Preparing for a Scalable Data Science Platform to Accelerate Target Discovery
Clarkston Consulting recently helped a client’s R&D organization develop a scalable data science platform. Read a synopsis of the project below or download the full case study.
Download the Scalable Data Science Platform Case Study Here
The client partnered with Clarkston Consulting to assess the research & development organization as it is transforming its genomics and computational biological capabilities. As part of this effort, the R&D team sought to unify massive amounts of multi-modal data to identify novel targets and use in AI-enabled drug discovery.
The Clarkston team interviewed key stakeholders, including genetic and computational biology research scientists and directors. By discussing research data, platforms, and methods, the team identified gaps and pain points impacting the ability to efficiently use available research data. Through close collaboration with the team, we surfaced over 25 unique challenges to the organization, centered around data silos and inaccessibility, limited cross-therapeutic area collaboration, and inconsistent data and process governance.
Clarkston facilitated a workshop with the stakeholders across therapeutic areas to discuss and align on the challenges and develop a vision for a future state of exploratory research. Through discussion of the organizational challenges, it became apparent the client’s pain points related to knowledge management, stakeholder buy-in, and data accessibility. Additionally, the team developed a vision for the novel tool that incorporated organizational gaps, leading to a more effective research methodology.
Based on the organizational challenges, the Clarkston team identified the need for specific functional and technical working groups, including governance, data strategy, data ingestion, infrastructure, tools, and enablers. Following the workshop, Clarkston guided the working groups to prioritize challenges, form objectives, and define ways of working for each team. Using this methodology, the teams developed an implementation plan, including platform selection, analytical method prioritization, and data acquisition.
