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Databricks

Why Talent Transformation Is the Missing Focus of Enterprise AI Public Health Intelligence Shouldn't Require a Data Scientist Mean Time to Detect Is a Data Access Problem First-party audience data is the ad sales relationship now Rethinking Distributed Systems for Serverless Performance and Reliability The AI Scaling Gap Hiding in Digital Native Companies 10 trillion samples a day: Scaling beyond traditional monitoring infra at Databricks AI success starts with clean data, not just better models How nOps Rebuilt Their Cloud Optimization Platform on Databricks Lakebase, and Why Other ISVs Should Too Peril Predicts: Precision Payouts for a Volatile World The foundation of AI scalability: one team, one platform, one operating model The Federal Data Paradox: Rich in Data, Poor in Access Driving Budapest Forward: How BKK Uses Databricks to Transform City Mobility LLM Vs AI: A Practical Guide to Differences, Use Cases, and Tools Model Risk Governance Is Not the Same as Risk Intelligence Generative AI for Business: A Complete Strategy and Implementation Guide Data Science vs Data Engineering: Choosing Analysis or Infrastructure AI Applications: Tools, Use Cases, and Platforms MLOps vs DevOps: A Practical Guide for Data Scientists and IT Teams Top Data Warehouse Tools For Modern Data Analytics Unlocking SAP Business Context in Databricks with Semantic Metadata Delta Sharing The marketing activation gap has a fix: Databricks and Stitch partner to turn data infrastructure into marketing performance Alert Fatigue Is a Business Risk Backstage with Lakebase Shipping Faster isn’t Learning Faster Why Your OEE Dashboard Is Lying to You The Turbine That Tried to Tell You It Was Failing Predicting Readmissions Isn't Enough. Acting in Time Is. Clinical Trials Run Longer Than They Have To. That's a Patient Problem Network Quality Is a Revenue Problem, Not a Technical One Shelf Availability Starts with Better Demand Visibility When Predicting the Next Hit Requires More Than Intuition Approximate Answers, Exact Decisions: New Sketch Functions for Analytics Companies Winning with AI Built the Data Layer First Rethinking SQL ETL for modern data platforms Stripe data now available on Databricks via Databricks Marketplace Databricks and Stripe Projects: Infrastructure Built for Agents Agents are ready but your architecture probably isn't Interoperability Between Unity Catalog and Google BigQuery via Catalog Federation Built In, Not Bolted On: What AI-Native Actually Means in Cybersecurity Operationalizing AI for public sector fraud prevention From months to minutes: Building real-time clinical data pipelines with natural language Agentic Data Engineering with Genie Code and Lakeflow Securely send first-party conversion signals with Snapchat Conversions API on Databricks Marketplace How leading tech companies are killing the builder’s tax with Lakebase Inside one of the first production deployments of Lakebase: LangGuard's agentic workflow governance engine The next generation of Databricks Genie Model Risk Management in 2026: A Banker’s Guide to the Revised Interagency Guidance OpenAI GPT-5.5 now available on Databricks, fully-governed through Unity AI Gateway Operational databases: How they work and when to use them Databricks partners with OpenAI on GPT-5.5 Announcing the Public Preview of Lakeflow Designer Are LLM agents good at join order optimization? How conversational analytics removes the BI bottleneck How to transform document activation workflows with Genie and Agent Bricks Beyond the spreadsheet: how Databricks is delivering the modern CFO in Financial Services AI App Development: Guide To Building AI-Powered Apps IoT in Manufacturing: Strategy, Components, Use Cases, and Challenges Stop Hand-Coding Change Data Capture Pipelines Multimodal Data Integration: Production Architectures for Healthcare AI Personalization Strategies for Media Companies A Modern AI Risk Management Framework Introducing the Databricks Excel Add-in for Business Users Real-Time Decisioning for AI Agents: Why you Need a Customer Context Layer First A Practical Guide to LLM Fine Tuning AI Data Transformation Guide for Data Engineers and Data Scientists Concurrency Control in DBMS: How Locking, MVCC and Optimistic Strategies Keep Data Consistent Bridging data science and marketing: Databricks unveils Delta Sharing integration for Adobe Experience Platform and agentic marketing workflows Take Control: Customer-Managed Keys for Lakebase Postgres Get hands on with agents, vibe coding and more at Data+ AI Summit Mercedes-Benz Builds a Cross-Cloud Data Mesh with Delta Sharing and Intelligent Replication, Cutting Costs by 66% What Is a Transactional Database? Introducing Genie Agent Mode Governing coding agent sprawl with Unity AI Gateway Governing Coding Agent Sprawl with Unity AI Gateway What is pgvector? Banks Don’t Have an AI Problem – They Have a Data Platform Problem Open Platform, Unified Pipelines: Why dbt on Databricks is Accelerating Why Your Agents Can’t Read Enterprise Documents — and How to Fix It Building with Databricks Document Intelligence and Lakeflow Databricks on Google Cloud: Innovate Faster. Smarter. Together. Introducing the Databricks Connector for Google Sheets: Real-Time, Governed Lakehouse Data in the Sheets Users Love Unity AI Gateway: How to connect agents to external MCPs securely Expanding agent governance with Unity AI Gateway Agentic reasoning in practice: Making sense of structured and unstructured data Agent Bricks: The Governed Enterprise Agent Platform 8 AI and data trends shaping financial services in 2026 Building real-time product search on Databricks Lovable + Databricks: Build Data-Driven Apps at the Speed of Thought Memory scaling for AI agents Powering clinical research innovation: How TriNetX uses Databricks to accelerate drug development Database Branching in Postgres: Git-Style Workflows with Databricks Lakebase How Zalando built a unified data foundation for AI and analytics on Databricks The next era of the open lakehouse: Apache Iceberg™ v3 in Public Preview on Databricks How FSIs eliminate silos between clients, operations, and finance How MakeMyTrip achieved millisecond personalization at scale with Databricks A multi-agent approach to audience intelligence AiChemy: Next-generation agent with MCP, skills and custom data for drug discovery Accelerate business insights with Lakeflow Connect, now with a Free Tier Unlocking Next-Gen Customer Experiences with Data Intelligence for Marketing
The question your commercial data should already be able to answer
2026-05-18 · via Databricks

Seventy to eighty percent of patients with transthyretin amyloid cardiomyopathy (ATTR-CM), a progressive, often fatal form of heart failure, don't know they have it. The disease mimics other cardiac conditions. The only way to confirm it is a specialized diagnostic scan. And the only way to get patients there is to make sure the right cardiologists, at the right practices, are educated and prioritized by a field force that's working from better information than a static call list.

That's the commercial reality that Heart Health Pharma lives every day. And it's a sharper version of the challenge every specialty pharma commercial team faces: 

The data exists, but it doesn't reach the right person, in the right format, at the right moment.

Most data platform investments solve half of this problem. They connect data from source systems like Veeva to an analytics environment, centralize it, govern it and then surface it in a dashboard that a rep can't access from a parking lot and an MSL can't query the night before a Key Opinion Leader (KOL) visit. The data moves. But, the intelligence doesn't.

Databricks and Veeva are built to close the commercial intelligence gap

Not just by pulling data out of Veeva and into a separate system, but by bringing AI directly into the workflows your teams already use, and integrating bidirectionally. For teams using Veeva Vault CRM itself, Databricks Genie agents and AI/BI dashboards are embedded in your workflow, surfacing insights powered by the full depth of your commercial dataset. 

The implication is profound: your commercial teams, from sales reps to medical science liaisons to territory managers, don't log into another tool. They ask a question in the app and workflow they already have open.

Here's what that looks like in practice across three commercial personas, on one unified platform.

The Sales Rep Agent

It's 7:45 AM and John is in the parking lot before a 9:30 call. With Databricks Genie embedded in Vault CRM, he opens a live geographic view of every healthcare professional (HCP) in the area, surfacing suspected ATTR-CM patient counts near each practice, formulary access scores, office availability, NRx-weighted priority rankings, and talking points, all dynamically generated from the full commercial dataset. When a physician cancels, he asks Genie to rebuild his day. He gets an answer in seconds, not a ticket to the analytics team.

Sales Rep Agent in action

The differentiator: Databricks can reason across any data you bring, whether IQVIA claims, Komodo patient signals, call notes, specialty society directories and beyond. The AI isn't working from a fixed template. It's making the best decision for that rep, in that geography, in that moment.

The MSL Agent

The night before a KOL visit, Sarah needs to know what Dr. Smith is publishing, which trials he's following, and who his closest scientific collaborators are. A Databricks reasoning agent searches only the sources her organization has approved (ie Veeva Link, PubMed, ClinicalTrials.gov, ASNC practice guidelines) and synthesizes a pre-call brief with traceable citations in minutes. 

MSL Agent in action

She can see exactly which source each claim came from. In a regulated scientific exchange environment, that's not a nice-to-have. It's compliance.

The Territory Manager Agent

Bob needs more than a KPI scan. He needs to know which reps are over-calling defenders and under-calling whitespace, which suspect-patient signal is sitting unworked, and which HCPs have gone dormant. Databricks AI/BI dashboards, dynamic, personalized to his role and territory, embedded in Veeva, give him that view. 

Territory Manager Agent in action

When the dashboard isn't enough, Genie is one question away. No analyst request. No waiting until next week's report.

All of your commercial teams, powered by the same Databricks platform, in Veeva Vault CRM

Databricks lakehouse architecture can power all of your commercial teams. Same governance layer. Same Unity Catalog managing access, lineage, and compliance across every persona. The sales rep, the MSL, and the territory manager are drawing from the same trusted data, surfaced in the format that fits their workflow, at the depth their role demands.

That's what it means when the platform conforms to the workflow, not the other way around.

Join Databricks at the Veeva Commercial Summit in Boston 

At Veeva Commercial Summit May 19-20 in Boston, stop by the Databricks booth (S1). Come with your hardest questions about your data, your field force, and your therapeutic area.

And on Wednesday, May 20 at 9:00 AM ET, the Databricks team will be presenting Embedding External AI Agents in Vault CRM, a live look at how Databricks Genie agents run natively inside Vault CRM, what the integration makes possible for field commercial teams, and how to start building. 

Veeva Commercial Conference Session

If you can't make it in person, reach out to your account team, and ask what it would mean for your field force to stop waiting on answers.