Webinar On-Demand
Gen AI capabilities are being explored across different functional areas, from Research & Development (Derisking Drug Discovery) to Clinical Trials (Accelerated time to market) to Commercials (reinventing HCP engagement). A McKinsey survey revealed that 79% of LS organizations plan to build their own gen AI solutions tailored to their unique requirements.
AI appears poised to address many of these challenges, yet many life science and pharma organizations need help in utilizing this technology effectively. Our perspective: AI is only as good as your trust in the data that it uses. Data trust is still an ongoing part of AI development and requires a framework built on people, processes, and technology.
That’s why Wipro and Alation teamed up with DQ Labs to share our Data Trust for AI solution and architecture built specifically with the unique situations of life science and pharmaceutical companies in mind.
You’ll learn:
How Data Trust is essential to solving current challenges
The need to involve people and technology in the implementation of trusted AI solutions
Where Gen AI capabilities are being explored across different functional areas and how this increases focus on data privacy and data access controls
Watch to find your way to trusted AI solutions.
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Are you maximizing the value of your data in legacy systems, or is it time to modernize with cloud migration and tap into new opportunities?
Alation Brief
Join us to explore how Alation Anywhere brings the power of data intelligence directly to your fingertips - right in your browser. Our integrations now extend beyond Excel, Teams, Slack, Tableau, and Google Sheets to include Chrome. With the Alation Anywhere Chrome Extension, you can search, preview, and trust critical data without leaving Chrome, enabling faster, more informed decisions across your organization and seamlessly fitting into your workflow.
Webinar On-Demand
In today’s rapidly evolving digital landscape, effective data governance is critical for organizations striving to manage diverse and numerous data types. Ensuring the trustworthiness of AI/ML models requires robust governance of both inputs and outputs.