For decades, the pharmaceutical industry has excelled at discovery and development while stumbling over its own data. In an era defined by data science and artificial intelligence, this weakness is more than inconvenient: it’s costly and inefficient. Regulators expect a level of process and product understanding that is significantly slower and more costly to deliver without stronger, smarter data connectivity. The promise of Quality by Design (QbD) is only achievable when we break down information silos.
The irony is that no one in pharma needs convincing. Every executive wants to move away from siloed systems, but (despite heavy investment in data products and data lakes) the problem persists. These tools may have improved access, but they have not enhanced the precision or speed of decision-making. Companies remain awash with fragmented and overwhelming information. The bottleneck isn’t access. It’s purposeful data integration that turns information into knowledge.
This matters because the stakes are so high. Consider investigations; in my experience, at least half the effort goes into simply gathering the right data. A significant proportion is spent stitching it together, cleaning it, and engineering it into a usable form. Only the sliver of time that remains can be devoted to actual scientific analysis – the very activity that drives discovery and innovation. This inefficiency is not just a nuisance, but slows the pace at which life-saving therapies reach patients.
The risks are equally stark. Input material quality drifts or process drifts may seem mundane, but they are the most common deviations in pharmaceutical processes. Left unchecked, they can culminate in late unforeseen discoveries or specification breaks, leading to deviation investigations or worse devastating product recalls. If better data connectivity can shorten the time from detection to action, then patients, providers, and the industry itself stand to benefit.
At the same time, the demand for automation and autonomy has reached critical mass. Autonomous manufacturing is no longer a distant ambition but the number one topic on the minds of many pharma executives. Autonomy, however, cannot thrive in a fragmented data environment. To build intelligent, adaptive manufacturing systems, we must first solve the connectivity problem.
That requires a cultural as much as a technical shift. Instead of hoarding data within divisional boundaries, companies should make data flow horizontally across the end to end business processes. Organisations should also consider sharing obfuscated or non-competitive information across CDMO or CMO partners and ultimately across the industry. In the case of supply chains, where bottlenecks continue to throttle output, collective intelligence could transform how companies plan, source, and distribute. Better optimization across the sector would not only reduce risk but increase resilience at a time when global health depends on it.
The road ahead is clear. We have the science, the technology and data security methodologies, and the regulatory framework pushing us toward a more integrated future. What we lack is the focus and will to tear down walls that no longer serve us. Data silos are a relic. The industry’s next great leap will not come from another blockbuster molecule, but from the ability to harness, integrate, and act on the data we already hold.
Data connectivity is the catalyst that will accelerate discovery and development, optimise manufacturing, and strengthen supply chains. Delivering better outcomes for patients. The future is right there on the other side of the data wall. Let’s tear it down.
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