The case for R&D observability

Why is it so hard to get answers to pretty straightforward questions, when it comes to biotech data? And what can biopharma borrow from what the tech world calls "observability", to solve this problem?

There’s a scene that plays out in almost every biotech boardroom. Someone asks: “where do we stand on the latest progress for our lead compound?”

That should be a simple question. Instead, the CSO frowns, promises to circle back, and spends the next few days pinging individual scientists for the latest data, and piecing together updates from email chains, Teams messages, and spreadsheets. By the time the answer arrives, the conversation has already moved on.

In other words: critical scientific decisions are being made with partial visibility. Everyone accepts this as just “the cost of doing R&D.” But it doesn’t have to be.

The software parallel

20 years ago, software engineers had the same problem. Applications were no longer a single codebase running on a server in the closet – they sprawled across fleets of machines, databases, and APIs. When something broke, engineers had no idea where to look. Debugging meant poring over log files like archaeologists.

That mess gave rise to observability. Companies like Datadog and New Relic realized engineers needed to understand their systems in real time. A way to answer the most basic questions immediately: What’s working? What’s failing? What needs fixing right now?

Today, observability is a multi-billion-dollar industry and an essential part of how modern software actually functions. Without it, you can’t run Amazon, or Netflix, or even the systems your biotech uses every day.

Biotech’s observability moment

Drug development today looks eerily similar to software in 2005. A single program spans computational design, chemistry, assays, CROs, regulatory prep – dozens of interconnected processes, each producing data that lives in its own silo.

But when leadership wants to know where things stand and where the biggest risks are, the answer usually comes from a patchwork of status meetings, spreadsheets, and emails. In other words: biotech R&D is operating with logs and gut feel in a world that demands real-time visibility.

So what does observability look like in this context? It depends on who you are:

• For a chemist: seeing the status of design cycles across the portfolio, which compounds are moving through assays, and which results actually change your hypotheses.
• For operations: knowing exactly what’s sitting with each CRO, whether it’s on track, and whether the data passes quality checks. No more “six weeks later we realized something got missed.”
• For leadership: being able to glance at the pipeline and see what’s aligned with your target product profile, what’s at risk for the IND, and where you’re missing critical data.

This is less about adding more data streams and more about turning fragmented activity into coherent visibility; the ability to interrogate your system while it’s running, instead of piecing it back together afterward.

This market cycle makes the need urgent

For much of the last decade, inefficiency in biotech was survivable. Capital was abundant, timelines were elastic, and “coordination cost” was baked into the model. A missed milestone might sting, but it rarely killed a company outright.

That’s not today’s environment. Funding cycles are tighter, investors demand capital efficiency, and every delay directly erodes competitiveness. In that world, operating without observability is an existential risk.

Think about what happens in practice: a program drifts for weeks because no one notices missing data until a quarterly review; a CRO delay pushes back a filing because operations didn’t flag the dependency; a team keeps prioritizing the wrong compound because the latest assay results were buried in email. Each small slip compounds into months of lost time. And in drug development, months often determine whether you’re first-to-market or not.

This is why observability isn’t “nice-to-have tooling.” It is the difference between being proactive and reactive, between learning fast and stumbling blind.

The infrastructure shift required

Of course, you can’t just flip a switch and declare yourself “observable.” Software engineers learned this the hard way: simply aggregating logs and metrics didn’t solve the problem. What mattered was structure and context.

Biotech faces the same hurdle. Teams generate enormous amounts of data (experiment results, protocols, CRO contracts, regulatory drafts) but it lives in silos. Even worse, most of it lacks the metadata that would make it interpretable: the hypothesis behind the assay, the rationale for the CRO request, the criteria for success. 

Without that scaffolding, you can’t build observability.

This is where a new layer of infrastructure is required. A system that captures both the data and the reasoning around it. When you do that, you're encoding the logic of your R&D into something you can query, update, and share.

That’s the layer Kaleidoscope is building. By capturing both the experimental record and the decision context, we give teams the ability to see their R&D as a live system, not a static archive. Observability emerges as a byproduct of working this way – you don’t have to piece together the state of your program, because the state is visible by design.

The compounding advantage

The final lesson from software is that observability compounds. The companies that adopted it early built a reflex for operating at scale. Biotech is about to replay this story. The companies that make their R&D observable will build pipelines that are structurally more resilient. They’ll know when to double down on a program and when to cut losses, with weeks of lead time instead of months of drift. They’ll manage CROs and regulators with clarity instead of scramble. And critically, they’ll compound knowledge: every experiment becomes not just a result, but a structured node in a larger decision graph.

In an industry where winner-takes-most dynamics dominate - the first to file, the first to show efficacy, the first to scale manufacturing - those advantages aren’t incremental. They’re decisive.

Kaleidoscope is a software platform for biotechs to robustly manage their R&D operations. With Kaleidoscope, teams can plan, monitor, and de-risk their programs with confidence, ensuring that they hit key milestones on time and on budget. By connecting projects, critical decisions, and underlying data in one spot, Kaleidoscope enables biotech start-ups to save months each year in their path to market.