Data topics

Chargeback exceptions can quietly erode pharma net revenue when pricing discrepancies, eligibility issues, duplicate claims, contract mismatches, and other anomalies become buried in high-volume transaction data. This guide explains how AI agents can transform chargeback analytics by continuously monitoring transactions, flagging unusual patterns, and investigating the drivers behind exceptions across wholesalers, contracts, products, customers, and pricing terms.

See how Agios Pharmaceuticals uses AI-native commercial analytics to support launch-scale decision-making. This blog explores how the team brings data and AI into commercial workflows to accelerate insights, investigate performance drivers, and give teams faster answers as launch complexity grows.

Launching a new therapy has never been more data-intensive—or more complex. Commercial teams must coordinate brand strategy, field execution, HCP engagement, market access, patient adoption, omnichannel performance, and competitive intelligence while making decisions in near real time. This buyer's guide compares the leading pharma launch analytics platforms in 2026, evaluating what each solution does best across launch planning, forecasting, HCP targeting, territory performance, market access analytics, omnichannel measurement, patient journey insights, and AI-powered commercial intelligence.

This buyer’s guide compares the leading GTN platforms in 2026 based on the specific jobs pharma teams need to accomplish, including GTN forecasting, accrual management, rebate administration, contract analytics, scenario modeling, revenue optimization, and executive reporting.

This field guide examines the AI agent use cases that are actually being deployed across brand analytics, field sales, market access, forecasting, omnichannel engagement, incentive compensation, and commercial operations. The guide compares leading vendors and deployment approaches, highlighting the differences between AI copilots, agent frameworks, agentic analytics platforms, and purpose-built commercial AI solutions.

Healthcare professional (HCP) engagement is becoming increasingly complex as pharma organizations navigate more channels, more data, and growing expectations for personalized experiences. This guide compares 12 leading HCP engagement platforms across the capabilities that matter most in 2026, including engagement orchestration, next-best-action decisioning, omnichannel activation, CRM integration, AI-powered personalization, and closed-loop measurement.

The blog explores how modern AI-powered analytics platforms are evolving beyond static dashboards into conversational, agentic systems capable of automating complex analytical workflows and surfacing deeper business context. Rather than simply reporting what happened, the strongest platforms help commercial pharma teams uncover why performance changed, identify growth opportunities, and operationalize faster, more confident decisions across brand strategy, field sales, and commercial operations.

This blog breaks down five major shifts shaping the future of commercial pharma analytics, including the rise of AI agents, deeper integration of structured and unstructured data, increasing demand for explainable AI, and the move from dashboards to proactive, automated insights.

This guide compares the 11 best AI platforms for pharma commercial analytics in 2026, evaluating how each supports brand performance, market access, patient analytics, field force effectiveness, incentive compensation, and omnichannel engagement. It highlights a key shift in the market: traditional BI and SFE tools focus on reporting what happened, while newer AI platforms differentiate by investigating why performance changed. The post breaks down core capabilities like root cause analysis, data integration, governance, and agentic workflows—helping pharma teams identify which platforms can deliver faster, more actionable insights across commercial operations.

This blog compares the best pharma market access analytics platforms in 2026, evaluating how each solution helps teams navigate complex payer dynamics, pricing strategies, and access barriers. It highlights a key shift in the market: traditional BI tools struggle with fragmented, slow-moving data, while modern platforms combine real-time data integration, semantic understanding, and AI-driven automation. The guide outlines what to look for—such as multi-source data federation, explainable insights, and automated root cause analysis—and shows how leading solutions enable faster detection of payer changes, improved pull-through, and more proactive, data-driven market access decisions.

Lessons from 70 sessions at Reuters Pharma USA 2026 show pharma shifting to AI-driven, data-connected models—prioritizing execution, real-time insights, and smarter decision-making.

A 2026 comparison of 10 AI platforms for pharma Field Force Effectiveness, focused on what actually separates “agentic” FFE analytics from dashboards: automated deep insights (root cause), proactive monitoring, governed Q&A, pharma data integrations, and workflow orchestration—plus clear “best for” recommendations by use case and team maturity.

Patient journeys in pharma don’t live in one place—they’re scattered across claims, APLD, EHR systems, and line-of-therapy (LOT) views. This post breaks down how patient journey analytics, APLD analysis, and LOT analytics fit together to show where patients stall, switch, or drop off treatment. It also highlights how AI-powered analytics can stitch these signals into a single view, helping commercial and medical teams target the right HCPs, close adherence gaps, and design more effective interventions.

A practical overview of AI-powered HCP targeting platforms for pharma, explaining how prescriber intelligence is built (claims, CRM, specialty/payer signals), what capabilities matter (segmentation, next-best-action, governance, transparency), and how teams use these tools to prioritize accounts, tailor outreach, and measure lift—without relying on black-box scoring alone.

Pharma’s most valuable insights live in unstructured content (CRM call notes, MSL interaction reports, payer meeting summaries, slide decks, emails/PDFs), but they rarely get analyzed at scale—so teams end up relying on skims and anecdotes; it then explains how Tellius uses AI agents to connect to those sources, extract entities (products, competitors, objections, sentiment), cluster recurring themes, and answer natural-language questions with supporting citations, highlighting a biotech example where analyzing 15,000+ MSL reports helped surface competitive signals faster and cut manual synthesis from weeks to same day.

Pharma incentive compensation analytics adds an intelligence layer on top of existing IC engines (Varicent, Xactly, Excel) so they don’t just calculate payouts, but actually explain them. The post dives into why reps build shadow spreadsheets—geographic inequity, data gaps, opaque plans, and risky Excel processes—and how AI + a semantic layer + conversational access + agentic workflows can investigate payout variance, monitor fairness, simulate plan changes, and catch data issues before statements go out. It also outlines practical use cases (automated variance investigation, fairness monitoring, scenario planning, data validation, plan simulation), a phased 9–13 month implementation approach, and the ROI metrics that show reduced disputes, faster resolution times, and higher rep trust.

Most pharma next-best-action programs promise AI-driven guidance for reps, but fall apart on bad data, missing context, and black-box models. This post breaks down why traditional NBA efforts stall, and what’s actually working: analytics-ready data products, a semantic layer that understands pharma metrics and entities, identity resolution to kill “ghost” HCPs, and agentic analytics that can investigate, validate, and recommend real next-best actions across HCP targeting, territory fairness, coaching, and omnichannel orchestration.

Pharma brand and commercial insights teams are stuck in the 5-system shuffle, Excel export hell, and a constant speed-versus-rigor tradeoff. This practical guide explains how agentic analytics, a pharma-aware semantic layer, and AI agents transform brand analytics—unifying IQVIA, Symphony, Veeva, and internal data, offloading grunt work, and delivering fast, defensible answers that actually shape brand strategy.

When pharma brands strike major deals, they don’t just gain access—they inherit complex tradeoffs. From navigating volume vs. margin to managing payer fatigue, these tradeoffs shape strategy long after contracts are signed. This blog breaks down five recurring tensions in market access and shows how AI-powered analytics helps commercial and HEOR teams model scenarios, simulate outcomes, and move from rigid strategy to adaptive execution.

Too often, commercial pharma reps are working from outdated, imprecise target lists—wasting time on the wrong doctors while missing key growth opportunities. This blog explores how Tellius transforms field sales by turning fragmented data into real-time, physician-level insights. Learn how natural language querying, AI-powered drop lists, and dynamic segmentation help teams respond faster, optimize territory planning, and drive impact at the moment of decision—not just at the end of the quarter.

With the FDA’s landmark approval of CAR-T therapies like Abecma and Carvykti as earlier-line treatments, the commercial landscape is shifting rapidly. This blog explores the next phase of CAR-T market access and the analytics brand teams need to navigate it. Learn how to forecast pull-through, identify high-opportunity accounts, and guide sales execution using real-world data, segmentation strategies, and AI-powered insights. From payer dynamics to provider referral patterns, find out what it takes to win in a competitive, high-cost therapeutic space.

Faster drug approvals are coming. Here's how field sales, market access, and brand teams must adapt—and why AI analytics is no longer optional.

The path from dashboard sprawl to true self-service isn't just about better technology—it's about unlocking your team's ability to act on insights in real-time. While traditional BI tools force pharma teams into endless workarounds, AI-powered platforms like Tellius are purpose-built for the complexity, compliance requirements, and speed demands of commercial pharma.

Key learnings from Novo Nordisk to help you along your journey to unlocking business value with AI in the commercial pharmaceutical space.

With Snowflake’s powerful AI Data Cloud as the foundation, Tellius adds the magic of conversational AI, automated insights, live Vizpads, and predictive analytics.

Learn how Novo Nordisk’s commercial analytics team is leveraging Tellius' AI analytics solution to drive data-driven answers and insights.

During a PMSA 2024 Q&A session, leaders from Novartis, Pfizer, UCB Biopharma, and Boehringer-Ingelheim covered the current state of GenAI and its immense potential to transform the pharmaceuticals industry. So, what's in store?

Learn how AI-powered approaches to pharmaceutical and biotech pricing, contracting, rebates, and revenue management are helping commercial and market access teams navigate common challenges.

Pharmacyclics, an AbbVie company, sought to empower more members of its commercial team to make better and faster data-driven decisions. The IT team used Tellius to up their analytics game.

Implementing AI-driven analytics is a catalyst for transformative change in the pharmaceutical and life sciences world.

Market access teams at pharmaceutical & life science organizations are critical to commercial success. Here's how AI-powered approaches can help.

Tellius has announced a partnership with Indegene, a technology-led healthcare solutions provider. The partnership will leverage Indegene’s deep domain knowledge in life sciences and expertise in advanced data and analytics, empowering companies with data at scale and AI-driven analytics that enable faster and better business decisions.

This post showcases how Tellius can be leveraged to more easily perform an analysis of a drug’s commercial performance using 3rd party pharmaceutical sales data combined with internal sales and operations data.

Humans are curious, collaborative, and intelligent. Our business systems are the exact opposite — they require us to think and communicate more like a machine to get what we want. To answer the question posited earlier, this gentleman’s BI experience was likely lacking humanity.

Healthcare organizations adopting a data-driven culture are set to derive major benefits in terms of improving care to patients and...

Doctors are pretty smart people, and it’s not a lack of want that historically denied them better insights to their...