The UK-based Industrial Biotech firm faced significant hurdles due to a fragmented data landscape, which hampered their R&D productivity.

Challenges

• Data Fragmentation: R&D data was dispersed across multiple systems, leading to inconsistencies in quality and delays in data retrieval.
• Manual Data Preparation: Scientists were forced to spend excessive time manually preparing data for analysis, which reduced the time available for core scientific experimentation and innovation.

Solutions

The company implemented Scispot OS to centralize and streamline its data management:

• Automated Data Standardization: The platform implemented processes that automatically converted various file formats (such as CSV, BAM, FASTA, VCF, and TIFF) into consistent, analyzable datasets.
• Streamlined Workflows: By unifying data, the system allowed scientists to focus on critical research tasks rather than manual data management.

Results

• Improved Data Quality: The company achieved 95% accuracy in R&D data aggregation, ensuring consistent formats ready for analysis.
• Increased Productivity: Productivity rose by 70%, as scientists significantly reduced time spent searching for data, allowing for more focus on experimentation.
• Faster Time to Insight: Experiment-to-insight time was reduced by 60%, powered by Scispot's AI Lab Assistant, which enabled quicker decision-making and faster product development.

‍