5 biotech observations to take into 2024

Happy 2024! Welcoming a new year is a wonderful opportunity to reflect on the past year of learnings and growth. And what a year it’s been. In the last 12 months, we weathered a start-up banking crisis, saw the world’s first CRISPR therapy approved, and witnessed massive strides in the field of generative AI. Just to name a few things.

At Kaleidoscope, we have the exciting opportunity to regularly interact with companies innovating at the forefront of these fields; from teams inventing novel therapeutics, to organizations pushing the bounds of what’s possible in cellular programming. And while our work entails a lot of building, it also involves a lot of listening, in an effort to take the pulse of the market and understand what’s top of mind for our customers.

So, here’s a look back at 5 of our observations from 2023 — trends and challenges alike.

Solving immediate problems while laying down forward-looking foundations is a delicate balancing act

At any company, there’s always a natural tension between focusing entirely on the most immediate problems versus investing time in robustness and scalability. Sometimes, the correct use of time is obvious: there are certain hair-on-fire problems that should be addressed with a sense of urgency, otherwise there won’t be anything to scale. However, the picture isn’t always so clear, and there’s usually a tipping point beyond which solving only short-term issues (while ignoring mid-longer term things) comes with a large (and often untenable) future tax.

Biotech is no different. Organizations are always faced with dilemmas about these things, like when to put a system in place for centralizing files vs letting scientists blaze through as many experiments as possible, without worrying about structured formats or naming conventions. While there is no golden rule to follow here, it’s important to remember that (1) as a biotech, your moat is in your data and your ability to tell a story with that data and (2) investors and other partners will need to be able to connect the dots between what experiments you’re doing, why you’re doing them, and what you found.

That’s why it’s worth spending a bit of time understanding what’s out there (in terms of tools and best practices) and how it can help you do better science. You may decide that you don’t want to change anything for now – that's fine. But you'll also likely come out with a clearer understanding of when does make sense to adapt. Or you may find that there are some simple, lighter changes you can implement today that will pay large dividends down the line.

Shifting tides in leadership: science meets tech-nativeness

Increasingly, we’re seeing a new type of scientist step up to leadership and management positions in biotech. For the first time in history, there are people with enough scientific experience and credibility to lead teams, who have also actively used software in most aspects of their lives.

Whether professionally or as consumers, these individuals know the difference a powerful piece of software can make. At the same time, they have enough experience in biotech to understand the context of the challenges at hand. This puts them in a unique position to coalesce two points of view: an appreciation and appetite for great software, grounded in a cognizance of the complexity of science.

As these shifts continue and this wave of bottoms-up change propagates further, we anticipate an increase in the appetite bio teams will have to change how they work. And not just when it comes to new software — people, culture, and behavior are key to unlocking meaningful change for much more fundamental things, such as proper data hygiene and organization. In a world with increasing types of work, volumes of data, and complexity of problems, this creates exciting opportunity.

People don’t just want better software, they also want guidance

A pattern we increasingly observed across the board, from the early demo stages of customer interactions all the way through ongoing customer support: biotech teams want to be active learners, not just blind consumers of software. This educational component pops up in a variety of ways: sometimes, we are asked point-blank for feedback or recommendations about a given flow of work, while other times we expose potential areas of improvement incidentally.

Regardless, it’s clear that many biotechs turn to us as thought partners, rather than merely software providers, in their efforts to pioneer novel R&D. And perhaps this shouldn’t come as a surprise — after all, scientists are curious by nature and the field hinges on collaboration. So while we continue to work hard to deliver purpose-built software that is powerful, beautiful, and intuitive to use, we’re also happy to act as sounding boards or advisors on a variety of both low and high level data strategy.

This is also why scientists form such an integral part of the Kaleidoscope team, allowing us to leverage our domain expertise across bio, data, and engineering to more holistically help our customers. Fun fact: over half of our team has previously worked in biotech or Life Sciences research.

Decision-making patterns are not always predictable

We interact with a very wide range of companies when it comes to things like size, stage, or therapeutic modality. Even when controlling for these factors, it’s interesting to see how different companies approach decision-making, when it comes to something like picking what tools to use.

Some organizations are bottoms-up in their selection of software, and allow for a certain degree of flexibility in who decides what they use. Other companies are very top-down, instead preferring to centralize decisions to a few core, internal roles. Many are a combination of the two, depending on what the tool touches or who it affects. This presents an interesting challenge for many players in the space, especially ones that are focused on innovation at the intersection of different teams.

It seems unlikely that there will be a one-size-fits-all approach (at least in the short-medium term), meaning providers will need to account for this and be adaptable. At Kaleidoscope, this has led to us examining how we engage with customers, and finding ways to enable different types and depths of product use.

Budgeting for software: an iterative process

Software is still a relatively nascent category of spend for much of biotech. Unlike consumables and services, many companies are still trying to figure out how to appropriately value and incorporate software into their financial planning for the year. While getting sign-off on things like FTEs or consultants is a well-trodden path, software approval processes can vary significantly from company-to-company.

That said, something we noticed more of in 2023 was companies putting concerted effort into figuring out how to navigate this important category of spend. From estimating budget by looking at comparables in other industries, to doing the math on how much the resulting time saved is worth, in dollars, teams are learning and adapting. We’re hopeful that this trend will continue, as software becomes more integral to how the best science is done, and as software providers get better at helping connect the dots between purchase and outcomes.

Onward!

Every challenge brings opportunity and this last year revealed challenges and opportunities alike. We’re grateful for the trust our customers put in us, and for the in-person time we get to spend with them. After all, what better way to witness how incredible and rapidly-changing the field is than with front row seats. With 2023 behind us, we’re thrilled for what the new year will bring!

If you’re interested in anything we wrote or want to find out how to partner with Kaleidoscope for the exciting year ahead, let us know here.