BioIT 2023: Highlights of Challenges and Approaches to Solutions on a Digital Transformation Journey

Many companies find themselves with a plethora of inefficient and cost-accumulating problems that require digital transformation solutions, unfortunately, many challenges prevent companies from going through the digital transformation process. While applicable to all areas of research, these challenges are especially difficult to overcome for Discovery science research, that has a tradition of paper notebooks and requires flexibility in experimental protocols. Often it is the middle-management that is initiating digital transformation journeys¹, as they are the ones aware of the company’s big-picture goals, as well as everyday challenges of the scientists. Consequently, they are also the ones that have to initiate change in both, upper management mindsets and scientists’ workflows. Here are some of the challenges and approaches to solving the issues that were discussed during the honest talks shared during BioIT conference by the pharmaceutical giants.

Challenge 1: Creating a digital environment that meets conflicting and competing requirements of user-centricity and data connectivity and accessibility. In other words, what is convenient to the users’ vs company’s vision and data “FAIRification”.

Approaches to solution:

• Sometimes a simple awareness of both sides and their priorities and challenges is the key. Finding balance by designing a platform that can address the needs of both is essential¹. It is very important to have both sides’ positions prioritized during different stages of implementation, especially during full adoption.
• Fostering an environment of FAIR data mindset². Changing people’s mindsets is the hardest part of every digital transformation journey. Company as a whole must adopt the principles of what FAIR data entails^3,4.
• Showing commitment:
• Commitment of the entire line of management; especially upper leadership has to be on-board⁵. Digitalization projects could last several years, and timeline transparency when agreeing upon commitment is essential.
• Providing the right staffing with digital transformation experts, developers and data architects to build the necessary platform framework⁴. Having the manpower that provides a strong foundation and understanding of the big picture and the scientific method5.
• Adoption of successful operating model from external partners and success stories⁵. Successful change does not happen in silos, communication on successes and failures can lead the path to necessary learning.
• Approach in stages. Assessment of both, users’ needs and company’s vision is necessary for stages to progress. Stages such as opportunistic digitalization, followed by systematic digitalization can be helpful¹. During Opportunistic digitalization stage, change is taking place as point solutions, by the scientists who are aware of benefits of digitalization and are eager to try new approaches. By building on the success stories from this first opportunistic stage and digitalized pockets, the second stage of Systematic digitalization can take place, during which the whole department(s) gets onboarded systematically. Only in a fully digitalized lab, the third and last stage, Transformative digitalization is possible, such as smart lab design and in silico methods¹.

Challenge 2: Scientists’ resistance to participate in digital transformation pilots.

Approaches to solution:

• Awareness of time and effort commitment from the scientists: work done for the digital transformation process is in competition with doing actual science, therefore helping to find a good balance is essential¹.
• Communication, communication, communication! Only through scientists’ understanding of why change is necessary, will their participation be possible⁴. On one digital transformation journey, communication with the scientists before the project could initiate took 80% of the project’s implementation time⁵.
• Communication with the scientists must be done in their language. Terms like metadata, semantic integrations and knowledge graphs are not usually part of their vocabulary and can have a negative impact. Instead, approaching the conversation using terms like experimental design, results analysis and data sharing, will promote their understanding and interest³.
• Allowing scientists to shape the digital world and have input in the digitalization decision process, so they can directly observe the potential benefits6. Designing a smart governance model will motivate scientists to participate; such as recruiting a project champion, who is knowledgeable and enthusiastic about the project and can influence others’ opinions. Change happens in stages. It is often during the design of the new workflows that the inefficiencies and redundancies of the old workflows become apparent. This also allows scientists to communicate and learn about gaps and pain points in each other’s workflows. Aligning and standardization can make workflows easier, but flexibility should be retained⁶. A governance model that includes scientists’ participation, input and feedback will make stages progression faster and motivate increased participation.

Challenge 3: Scientists resistance to adopt new technologies.

Approaches to solution:

• Sometimes a simple reminder is helpful that research is a continuously evolving and agile environment, and progress in research is inevitably linked to new discoveries that rely on new technologies.
• Adoption of continuous feedback model of communication. Communication and careful feedback gathering from the scientists is essential⁴. Understanding and awareness of the efficiencies gained along the way ensure sustainability of the journey. Focusing on value creation motivates progress^1,3,5.
• Structuring digitalized environment in a way that still enables a familiar workflow to the scientists, such as record of introduction (background, hypotheses), methods and results. The ELNs that allow scientists to record scientific process and still follow FAIR guidelines during the data gathering stage are more likely to be embraced³.
• Shaping the digitalization project in a way that delivers benefits to the business early, and then sharing and celebrating progress, even if it is subtle. It’s very important to identify and share the efficiency gains, improved data integrity and time savings that resulted from the adoption of the new workflow or technology⁵. Even small improvements can lead to significant time saving in the workflow and can motivate further participation and enthusiasm.
• Giving extra time for scientists to get accustomed to the new workflow and learn about digital transformation potential. Patience here is the key – transformation takes time⁵. “The path toward the digital lab is a marathon, not a sprint – strategic planning beyond project timelines is essential”¹.

Challenge 4: Data management and harmonization.

Approaches to solution:

• Careful and thorough analysis of the current state of data management. Understanding of the current gaps and unmet needs of data users and data generators is essential to build a better process of data management. Often the understanding of operational gaps is underestimated and must be addressed prior to digital transformation journey⁶.
• Deep understanding of the design of the proposed data architecture is essential: data capture, data movement and data consumption⁷. FAIRification of experimental data should be the golden standard^3,4.
• Awareness that harmonization of workflows and data analysis requires compromise, but in return brings gains in efficiencies in time and effort. Ample discussions and thorough understanding of the future gains are necessary for scientists to adopt a common way and to harmonize⁵.
• Scaling equipment connectivity requires manpower. Staffing with sufficient amount of skilled technology developers and business analysists is required for building a platform to sustain the needs of pharmaceutical giants^4,5.

Challenge 5: Leadership buy-in.

Approaches to solution:

• Learning the proper communication style with company's leadership is the key. Technical communication won’t be helpful; instead, it has to be accessible and engaging to a wide audience.
• Emphasizing that data quality is the key to future successes. Tailoring explanation of data quality to the appropriate SMEs, scientific vs technical⁵.
• Explanations of the proposals need to involve a cost-benefit analysis: up front and accumulating cost over time; as well as immediate and future benefits⁵. It needs to be as tangible as possible⁴. Digital transformation successes end up benefiting everyone – from scientists and managers to CEOs and investors, and especially to patients, that receive better and faster care⁸.

References:

1. TRACK 11: AUTOMATION, DIGITAL LAB, AND ROBOTICS. 2:40 pm, Moving towards the Fully Digital Lab in Pharma Research – A Story of Tenacity and Ingenuity Angelika Fuchs, Chapter Lead Data Products and Platforms, pRED Data & Analytics, Roche Diagnostics GmbH.

2. TRACK 3: DATA SCIENCE AND ANALYTICS TECHNOLOGIES. 4:25 pm, FAIR Data Platforms need FAIR PIPELINES Dave Clifford, Head of Technology and AI, BDH Data, Biogen

3. TRACK 10: DIGITAL BIOPHARMA. 11:25 am, A Comprehensive Platform for Innovation with Data for innovation with data – the real challenge! (Alternate title: When fair becomes unfair, and how are we attempting to make it fair). Ajay Shah, PhD, MBA, Executive Director & Head of IT, Cell Therapy TRC, Early Clinical Development and CP&P, Bristol Myers Squibb Co.

4. TRACK 9: PHARMACEUTICAL R&D INFORMATICS. 11:25 am, Development Principles for Biotech Data Teams: Aligning Projects with Organizational Strategy Jesse Johnson, PhD, formerly Head, Data Science and Data Engineering, Dewpoint Therapeutics.

5. TRACK 11: AUTOMATION, DIGITAL LAB, AND ROBOTICS. 10:55 am, Digital Transformation Journey for a Faster and Data-Driven Pharma Product Development Christian Airiau, PhD, Global Head, Data Sciences Biologics Development, Sanofi Mark Schatz, PhD, Global CMC Digital Transformation Leader, MSAT GenMed, Sanofi.

6. TRACK 9: PHARMACEUTICAL R&D INFORMATICS. 10:55 am, Digital Transformation in R&D Resulting in a Fundamental Change: An Effective Rollout of New Tools That Impact a Whole Research Organization. Monika Bug, Leader, pRED Data & Analytics, Roche

7. TRACK 11: AUTOMATION, DIGITAL LAB, AND ROBOTICS. 11:55 am, The Journey of Data in a Lab - Challenges and Solutions Aishwarya Balajee, Head of Digital Services, North America, Zifo Technologies, Inc

8. TRACK 2: DATA MANAGEMENT. 3:40 pm, AbbVie R&D Convergence Hub (ARCH). Brian Martin, Head of AI, R&D Information Research, Research Fellow, AbbVie, Inc

Disclosure Statement:

Written by: Jenya Kolpakova, PhD Senior BSA, Zifo RnD Solutions Zifo is a scientific informatics services provider. We partner with biopharma customers to help with every stage of their digital journey.

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