There's no point in trying to hide it: Aligning processes and integrating tools in a drug discovery organisation is often a bit of a hassle. It requires resources and discipline, which is why it often doesn't happen.

However, that doesn't change the fact that there is a lot of value to be gained downstream if you lay the necessary groundwork to achieve the goal: Connected tools and a unified data model that makes it possible to enhance research efficiency and collaboration across the organisation.

Why is alignment even important?

Full alignment on processes and tools might sound like the optimal way of working in a drug discovery organisation. And if it was possible to achieve that there would be great benefits to reap on multiple levels:

It’s easier and cheaper to provide support in an organisation with fewer tools and aligned processes than in an organisation where each department works in its own way.

The better the processes and tools are aligned throughout the drug discovery process, the faster you can complete each task. This means that compounds move faster to drug development and ultimately, new treatments reach the market faster.

And most importantly, it will naturally create alignment around data, formats, and vocabulary, so you can compare data across assays, departments, and organisations.

But…

While full alignment on tools and processes might be ideal in some departments of the company, imagining that everyone works the same way and produces data in the same formats in a research organisation is a utopia. And not necessarily a desirable one.

Leave space for innovation

In a research organisation you need to allow a bit of wiggle room so the scientists can focus on innovation and experimentation. Each department or lab should be set up to do its job in the best way, so alignment cannot be at the expense of data production.

So when we acknowledge that differences will always exist in a drug discovery organisation it becomes a matter of finding the most efficient way to build a bridge between these differences.

In other words, integration of tools becomes key. The focus should be on having a common data management system to which data producers - internal as well as external - can deliver data in an aligned format, for example via Excel data templates.

What's the best way to get started?

Unfortunately, integration of tools and alignment around data doesn't happen by itself. It requires a management decision to prioritise the task, and it requires an investment in laying the foundation.

To support the process, it's a good idea to appoint one or more data managers who understand the discipline, assays, data and, not least, what the goal of the whole exercise is.

You need someone who can drive the process forward and also create data templates that the various data producers can use to deliver data in the right format - in cases where their production does not deliver in this format.

Lay the foundation for better decision-making

Prioritising time and resources to change tools and processes in a busy day-to-day discovery organisation can at first seem like an administrative exercise that steals time away from the "real" work. And it's true that the benefits should be seen in a long-term perspective.

But when you do, integration and alignment actually becomes a way to do the "real" work faster and better. Seamless data flow and interoperability make laboratory operations more efficient.

And when project managers, data scientists as well as AI and knowledge graph tools can easily access and analyse data without a lot of copy/paste work in Excel and post-processing of data, you’ll lay the foundation for data-driven decision making and speed up your drug discovery process.