Giving Biotech Organizations a ‘Digital Brain’ with Scispot

This week on Data in Biotech, we’re delighted to be joined by Guru and Satya Singh, co-founders of SciSpot, a company focused on transforming biotech companies through smarter embodiment of biological processes in data/software and acceleration of the R&D process. They discuss how their respective biotech and data backgrounds led them to develop the platform and their very personal motivation behind their mission to enable data to accelerate life science research. Guru and Satya explore the concept of giving biotech companies a “digital brain” that uses AI to learn from every experiment. They emphasize how this requires modern software principles like being API-first and data-centric. Based on their work helping their biotech customers move towards this model, Guru and Satya discuss overcoming some of the biggest adoption challenges – instilling data competence, moving to standardized data models, and bridging the gap between wet lab scientists and computational experts. Data in Biotech is a fortnightly podcast exploring how companies leverage data innovation in the life sciences. Chapter Markers [1:38] Guru and Satya both give a brief overview of their respective backgrounds and the industry challenges that led them to launch SciSpot. [3:35] Guru discusses the challenges of bringing organic and inorganic intelligence together and introduces the concept of a “digital brain.” [6:50] Ross asks about the components of the SciSpot platform and how it works for companies using it. [9:42] Guru and Satya emphasize the challenge of educating scientists on the advantages of adopting an API-first, data science-focused system. [12:09] Guru and Satya highlight the ‘a-ha’ moments for customers using the platform, which include standardizing data models and connecting all instruments into SciSpot. [14:27] Satya discusses knowledge graphs and how the system enables both implicit tagging and human input to enrich the data for data science purposes. [17:52] The discussion covers the need for flexible workflows in biotech and how SciSpot changes the way its customers think about data science workflows. [22:44] Guru shares his views on the future of biotech companies and underlines the importance of standardized data models.   [25:59] The discussion covers the challenges of integrating biotech-specific systems into an API-first platform and the current gaps in data capabilities. [29:45] Ross highlights the importance of a unified platform for the range of biotech personas to drive AI faster. [31:32] Guru and Satya revisit their vision of biotech organizations with a “digital brain” and real-time, established feedback loops that will make them smarter. [34:46] Guru and Satya share advice for biotech organizations, focusing on how they should think about data and tooling. Download our latest white paper on “Using Machine Learning to Implement Mid-Manufacture Quality Control in the Biotech Sector.” Visit this link: https://connect.corrdyn.com/biotech-ml

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