Helpful articles
Perspective on data, analytics, and AI in life sciences, from the team that builds it.
How MCP Servers and GitHub Copilot Are Automating Power BI in Pharma
At Microsoft Ignite 2025, Power BI gained Model Context Protocol servers that let GitHub Copilot build and query semantic models with natural language. Fo…
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From Dashboards to Decisions: Agentic Analytics Copilots for Commercial Teams
Dashboards tell you what happened. Commercial leaders need to know why, and what to do next. Agentic analytics copilots, maturing fast across pharma in 20…
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Building Production RAG for Medical Information: Accuracy, Citations, and MLR Review
Retrieval-augmented generation can turn a medical information team's document library into accurate, cited answers. But moving from a promising demo to a…
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Governing AI Agents in Regulated Life Sciences
AI agents are moving into GxP-regulated workflows across pharma. The 2025-2026 wave of guidance, from FDA computer software assurance to a revised EU Anne…
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Making Your Data Warehouse AI-Ready for Agents
AI agents fail in production far more often because of the data beneath them than the model inside them. Making a warehouse AI-ready is less about new tec…
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Real-World Evidence at Scale: OMOP, External Control Arms, and Natural-Language Querying
The OHDSI network now spans hundreds of data sources and close to a billion patient records mapped to a common model. Pair that standardization with exter…
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LLMOps for Pharma: Evals, Guardrails, and Drift Monitoring
Getting a pharma LLM application to a convincing demo is easy. Keeping it accurate, safe, and auditable in production, as models update and data shifts un…
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How Pharma Companies Can Align In-Network Doctors with Preferred Formulary Drugs for Better Market Access
In the competitive pharmaceutical landscape, securing a favorable spot on a payer’s formulary is only half the battle. Even when a drug is listed as prefe…
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The Power of Market Access Analytics in Pharmaceutical Rebates and Reimbursements
The pharmaceutical industry faces significant challenges in market access, particularly when it comes to pricing, rebates, and reimbursements. With rising…
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Assessing System & Data Readiness for Deep Learning in Advanced Analytics
AI-driven analytics is evolving fast, and businesses that want to stay ahead are shifting from traditional machine learning to deep learning for more adva…
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How AI is Supercharging Predictive & Prescriptive Analytics in Life Sciences
Predictive and prescriptive analytics have been around for years. Life sciences companies have long used data to forecast trends, optimize sales strategie…
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Natural Language Querying to Democratize Data in Life Sciences
If you work in life sciences, you know the struggle of getting the right data when you need it. Whether you’re running clinical trials, tracking sales per…
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Why Data Governance and Quality Are the Cornerstones of Omnichannel Success in Life Sciences
In our previous article, Why Data is the Backbone of Omnichannel Success, we talked about how data powers every aspect of an effective omnichannel strateg…
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How Life Sciences Companies Can Build a Data-Driven Omnichannel Marketing Strategy
Life-sciences organizations use omnichannel marketing to reach healthcare professionals (HCPs) and patients across email, webinars, field teams, social me…
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