The 'too early to track' fallacy

When does it make sense to start tracking your work? In this Kaleidoscope blog, we explore a common misconception in biotech R&D: that tracking things only makes sense when you're operating at larger scale. If anything here resonates, please reach out!

A strange misconception has taken root in R&D: that tracking data and projects only becomes necessary past an arbitrary threshold. Many people claim that early-stage R&D is too chaotic and unpredictable to track anything effectively, and that later-stage development naturally lends itself more to rigorous, structured schemas for tracking and managing data. This false dichotomy has led many teams in our industry to adopt an all-or-nothing approach to documentation (in the broad sense of the word), often to their detriment.

We've all heard the justifications. "We're too early-stage to worry about documentation." "Tracking everything will slow us down." "We'll implement systems when we scale up." These sentiments echo across biotech teams, reinforcing this idea that meticulous record-keeping is a luxury reserved for older companies that have more established operations and can spend the time and money adhering to structured processes and software. 

But this mindset is flawed. It's possible to be diligent in your processes from day one, while also accounting for the fact that your current work might not scale or be entirely useful later on. The key lies in finding a balance between tracking and flexibility.

To give an internal example: although we’re not a bio company, we faced a similar challenge in the early days of Kaleidoscope when we were tracking customer conversations during our market discovery phase. As soon as we started having exploratory conversations with potential customers, we implemented Airtable as a basic customer relationship management (CRM) tool. We created a basic set of labels, categories, and note-taking systems, even though we had no clear idea of what our final product would look like or if these conversations would even prove useful to us later on. As we grew, built our product, started scaling our sales engine, and increased the amount of conversations we were having, we outgrew our DIY Airtable system and transitioned to Hubspot, a more robust CRM.

It would’ve been really tempting to fall into the trap of thinking, "Most of these early conversations won't be relevant later, so there's no point in cataloging them now." But we recognized that, while not every early interaction would prove significant in the long run, the aggregate data and insights we could pull from consistently documenting these conversations would be invaluable – and they were!

The same approach can bridge this false gap between early and late-stage R&D. If more bio companies implemented flexible yet thorough tracking practices from the start, they would be able to create rich datasets that evolve alongside their research. This way, when it's time to scale up or pivot, you're not starting from scratch – you're building on a foundation of historical data and insights.

Of course, the main concern for many companies is that rigorous documentation takes time, distracts from the day-to-day work, and, as a result, impedes progress. It's a valid worry, especially given the time-pressure to go from idea to clinic, and clinic to market. But, in our experience, this fear often stems from bad experiences with cumbersome, ill-fitting software rather than an inherent conflict between documentation and productivity. The solution isn't to abandon documentation altogether but to implement systems that can easily grow with your company and don’t demand too much time. Start with lightweight, flexible tools that integrate seamlessly with the other tools you’re already using (although finding these tools is easier said than done!). 

It's important to recognize that you'll care about tracking different kinds of information at different stages of your company's growth. Begin by defining some essentials that you want to track early on, and then add to that list as you scale. This approach allows you to take advantage of the structure you've put in place early on, while also building the muscle and encouraging the behavior of documenting things from the start. As your needs evolve, you can upgrade your documentation practices incrementally. This gradual approach helps you build a culture of tracking important data from day one without sacrificing agility. Moreover, it's much easier to instill good documentation habits early rather than trying to retrofit this behavior years down the line.

Adopting this approach, even in early stages, dramatically improves reproducibility later on. It enhances collaboration by providing clear, accessible records of past work. Perhaps most importantly, it can accelerate your time to market by allowing researchers to build more effectively on past successes and failures – especially since it’s very difficult to draw a distinct line about when the ‘exact right time’ to start documenting is.

Also, at a time when the vast majority of biotechs are looking to incorporate AI to speed up the drug discovery process, comprehensive documentation around data can be a competitive advantage and the key to being able to optimize AI models effectively. 

Whenever we meet R&D teams who have the same concerns about tracking things too early, we tell them that today's seemingly irrelevant data point could be tomorrow's breakthrough insight. You never know which thread, when pulled, might unravel a solution to a critical problem. And if you’re intentional about implementing a lightweight tracking system that doesn’t disrupt your progress, you have a lot more to gain than you have to lose. 

If you want to chat more about anything we wrote, or you’re interested in finding a way to work together, let us know!