The Lab of the Future: Finding the Right Recipe for Success

The Lab of the Future: Finding the Right Recipe for Success

How to shift from a human-centric approach to a compute-centric one


By Eric Jones, PhD, Enthought CEO

 

The R&D laboratory of the future is here, and it’s powering scientific innovation and discovery faster and more efficiently than ever before. Yet in a 2021 survey of 200 global laboratory leaders, 64 percent admitted they weren’t investing enough in intelligent, connected technology, and 69 percent believed they would lose their competitive advantage if they didn’t find ways to connect and automate their labs. Of the science-driven companies who have started their digital initiatives, many are failing to achieve their connected lab aspirations as legacy systems with siloed data, insufficient resources, missing change agents, a growing skills gap, and a limited line of sight to business value hamper efforts.

Despite these roadblocks, companies can and should prioritize upleveling their labs—particularly as competition in both existing and emerging markets is more intense than it ever has been. If organizations are going to realize the full potential of digital transformation, they must take a step back and think about the R&D lab differently. They need to shift from a human-centric approach to a compute-centric one.

Read the full article in Lab Manager here.

More resources about building the Lab of the Future here.

Share this article:

Related Content

Making the Most of Small Data in Scientific R&D

March 9, 2023|Life Sciences, Materials Science, Transformation Making the Most of Small Data in Scientific R&D For many traditional innovation-driven organizations, scientific data is generated…

Read More

ChatGPT on Software Engineering

Recently, I’ve been working on a new course offering in Enthought Academy titled Software Engineering for Scientists and Engineers course. I’ve focused on distilling the…

Read More

What’s in a __name__?

if __name__ == “__main__”: When I was new to Python, I ran into a mysterious block of code that looked something like: def main():  …

Read More

Why Python?

Why Python? Of all of the questions that I have been asked as the instructor of an Enthought Python course, this has been one of…

Read More

3 Trends for Scientists To Watch in 2023

As a company that delivers Digital Transformation for Science, part of our job at Enthought is to understand the trends that will affect how our…

Read More

Accelerating Science: the Classical Mechanics Perspective

When thinking about enhancing R&D processes, Newton’s second law of motion provides the perfect framework. Classical mechanics teaches us that putting a body into motion…

Read More

Retuning the Heavens: Machine Learning and Ancient Astronomy

What can we learn about machine learning from ancient astronomy? When thinking about Machine Learning it is easy to be model-centric and get caught up…

Read More

Announcing Enthought Academy

Dear Students and Friends of Enthought,  I am pleased to announce Enthought Academy—the culmination of over twenty years of teaching Scientific Python. Since our founding…

Read More

Extracting Target Labels from Deep Learning Classification Models

In the blog post Configuring a Neural Network Output Layer we highlighted how to correctly set up an output layer for deep learning models. Here,…

Read More

True DX in the Pharma R&D Lab Defined by Enthought

Enthought’s team in Japan exhibited at the Pharma IT & Digital Health Expo 2022 life sciences conference in Tokyo, to meet with pharmaceutical industry leaders…

Read More