Click to learn more about author Updesh Dosanjh.
Artificial intelligence (AI) and machine learning (ML) are present in nearly every industry today. However, not all industries have readily embraced these technologies with open arms. Due to the nature of the business, the life sciences industry has historically been careful about its adoption of new technologies. This is understandable because the number one priority is to ensure that treatments are safe for patients and adhere to complex regulatory requirements around the world, as any missteps or errors can have costly consequences.
The sector has started embracing these technologies to realize transformational benefits. These include being able to gather and analyze more complex and holistic health data, improved regulatory information management, and greater operational agility and collaboration across departments and business functions. As a result, life sciences companies can make more accurate clinical decisions, improve patient safety, and enhance their ability to meet challenging regulatory guidelines required to bring treatments to market. However, successful digital transformation must be much more than simply purchasing a new technology. It also requires the right talent, vision, and organizational culture change to make the promised benefits a reality.
A Commitment to Evolution
More companies are committing to adopting new technological advances, and such foundational systemic changes require significant investment, resources, and time. To remain competitive, life sciences companies must transform their technology teams and explore best practices for new technology implementation and training, as the evolution and success of their commercial strategies demands complete compliance, as well as optimization and innovation.
Historically, companies have had cross-organizational IT teams to manage infrastructure. However, departmental experts tended to oversee their teams’ largely manual data entry processes for activities such as regulatory information management (RIM) and drug and product safety. This siloed business model has resulted in information and process gaps within organizations, which can stymie innovation, increase time to market, and hinder monitoring of patient outcomes for critical safety signals.
Today, companies are rethinking their approach to structuring their IT teams to integrate automation-enabling solutions for improved processes and to leverage advanced analytics. As more technologies have emerged to automate those processes, support for these more advanced functions is needed. A clear strategic vision will be needed by digital leaders for optimizing these digitalized processes to best capture and operationalize the integration of data-informed insights into future efforts.
Emerging Digital Leadership Must Attract the Right Talent
The move toward processes where information and data flow across functional and organizational boundaries seamlessly requires a shift in skills needs, allied to a clear top-down vision for how to effectively restructure teams/functions and foster change management to get there.
The best companies are addressing recruitment obstacles through dynamic leadership driven by visionary leaders who get it. There is now a clear data-led career path for experts doing work that makes a difference. The power of using Data Science to improve people’s quality of life has intrinsic value and should be communicated.
The ascendence of new technologies means that data analysis can be enhanced and sped up, but this needs to have cross-organization integration, versus vertically within individual departments. Traditional roles have evolved, and new ones will need to be created to take advantage of these capabilities. For example, more chief digital officer roles are emerging, some focused more on digital engagement and others on data technology. More roles such as Head of Data Analytics and Head of Data Science roles are emerging in the life sciences space, in addition to the more traditional Data Management functions.
Some companies are recruiting professionals with experience outside of their space into their IT divisions with the remit of doing what they did for other industries. Companies are then teaching them about life sciences while running IT from a fresher, nontraditional perspective. All of this means that companies are transitioning to becoming technology and data companies and are now competing for Data Science talent.
Looking Ahead
As AI and machine learning become embedded in life sciences over the next decade, they will become more commoditized. What is needed from the industry is a continuing commitment to holistic organizational change.
Understanding the masses of big data has led to the emergence of the biotechnologist as a specialty. Analyzing and actioning the data generated from clinical trials has also led to the role of the biostatistician. AI will similarly open the door for new specialties to emerge, as it increases the need for Data Science and analyst roles in life sciences.
Significant innovation is happening in the Data Management, cloud platforms, and AI spaces. Buyers in the life sciences market will have to wholeheartedly embrace and understand these technologies to realize their value while maintaining security, safety, and regulatory demands.
Ultimately, the industry is starting to reinvent the underlying infrastructure, creating Data Science teams, and leading transformational changes through leadership that uses Data Modeling and automation for decision making. Those who spearhead this data revolution for the industry will have a competitive advantage to their innovation in the long term.