Evolving Your Lab's Data Model Alongside Your Science

As your biotech's scientific needs change, don't let your data systems get left behind. Here at #TeamKC, we understand the importance of lab software flexibility. 

Implementing a new laboratory software system is an important step for moving your biotech forward, but the process does not stop there. As your lab grows, and your scientific needs change, it is crucial that your systems evolve in concert with your science to ensure that your research never skips a beat.

One of the most common problems we see in both startups and established biotechs is a strong implementation that has remained largely static since go-live. But scientific organizations are dynamic by nature. No two biotech labs are the same, and no two teams within that lab are the same either. Therefore, a one-and-done lab software solution just doesn't cut it. The data model should be iterative, just like your science. Continued optimization past the initial launch is key to help companies grow. For example, let's say your lab has a platform team, an analytical development team, and a formulation delivery team, all working on different aspects of the same research. As your scientific focus shifts from one team to the next, or even grows over time with the addition of teams or a redirect in target focus, the data model should be flexible enough to evolve with your science. 

We often hear of an initial implementation focused on one or two original teams, and when the company grows, each team will be tracking data in its own unique ways. As a result, your data management system needs to be managed so that it's efficient for each individual team to use, as well as easy to transfer to other teams over time. Otherwise, you run the risk of introducing inconsistent processes, inaccurate data inputting, and lost samples during handoffs – All things that can be avoided with the right systems in place. The key is to treat your lab's data model as its own scientific process. Through identifying your system's necessary fundamentals, and collaborating directly with your scientists and engineers, we help shape a solution that is highly structured but also flexible, one that we can iterate and refine as needed. 

A strong data model should anticipate change in a proactive way, not a reactive one. This means building functions within your model so that it can support emerging technologies without requiring disruptive rework. Instrument upgrades, automation initiatives, or other additions should be solvable with incremental adjustments and without needing full-scale rebuilds. Future-proofing your data also helps ensure clean, standardized data for downstream analytics, AI/ML, and regulatory reporting.

Here at Karchem Consulting, we're not a one-stop shop. We form deep, long-term partnerships with our clients. Our goal is to grow alongside your biotech, and as your science expands, we'll make sure your lab software is right there to keep up. As scientists ourselves, we are able to understand your research, no matter how complex it is, and our technical expertise means we can design the perfect system for your lab. On top of that, we maintain flexibility so that as your research needs pivot, we can jump in and scale or adjust any aspect of the software to grow with you. We believe that the best data tools aren't necessarily the ones that are the most intricate or have the most bells and whistles, but rather the ones that are continuously optimized and strategically aligned with each specific lab.

Ready to implement a lab software that evolves hand-in-hand with your science? Contact us at Karchem Consulting today to get started.