that data scientists will become further indispensable for the industry.
As data scientists become more and more vital across all areas of the value chain, it will be crucial for companies to guarantee that these in-demand profiles can manage their growing workloads efficiently. Establishing strong data infrastructures with optimal management and exploration processes will be key to not only ensuring data scientists can tackle their work with efficiency, it may also enable citizen data science for other roles as to help ease the burden placed on data scientists.
Real-world data (RWD) is data collected from patients in their daily lives, rather than in a controlled clinical setting. In the life sciences industry, RWD can provide a deeper understanding of how drugs are being used and how they are impacting patient outcomes. This can be particularly valuable for evaluating the long-term effectiveness and safety of drugs, as well as identifying any rare side effects that may not have been detected in clinical trials.
Additionally, RWD can also be used to inform drug development by helping companies better understand the unmet needs of patients and identifying potential areas for innovation. On top of this, RWD can be used to inform the design of future clinical trials, helping to pinpoint potential biases or confounding factors. Overall, the use of RWD can help life sciences companies to make more informed and data-driven decisions across the value chain, leading to the development of more effective and innovative treatments.
Various sources contribute to generating RWD, such as electronic health records, patient-generated data, product and disease registries, scientific literature, social media, and adverse event reports. As companies continue to prioritize RWD and as their usage of it expands in 2023, the need for singular and careful data stewardship will be paramount in order to capitalize on the full value of this type of data.
The use of artificial intelligence (AI) and machine learning to speed up the drug discovery and development process enables companies, among many other things, to identify new drug candidates and conduct clinical trials more efficiently, potentially reducing the time and cost of bringing new treatments to market.
While AI could of course be considered a trend itself within the industry, many companies are not yet close to realizing AI for their businesses because their infrastructure is lacking. An organization needs a strong foundation before it can even dream of implementing AI. As we see many new AI tools and automations take off throughout the coming years, now will be the perfect time for organizations to focus on establishing sound foundations for AI.
A key part of building the foundation for AI is ensuring a strong data infrastructure is in place. This includes data storage, management, and governance systems that can handle the large and complex data sets that are used in AI applications. Additionally, having clean, well-structured, and well-annotated data is needed in order to train and validate AI models, and build AI-enabled processes and automations.
As AI methodologies are being leveraged for various purposes across drug development, the industry has learned that data volume is not sufficient, and that algorithms need to be fed with high-quality data, further enforcing the need to build FAIR data ecosystems.
The past few years have brought massive change for the life sciences industry - we know that 2023 will be no different. We’ll be keeping an eye on our five trend predictions for the industry in 2023: digital transformation, R&D refinements, the growing role of data scientists, real-world data, and foundations for AI.
As it’s been in previous years, companies adopting innovation and evolving their current operations and practices will undoubtedly benefit from improved efficiencies leading to a competitive upper hand. We’re looking forward to seeing how the year unfolds for the industry and what future change the coming months will bring.
DISQOVER, ONTOFORCE's flagship product, was developed specifically for the life sciences sector as a knowledge discovery platform to structure and link any type of data to deliver actionable insights. As such, it supports organizations in their digitalization processes, enables efficient citizen data science, and allows real-world data and real-world evidence to be leveraged for optimized clinical, submission, and surveillance operations.
With over 10,000 users across the globe, DISQOVER and ONTOFORCE’s team of experts are helping life sciences organizations accelerate research and drug development for improved health outcomes. See what’s possible in DISQOVER by trying out our community edition.