Vismit Sharma and Tanisha Tiwari coauthored this article.

Key takeaways:

• Launch success depends on establishing a commercial data and analytics foundation well before Day One, not scrambling in the final months before launch.
• Lean biopharma teams must make disciplined build vs buy decisions to balance speed, cost and long term scalability.
• Clear alignment on success metrics and KPI definitions is essential to prevent fragmented views of performance across functions.
• Proactive data quality monitoring and governance are critical to maintaining trust in insights and executive decision making.
• AI can significantly accelerate commercial productivity, but only when built on a unified, well governed and scalable data foundation.

The strength of a biopharma company’s commercial data foundation often determines whether a product launch gains early traction or will struggle to scale. Fragmented data and delayed insights do more than just create inefficiencies. They fracture alignment and slow the decisions that launches depend on.

In a recent webinar, ZS’s Vismit Sharma (associate principal, platform and products) and Apellis’ Nicholas Ruhl (director, commercial data operations and insights) discussed what it takes for emerging and midsize biopharma organizations to build a data-driven launch organization that’s not only ready for Day One but prepared to grow and evolve in-market.

The following is a summary of their conversation. Watch the full webinar here.

Moderator: Small and midsize biopharma companies operate very differently from large pharma. What are the key differences when it comes to commercial data and launch execution?

Nicholas Ruhl: Smaller biopharma organizations typically have leaner teams, tighter budgets and fewer in‑house data and technology capabilities than large pharma. That reality creates both constraints and opportunities. On the one hand, people wear multiple hats, so you have to be very intentional about how you spend time and money—and very selective about which capabilities you build versus which you source from partners. On the other hand, you can often move faster, with less red tape and more willingness to “move quickly, learn and adjust” as you go.

Because of this, picking the right vendors and partners becomes critical. You simply can’t afford to stitch together a complex ecosystem that only a few people understand. You need solutions and collaborators that can flex with you as you grow and as launch needs evolve.

Vismit Sharma: What I see consistently with emerging and midsize biopharma is less formality and more collaboration. There are fewer long slide decks and more whiteboarding and working sessions. One person may effectively be doing the job of three, which makes hyper‑prioritization essential.

That also means the external leverage model matters a lot. You need an operating structure where internal teams focus on the highest‑value decisions, while partners and platforms handle as much of the repeatable data and analytics work as possible. When that balance is right, you get speed without burning people out or creating chaos in your data foundation.

Moderator: From a commercial readiness perspective, what should emerging biopharma teams focus on before launch?

NR: I tend to think about launch readiness as a series of very practical steps. First, you need a deep understanding of the market, the patient journey and the access dynamics in your space. Without that, it’s hard to define what “good” looks like.

Second, you need alignment on what success means, both for launch and for the long term, and the KPIs that tie to those outcomes. Only then does it make sense to move into step three, which is identifying the data you need to generate the right insights. That includes thinking about data availability, granularity, latency and cost.

The other consideration should be about who and what will help you meet your launch goals in a way that also sets you up for long‑term success. And finally, looking at progress against plan, surfacing risks early and adjusting as the launch reality comes into focus.

VS: Timing is one of the biggest themes here. For a first launch, you really want to be thinking about your commercial data and analytics foundation at least six months before launch, often earlier. If you wait until the last 90 days, you end up cutting corners or accepting a lot of manual work that will become unsustainable postlaunch.

The other important thing is interoperability. It’s not just about individual tools; it’s about how your systems talk to each other and how easily you can scale or swap components as you grow. And it’s important not to chase perfection on Day One. Launches are inherently messy. The goal is to be “ready enough” with a foundation that you can iterate on quickly, not to freeze in pursuit of a perfect blueprint that never survives contact with the real world.

Moderator: One of the biggest decisions biopharma teams face is whether to build in‑house or buy a platform. How should companies approach this decision?

VS: At its core, the build‑versus‑buy decision is about balancing control, cost and customization over time. Building everything internally can give you a high degree of control, but it also demands significant investment in talent, infrastructure and ongoing maintenance. That’s a heavy lift for many emerging biopharma teams that are already stretched thin.

When you consider platforms, there are a few dimensions that matter. You want an open architecture rather than a black box, so you can understand how things work and integrate other components. Self‑serviceability is key; your business users should be able to get answers without filing a ticket every time. Transferability matters so that knowledge and ownership don’t get locked into one individual. And modularity and breadth are essential, so the platform can grow with you—across brands, indications, functions and eventually geographies—without needing a complete rebuild.

Moderator: Is there a “right” operating model for commercial data and analytics in biopharma?

NR: I don’t think there’s a universal “right” model. The optimal structure depends on your company’s size, pipeline, therapeutic focus and internal culture. That said, effective models tend to share a few characteristics. Responsibilities and ownership are clearly defined. There is transparency in how data is processed and how metrics are calculated—no black boxes. And there are as few bottlenecks as possible, both in terms of technology and decision‑making.

Strong cross‑functional collaboration is also a constant. Field, market access, finance and executive leadership all need to trust that they’re looking at the same underlying truth. When your operating model supports that alignment, your launch decisions become faster and more coherent.

VS: Commercial operations and IT or data teams typically sit at the center of this. They are in the best position to unify metric definitions, terminology and data standards across functions. When they embrace that role, you can use dashboards not just as insight tools, but as governance tools, ensuring everyone is speaking the same language and that “one source of truth” is more than a slogan.

If you don’t do this, you end up with shadow systems—different teams building their own versions of the truth—which is one of the fastest ways to erode trust in data and slow down the organization.

Moderator: As companies grow, how should they think about scaling across functions and geographies?

VS: Scaling happens on multiple axes. There’s the operational side—adding new metrics, new data sources and new processes. Then there’s scaling across brands and indications, and finally across geographies. If you don’t design for this from the beginning, you build a technical debt that becomes very expensive to unwind.

The goal is to create robust data models and a system architecture that are modular. You want components that can be reused and localized, rather than reinvented. For example, when companies expand ex‑U.S., those that invested in reusable models and integrations up front can often stand up new markets in a matter of months instead of years. That’s where you start to see meaningful synergies from a thoughtful platform and data design.

Moderator: Data quality issues can significantly impact decision‑making. What are the most common challenges you see, and how can teams address them proactively?

NR: A lot can go wrong with data quality, and the downstream impact can be huge. Incomplete or inconsistent data, lags in data availability and misalignment on metric definitions and business rules are some of the most common issues. The result is confusion, erosion of trust and, ultimately, a reluctance to rely on analytics for critical decisions.

The key is to shift from reactive fire‑drills to proactive quality management. That means monitoring, validation and alerting that catch issues before they land in an executive dashboard. When teams know that quality checks are built into the process, they’re much more willing to trust and act on the data.

VS: Setting the right accountability structure upfront is essential. You need clarity on what checks your data partners perform, what your data warehouse and MDM teams are responsible for, and how issues get escalated. Standard operating procedures should spell out who gets alerted, what the next steps are and how you communicate to business users if something might delay a refresh.

A simple example is shipment data. If you expect shipments to follow a certain pattern, you can define thresholds for what counts as a spike. The moment those thresholds are breached, the right people are notified, the refresh can be paused if needed, and the audience can be informed of a potential delay. That kind of discipline goes a long way in preserving trust.

Moderator: With the rise of generative AI and agentic AI, how do you see the role of AI evolving for commercial teams?

NR: We could easily spend an entire session just on this topic, but at a high level, I see AI as a productivity accelerator and decision‑making enabler for commercial teams. It helps lean organizations move faster, separate signal from noise more efficiently and level the playing field for go‑getters who want to do more with less.

The best outcomes come when AI augments judgment rather than trying to replace it. AI can surface patterns and insights at a scale and speed that humans alone can’t match, but you still need human context and accountability. And because the landscape is evolving so quickly, it’s critical to be AI‑ready from a data foundation perspective—if your data is fragmented or poorly governed, AI will simply amplify those issues.

VS: Practically, we’re seeing a few clear categories of use cases. “Talk‑to‑data” style chat interfaces that let users ask questions and get insights in natural language; rep productivity tools, from advanced next‑best‑action engines to coaching solutions; and early agentic use cases that can execute simple workflows autonomously. On the technical side, AI is also boosting developer productivity across documentation, code generation and testing.

The common denominator is that none of this works well without a strong, unified, high‑quality data foundation. AI is not a magic layer you can bolt on at the end. It’s something you design for by building an architecture that is flexible, governed and ready to support new capabilities as they emerge.

Moderator: Based on everything we’ve discussed, how do platforms fit into the journey of building and scaling a strong commercial data foundation?

NR: The right platform should support teams from prelaunch through in‑market growth and into future indications. It needs to be scalable, adaptable and AI‑ready, and it should help you move from fragmented, manually stitched data to a more automated, insight‑driven environment.

When you have a single environment that brings data together, aligns on common metrics and streamlines how insights are delivered, you start to see much faster time to value and a wider cross‑functional impact. In that kind of setup, field teams, access, leadership and patient services can all draw from the same underlying foundation to make more coordinated, confident decisions—exactly the kind of role a platform like ZAIDYN® is designed to play.

VS: A modern platform approach also lets you sequence the journey instead of trying to do everything on Day One. You can start with the essentials—launch reporting and core operational analytics—and then gradually add more advanced capabilities such as AI‑enabled insights, new use cases or additional indications and markets as you mature. With a platform like ZAIDYN, that modular and AI‑ready design means you can keep extending your capabilities without constantly rebuilding the foundation. The real goal is to take teams out of reactive data wrangling so they can focus on the decisions that matter. When your commercial data foundation is connected, high‑quality and platform‑enabled, you’re not just prepared for launch—you’re set up to learn, adapt and scale throughout the product life cycle.