Data Visualisation for Life Sciences: Why You Should Start With an Interface, Not a Platform

In conversations with life sciences companies about the rationale for using data visualization in their projects, we’ve noticed a recurring pattern: an all-or-nothing approach to creating their front-end layer. Teams often come with a vision for a comprehensive platform: a large, complex system with advanced capabilities and enterprise architecture from day one.

This way of thinking is understandable. In environments shaped by complex data and multiple stakeholders, platform-oriented thinking feels like a natural response.

But our experience shows that a full platform doesn’t have to be the starting point. In most cases, we recommend the MVP (Minimum Viable Product) approach that has driven successful SaaS companies. This means building the minimum that delivers value, learning from real usage, then expanding based on what you actually need.

In this context, a well-designed visualization interface focused on presenting your R&D data can serve as that MVP. Not as a replacement for a future platform, but as a strong starting point that delivers value faster and can be easily transformed over time as needs evolve, use cases expand, and understanding grows.

Instead of designing the entire system upfront, you start by building an initial interaction layer and then decide what actually needs to be removed, what needs to scale, integrate, and evolve further, listening to your market response. And sometimes, a single user journey is enough to ensure a successful evaluation process with your investors and potential buyers.

In this article, we examine why the interface-first approach makes sense in the life sciences, particularly for sales and business development. Read on to see whether it applies to your case.

5 Reasons Why Interface-First Actually Works in Life Sciences

1. You learn real usage patterns, not just stated requirements

When building a platform, certain decisions are inevitable: 

• Which features will buyers actually use? 
• How should data be navigated? 
• What level of interactivity supports their work?

Answering these questions helps shape how products are designed, structured, and ultimately delivered to users.

When you decide to start by building a complete platform, you need to answer everything upfront. Many companies approach this process carefully: they talk to potential clients, partners, and internal stakeholders, gather detailed requirements, and design based on that feedback.

But the challenge is that stated needs and real usage patterns can diverge. Not because anyone is wrong, but because people cannot reliably predict how they will actually work with complex data until they have something concrete to interact with.

This is not unique to life sciences. A Pendo study found that 80% of features in software products are rarely or never used, not because they are poorly built, but because real usage differs from early assumptions.

For example, your potential client may express interest in uploading their own datasets and running custom analyses. It sounds reasonable: advanced users value flexibility and control. Your team invests time building upload flows, parameter controls, and filtering mechanisms. In practice, these features may be used far less than expected, while other needs become far more important.

For instance, buyers might discover that datasets don’t change frequently enough to justify uploads, but they need to generate custom reports for different internal audiences. Or they discover that interactive filtering of your standard results is more valuable than running entirely custom analyses.

Starting with an interface as your MVP can change this dynamic. Instead of designing primarily around stated preferences, teams can observe how people actually interact with the data and what they need from it. They can discuss real-world use rather than hypothetical scenarios.

One example in practice is Deepflare, where an early visualization interface helped validate how scientists interacted with complex AI-generated results:

This approach may reduce the risk of building unnecessary features by grounding decisions in observed practical use.

2. An interface allows buyers to evaluate your results without waiting for a platform

When you’re selling to buyers who already understand the domain, the core challenge is making it clear how your results apply to their specific case.

To support this evaluation, organizations often assume they need a full platform: user accounts, workflows, data management, permissions, and infrastructure.

In practice, buyers usually need something simpler at this stage. What matters first is a way to see and understand the results clearly:

• How does the data behave?
• How do different conditions compare?
• What patterns emerge?
• What do the outputs actually look like in their context?

A visualization interface allows this kind of evaluation without requiring a full platform structure. It creates a focused environment where buyers can examine the actual data directly and evaluate the results.

Interactive access can further support this process by allowing evaluators to explore relevant conditions, compare scenarios, and focus on what matters to their use case.

This kind of exploration can reduce friction in the evaluation process. Verification that might otherwise require multiple follow-ups, explanations, or back-and-forth communication can happen directly within the interface. This can lead to faster buy-in, even at such an early stage of your platform development, such as the interface.

Following the MVP approach, the interface does not replace future platform needs. It simply creates a space where results can be examined and evaluated first, before committing to a full system architecture. Platform decisions can then be made later, informed by how buyers actually interact with the data.

3. You unlock sales capability months earlier

We’ve discussed what buyers need to evaluate your results. The next question is when you can actually start using that capability in real sales conversations.

Based on our experience, an interface can be ready in 4-8 weeks. A full platform typically requires 6-12 months.

In competitive markets, six to twelve months is a long time. During that period, buyers evaluate solutions, make decisions, and move forward. If you cannot show real data, validate claims, and let buyers explore scenarios relevant to their needs, those opportunities often move on.

Building an interface as an MVP gives you a usable sales tool much earlier than waiting for a full platform to be completed.

However, it’s not only about faster development but also about how quickly we handle new sales opportunities.

When buyers can explore results through an interface, conversations move faster. Your team spends less time preparing custom materials and manual explanations and more time engaging in real decision-making discussions.

The practical advantage is straightforward: you start selling earlier, handle opportunities faster, and engage more prospects while their evaluation windows are still open.

4. Partnership readiness comes sooner with an interface

The same timing dynamics that matter in sales also apply to research partnerships, investor engagement, and strategic collaborations — and these opportunities often operate on timelines you cannot control.

When these opportunities arise, having even a simple visual interface to demonstrate your solution supports the conversation.

The underlying mechanism here is data transparency and interpretability.

When results are accessible, understandable, and verifiable, trust becomes easier to build across different stakeholder groups.

This relationship is reflected in industry research. In Accenture’s report, executives directly associate increased data transparency with trust outcomes: 42% report greater trust with partners, and 37% with investors and shareholders.

A visualization interface operationalizes this transparency in practice, turning complex research outputs into something that can be explored, interpreted, and discussed across various audiences.

For this purpose, an interface provides the necessary functionality. Just as buyers do not need the full platform infrastructure to evaluate your technology, partnership stakeholders do not either. What they need is a clear demonstration of what you deliver.

5. Interface-first lowers both cost and risk

Starting with an interface, following an MVP approach, speeds up value delivery and changes the cost structure.

Based on our experience, a basic interface typically accounts for 10–20% of the investment required to build a full platform.

Even if your organization is already committed to building a complete platform in the long term, by starting with an MVP, you’re not increasing total cost, but reducing risk. You are not investing 20% now and then 100% later for a total of 120%. A well-designed interface becomes the foundation you build on — provided it is architected and built with the right components from the start.

This is where quality and structure matter.

An interface built on scalable, production-grade components (such as Highcharts) and modular architecture can evolve into a full platform, and the investment carries forward.

By contrast, building a full platform before validating assumptions with real users creates structural risk: core decisions get locked in without evidence that they reflect real buyer behavior. At the end of the day, you may end up spending your whole budget on a platform covering just 20% of your customers’ needs.

Let’s imagine building a platform priced at $1,000,000. Following the Pendo research we mentioned earlier, which found that 80% of features go unused, you may spend the full $1,000,000 but deliver only $200,000 in value. Building what users actually need on top of this could add another $800,000, bringing the total to $1,800,000.

When done correctly, interface-first changes the risk profile of product development:

• If assumptions about buyer needs turn out to be wrong, the financial exposure is limited while learning occurs
• If the interface already supports commercial goals, full platform investment can be delayed until demand clearly justifies it
• When platform capabilities become necessary, the interface serves as the foundation — you are extending, not replacing

Starting with an interface allows organizations to validate commercial value and observe real buyer behavior before committing to large-scale infrastructure investment. If a platform is the right next step, those decisions are guided by evidence from real usage, not projections.

The financial logic is not about avoiding platform development, but about sequencing capital efficiently: Build a strong, usable foundation first, and scale infrastructure only when demonstrated demand and usage justify the investment.

6. An interface can reveal new revenue opportunities

An interface created as an MVP for evaluation and validation can sometimes uncover additional commercial value.

In early customer interactions, some buyers express interest in continued access—not just one-time evaluation, but ongoing ability to explore data, compare scenarios, and support internal analysis. This can create an opportunity to offer interface access as a complementary service alongside core offerings.

This aligns with broader market trends. Data monetization in life sciences was valued at $386 million in 2024 and is projected to grow at 16.5% annually through 2030, indicating increasing recognition that data assets can generate commercial value. Interface access represents one potential path within this landscape.

Starting with an interface lets you assess whether such opportunities exist in your case before committing to platform infrastructure or business model decisions. You discover this through real interactions: when buyers ask about extended access, request team-wide availability, or propose commercial arrangements you hadn’t considered.

If the interface is built with a scalable architecture, it can evolve to support these use cases without requiring a complete rebuild. Access controls, user management, or additional views can be added incrementally.

Not every interface becomes a revenue stream. But interface-first creates visibility into these possibilities early, when acting on them requires less investment than retrofitting a platform built for different purposes.

Interface-First vs. Full Data Platform: Key Differences

We’ve covered key reasons why the interface-first approach works in biotech. To help you see these differences at a glance, here’s a direct comparison of what each approach delivers:

Considering an Interface-First Approach?

If you’re considering a visualization interface as a first step, it’s worth building it in a way that can scale if needed.

At Black Label, we work in long-term partnerships and build solutions that are structured, transparent, and maintainable, so they can evolve over time. This is how we’ve worked with partners like Highsoft and WiTronix for more than a decade.

If you’re exploring how to present and use your life sciences data more effectively, we can help.

Contact us to book a Discovery Sprint and see whether an interface-first approach makes sense for your case.