Data Visualization is Broken in Biotech: Q&A with Sunitha Venkat

Sunitha Venkat, vice president of data services and insights at Conexus Solutions, spoke with Pharmaceutical Executive about the importance of using high quality data visualization methods. According to her, the industry has been relying on static and fragmented methods that can reduce the potency of high quality data.

Pharmaceutical Executive: Why is data visualization broken in biotech?
Sunitha Venkat: Biotech is sitting on some of the richest data in healthcare, spanning discovery science, clinical trials, real-world evidence, and emerging commercial signals. The problem is not a lack of data, but how organizations experience it. Most teams still rely on static, fragmented views designed for a much simpler world of rows, columns, and summary charts, while the science itself has become deeply multidimensional.

Visualization is often treated as an afterthought, something applied at the end of an analysis rather than as part of the reasoning and decision process. Dashboards tend to present tables and filters rather than helping leaders understand trade-offs, risks, and what to do next. On top of that, data is siloed across clinical, medical, and commercial functions, so executives rarely see an integrated view of patient journeys, access barriers, and scientific signals in one place.

In a lean biotech environment with aggressive milestones, teams often accept reporting that is “good enough.” But those visuals can hide weak assumptions, missing context, or early warning signals. In that sense, data visualization in biotech is not just aesthetically broken; it is strategically broken.

PE: What are the benefits of improving visualization methods?
Venkat: When visualization is done well, it becomes a thinking tool rather than a reporting artifact for biotech, which directly translates into better, faster decisions. High-quality visuals help teams spot patterns, correlations, and outliers in complex data much more quickly, which matters when making calls on trial design, enrollment risk, safety signals, or launch investments.

Better visualization also fosters more substantial cross-functional alignment. A well-designed view becomes a shared language across clinical, medical, commercial, and finance teams, shifting conversations away from whose spreadsheet is right and toward scenario planning and tradeoffs. Externally, clearer visuals improve how evidence is communicated to regulators, investors, and partners, especially for emerging biotechs that need to tell a credible story with limited data.

Ultimately, improving visualization supports better patient and business outcomes by helping organizations identify the right patients faster, surface access barriers earlier, and allocate scarce resources more effectively. This is not a cosmetic upgrade. It is an operational and scientific advantage.

PE: In what ways can biotech expand its use of visualization?
Venkat: Biotech needs to expand visualization across where it is used, how it is designed, and who it serves. First, visualization should be embedded across the whole data lifecycle, from exploratory analysis through decision making, not just at the end to decorate slides. Trial teams, for example, should be using interactive visuals to monitor enrollment risk and data quality in near real time. In contrast, commercial teams use them to test different access or targeting scenarios.

Second, biotechs should move from static dashboards to interactive, narrative-driven experiences. Instead of a wall of charts, dashboards should guide users through what is happening, why it matters, and what options exist, and allow exploration of “what if” questions. This often means integrating clinical, real-world, and operational data into a single navigable view.

Finally, there is a growing opportunity to leverage AI-assisted and advanced visual analytics. Rather than relying solely on manual dashboarding, smaller biotechs can use intelligent systems that surface anomalies, patterns, and emerging narratives while still grounding insights in transparent visuals that decision-makers can trust.

PE: Does improving visualization require multi-department cooperation?
Venkat: Yes, and that is one of the main reasons progress has been slow. Effective visualization sits at the intersection of data, domain expertise, and decision context. Data teams understand pipelines and models. Clinical and medical teams understand the science and regulatory constraints. Commercial teams understand markets and patient access. Executives and finance leaders understand risk, runway, and strategy.

If any of those perspectives are missing, you end up with visuals that are technically correct but practically useless. Improving visualization requires co-designing dashboards with end users, establishing shared definitions and standards, and embedding visualization into operational rhythms such as clinical governance, portfolio reviews, and brand planning. Multi-departmental cooperation is not optional if visualization is to be trusted and adopted.

PE: What prevents biotech companies from expanding their use of visualization?
Venkat: Several barriers show up consistently. The first is fragmented and heterogeneous data. Biotech data spans omics, imaging, trial systems, EHRs, claims, and commercial feeds, often residing in disconnected environments with inconsistent standards. When the underlying data fabric is messy, organizations hesitate to invest in more advanced visualization.

The second barrier is limited resources and competing priorities. Many biotechs run lean, with no dedicated visualization function and analysts stretched thin, producing one-off dashboards. There is also a tendency to think in tool-centric terms, assuming that buying a BI platform solves the problem, when the real work is understanding user journeys, decision points, and cognitive load.

Finally, there are cultural and literacy challenges. Different teams define metrics differently, interpret visuals differently, and default to familiar formats like spreadsheets and slide decks. Moving to interactive, integrated visualization requires change management, training, and leadership to model new ways of consuming information.

For biotech, the opportunity is to treat visualization as a strategic capability rather than a reporting chore. When that shift happens, visualization becomes a force multiplier, allowing small teams to punch far above their weight in both scientific and commercial impact.