Given rising competition, higher customer expectations, and growing regulatory challenges, these investments are crucial. But to maximise their value, leaders must rigorously consider find out how to balance the important thing aspects of scope, scale, speed, and human-AI collaboration.
The early promise of connecting data
The common refrain from data leaders across all industries—but specifically from those inside data-rich life sciences organizations—is “I actually have vast amounts of knowledge throughout my organization, however the individuals who need it might’t find it.” says Dan Sheeran, general manager of health care and life sciences for AWS. And in a fancy healthcare ecosystem, data can come from multiple sources including hospitals, pharmacies, insurers, and patients.
“Addressing this challenge,” says Sheeran, “means applying metadata to all existing data after which creating tools to search out it, mimicking the benefit of a search engine. Until generative AI got here along, though, creating that metadata was extremely time consuming.”
ZS’s global head of the digital and technology practice, Mahmood Majeed notes that his teams frequently work on connected data programs, because “connecting data to enable connected decisions across the enterprise gives you the flexibility to create differentiated experiences.”
Majeed points to Sanofi’s well-publicized example of connecting data with its analytics app, plai, which streamlines research and automates time-consuming data tasks. With this investment, Sanofi reports reducing research processes from weeks to hours and the potential to enhance goal identification in therapeutic areas like immunology, oncology, or neurology by 20% to 30%.
Achieving the payoff of personalization
Connected data also allows firms to give attention to personalized last-mile experiences. This involves tailoring interactions with healthcare providers and understanding patients’ individual motivations, needs, and behaviors.
Early efforts around personalization have relied on “next best motion” or “next best engagement” models to do that. These traditional machine learning (ML) models suggest probably the most appropriate information for field teams to share with healthcare providers, based on predetermined guidelines.
Compared with generative AI models, more traditional machine learning models could be inflexible, unable to adapt to individual provider needs, and so they often struggle to attach with other data sources that might provide meaningful context. Subsequently, the insights could be helpful but limited.
