Busting 4 Myths of Digital Transformation in Life Sciences

It’s no secret that science is an ever-evolving discipline. The data, processes, and solutions powering life sciences R&D are in a constant state of growth and flux.

Even compared to a decade ago, today’s life science labs operate very differently:

• Data sets are larger

• Teams are more multidisciplinary

• Projects are more complex

For companies trying to gain a competitive edge with faster time-to-milestone and deeper data-driven decision-making, the only path forward lies in embracing a cloud-based digital transformation. However, for many organizations, those two words — “digital transformation” — surface long-held assumptions that bring progress to a screeching halt.

As scientists, it’s in our nature to challenge assumptions. So, we wrote this post to prove the top “blockers” to digital transformation wrong.

• Switching software systems is too hard

• Digital transformation takes too long

• Teams resist change

• New tools are too disruptive to workflows

These common misconceptions are ingrained in R&D and IT leaders from years of working with legacy vendors. But now, there is a new way. Let’s bust some myths.

Myth 1: Switching software systems is hard

“Bringing a new system into our informatics ecosystem is prohibitively challenging.”

Scientists already know that legacy R&D software doesn’t often pair well with other legacy systems or lab instruments. So, throwing new technology into an already delicate balance is assumed to be too complex. With so many opportunities to lose research momentum along the way, digital transformation doesn’t always seem worth it.

Busted: This implied trade-off simply doesn’t apply to all software. New, cloud-based R&D systems, developed by scientists, for scientists, work the way you work. Now, it’s possible to break through the headaches of multiple systems to unify workflows, migrate data, link lab instruments, and customize your platform in record time.

Learn more about how open APIs and other implementation best practices can make transitioning to a new solution both simple and efficient.

Myth 2: Configuring a new system takes too long

“Configuration and updates take a long time.”

It can take months or even years to make necessary configuration changes to legacy systems. Whether that’s because the code is too rigid or because each vendor needs to be involved in each change, the sheer time it takes is prohibitive.

Busted: Good news; with modern, cloud-based technology, you don’t have to use code or depend on vendors anymore to adapt a system to your needs. Codeless software configuration empowers administrators to customize registration schemas and inventory hierarchies, build dashboards, and tweak workflows without writing a line of code.

Learn more about how codeless configuration can help your systems adapt and evolve as quickly as science does.

Myth 3: Teams resist change

“The new system will go unused.”

It’s a big fear among decision-makers: “What if I invest a huge amount of time and money into an improved system, but my team won’t even use it?” Users often prefer to stay with the status quo, even if it’s suboptimal since the learning curve for new software is just too steep.

Busted: Scientists work best when the systems they use are designed with them in mind. User-friendly interfaces, scientifically intuitive processes, and hands-on training can make new software adoption simple at the user level, while a time-tested Enterprise Implementation Methodology helps entire organizations hit the ground running.

Learn more about why scientist satisfaction can actually increase 225% when you switch to a software system designed for their needs.

Myth 4: New tools disrupt workflows

“Switching to a new system will cause data loss and confusion.”

The amount of data R&D companies generate is exploding by the day. It’s common for critical information to exist in disparate systems and databases and often requires scientists to manipulate data manually if they want to draw important connections. It stands to reason, then, that adding a new system could mean old data gets scattered, lost, or forgotten.

Busted: It’s now possible to store 100% of your organization’s data, workflows, and insights all in one spot. Nothing gets lost, and a searchable, cloud-based platform means that no matter the team or experiment, information is fully traceable – and better yet — usable.

Learn more about why implementing a system with a single source of truth means continuity, not complexity, by reading the full paper.

For life science R&D, the future is now. Adopting a digital R&D cloud platform is the critical next step to deepening your insights and scaling your impact. Ready to take your next step towards making the switch?

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