Key Considerations When Implementing a Next-Gen Insights Platform for Life Sciences
For decades, life science companies have contended with the challenges of an increasing scale and variety of data while seeking ways to use insights platforms to make information available to decision makers.
Ravinder Singh, VP of Healthcare Technology Consulting CitiusTech, pointed out in WhizAI’s webinar (powered by PMSA), Key Technology Considerations for the Next Generation Insights Platform, that now the industry is facing a new challenge. “How do you start making the shift from reports to dashboards to a self-service model? That’s the biggest shift we are seeing from a life sciences commercial standpoint,” Singh said.
He explained that making insights platforms user-centric requires next-gen technology but also considering people and processes. “It’s like building a car. I need it to perform, but the real challenge comes from making it reliable and knowing when I go out on the road, I can reach my destination. The same holds true for a machine learning (ML) algorithm.,” he said, “Can you create it in a manner that really runs and solves end users’ challenges?”
How to Meet Life Science Analytics Challenges
Chris Ilacqua, Director of Product Management for WhizAI, said innovators are using emerging technology to create next-gen life science insights platforms. He said, “The velocity, volume, and variety of data requires a lot of scalability. Cloud and technologies like Kubernetes allow for scalability while keeping costs down.”
Additionally, he explained that natural language processing (NLP) is key to self-service analytics, enabling platforms to understand users – including context and intent – when they ask a question. However, it’s also important to train the ML model for the life sciences domain.
“ML models aren’t as good when you train them generally, but they’re excellent when you give them specifics around an industry,” he commented. “Domain specificity is critical to success, not only of getting an answer but also rollout of the platform. If it doesn’t understand your question, it just becomes spam to the end user, and they’ll stop using it.”
Also, because many life science consumers’ expertise may not extend to IT or coding, the platform should enable them to collect insights and share them throughout their organizations in a no-code environment.
Tips for Managing the Transition to a Next-Gen Insights Platform for Life Sciences
Ilacqua pointed out that decreasing analytics friction for life science analytics consumers has numerous benefits, including enabling data-driven decision making across an organization. However, it will also require people to assume new responsibilities to ensure data privacy and security and support compliance. “There will be fewer guardians of data,” he said. “New processes will need a gatekeeper, one who understands the business as well as insights.”
Singh added that life science enterprises also need to consider how to continue seeing value from existing investments in analytics while enabling self-service. He said they need to answer the question of how much to analyze using dashboards and how much to push to the edge. He said one approach is to leverage the existing investment and use next-gen insights platforms on top of existing data repositories to make analytics conversational and consumable.
Both agreed that the agility that self-service life science analytics provides is key to competitiveness in a dynamic industry. Business users throughout a life sciences organization can access insights in an instant, enabling them to make data-driven decision making routine.
While change takes planning, effort, and building buy-in, implementing a next-generation insights platform will result in enhanced performance and better outcomes.
“Start small, get wins, and see how it impacts your organization,” Ilacqua said.
For more insights, watch Key Technology Considerations for the Next Generation Insights Platform on demand.
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