We’re entering an era where “more data” is no longer an advantage - unless the workflows are ready for it.
We’re entering an era where “more data” is no longer an advantage - unless the workflows are ready for it.
One thing that’s become increasingly clear to me - first during my PhD, and even earlier during my internships in drug manufacturing - is how differently production and R&D approach progress.
On the manufacturing side, everything depends on strong foundations: standardization, quality control, clear processes, and continuous improvement. You see very quickly that consistency isn’t bureaucracy - it’s what makes automation reliable and scale-up possible. It’s what keeps products safe, reproducible, and compliant.
But in R&D, we often operate in a completely different world.
During my time in academic labs, and later while working with industry teams, I saw how experiments are often treated as one-off creative acts. Every dataset comes in a slightly different shape. Insights might live in individual notebooks, email threads, or someone’s memory.
It feels flexible and inventive in the moment, but you only realise the cost later - when you try to compare results or build something systematic on top of it.
And this gap is widening as our tools get better.
Modern instrumentation now produces metadata-rich datasets. High-throughput platforms scale the number of parallel reactions to an unprecedented level. And with increasingly sophisticated process analytical technologies (PAT), we can measure almost everything at incredible resolution and speed.
But our ability to interpret that data hasn’t kept pace.
It’s not a question of scientific talent. It’s a lack of structure, consistency, and the foundations needed for data to become knowledge.
The more time I spend with teams across pharma and chemicals, the clearer the pattern becomes: the bottleneck isn’t our measurement capability. It’s the workflows surrounding the data - how it’s collected, structured, reused, and aligned.
Manufacturing solved this decades ago because it had no choice.
R&D is now reaching the same inflection point.
The future belongs to the labs that combine scientific creativity with manufacturing-style discipline: clean processes, reliable automation, standardised data, and continuous learning.
