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    Health & Life Sciences: A Data Science Innovation Hub

    December 8, 2022   4 min read

    Innovation is the magic word for improvement: a new idea, a different process, a methodology change that can mean all the difference in business value. If you are a data science leader or practitioner either working in or seeking information about healthcare and pharmaceutical industries, Domino created an eBook of new ideas by five peer innovators in this domain. Tailored just for you!

    The new Health and Life Sciences edition of the Data Science Innovator’s Playbook is available now as a free download. Each of its top data science innovation leaders in healthcare, life sciences, and medicine share insights on their work, careers, and the data science profession. 

    Examples of What You’ll Learn in the eBook

    As a teaser, here are two specific insights from advisors for this eBook project. The first is by Sanjay Jaiswal, Managing Director and Lead of R&D Analytics for Accenture in North America. The second is by Murali Gandhirajan, Healthcare Field Chief Technology Officer at Snowflake. 

    Cost Reduction: Meeting the Biggest Innovation Challenge for Pharma

    Overcoming the hurdle of enormous, rising costs of developing new products and treatments – and getting them approved for use – continues to plague the industry. Typically, this ranges from US$2.6B to US$6.7B, which includes the cost of capital and cost of failure depending on use case specifics. “This enormous cost must come down exponentially, from billions to millions,” says Jaiswal.

    Data science has a pivotal role in this transformation, and productivity hinges on the rising ratio of R&D spend per each new treatment approved,” says Jaiswal. One organizational model for improving productivity is an “agile lab,” according to Jaiswal. Functional teams working in four-to-six-week sprints, coupled with acceleration capabilities of technology such as the Domino Enterprise MLOps Platform, are unique enablers of improvement. 

    “Fast access to data and the ability to very quickly spin up an environment with the necessary prerequisites and prioritization parameters can accelerate speed to value,” he says. “If you want to do machine learning at scale, you need to monitor your models continuously, at scale.” This is the role of Enterprise MLOps.

    Getting Enough Data to Properly Train Models

    This old problem can be swiftly solved with faster access to data, which exists within an organization but may be scattered around the world – often burrowed within silos unseen by needy collaborators. “It’s critical for researchers to get their hands on all of the relevant data in a governed way and be able to use it meaningfully, says Gandhirajan. He notes these barriers include regulations that limit external and/or international sharing. He suggests using an approach to meet this challenge called “federated learning.” Want to know more? The eBook’s profile on NVIDIA’s Mona Flores explains how!

    These advisors also noted the importance for all models to be reviewed for explainability. This makes it easier for regulators and stakeholders to understand the benefits of a model. Such benefits are provided with the help of Enterprise MLOps.

    Meet More Innovation Leaders

    The eBook includes insights from innovators, advisors, and industry experts at the top of their game in data science. All describe tremendous growth of their data science departments in terms of team size and centrality in answering critical questions for their businesses. The profiled leaders include:

    1. Luca Foschini, Ph.D. – Co-founder and consulting Chief Data Scientist at Evidation 

    2. Najat Khan, Ph.D. – Chief Data Science Officer and Global Head, Strategy & Operations for Research & Development at the Janssen Pharmaceutical Companies of Johnson & Johnson

    3. Andy Nicholls, MSc – Senior Director, Head of Statistical Data Sciences, GSK

    4. Mona G. Flores, MD – Global Head of Medical AI at NVIDIA

    5. John K. Thompson – Analytics Thought Leader, Best-selling Author, Innovator in Data & Analytics and Building High-Performance Teams 

    Conclusion

    These simple, yet innovative ideas are available for free to those who download the eBook and feast on other insights shared by our featured industry experts. There’s an old saying that 85% of hard problems are solved by subject matter experts with vast experience, and about 15% are handled with appropriate tools. You may have the expertise but scaling data science also requires the guided use of an Enterprise MLOps platform to get your team and organization over the finish line. We invite you to read the new Health and Life Sciences edition of the Data Science Innovator’s Playbook and learn how to join the innovator’s circle.

    Sid Khare

    Sid is the Head of Partnership at Domino Data Lab. He is an AI/ML ecosystem developer with over 20 years of global experience. He has a strong track record of closing new partnership deals, co-innovation of solutions, and driving mutual partnership value.

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