[eBook] Digital Transformation in the Life Sciences Industry

The Lab of the Future: Barriers to Digital Transformation in the Life Sciences Industry

Enthought eBook: Five Barriers to Digital Transformation in the Materials Science Industry

Experts predict over the next two years, life sciences companies will invest over $3 billion in AI, with two-thirds adopting the "intelligent lab of the future" within four years.

Taking a digital-first approach has become a strategic imperative in the pharmaceutical and biopharma industries. Yet many life sciences companies, big and small, are early in their digital transformation journeys or not yet gleaning value from their existing investments.

Enthought has been digitally transforming R&D for leading, global science-driven companies for over 20 years. We're providing this eBook to help guide your lab's transformation initiatives.

Download “The Lab of the Future: Five Barriers to Digital Transformation in the Life Sciences Industry” to learn:

  • How current industry-specific market trends and pressures are impacting decisions
  • The common barriers to successful digital transformation in life sciences
  • Recommendations on what can be done now to attain the future-proofed R&D lab


Questions? Contact info@enthought.com to discuss how the Enthought Materials Science and Chemistry Solutions Group can help future-proof your R&D lab and accelerate your business.

Click for more on building the Lab of the Future.

Download eBook

Share this article:

Related Content

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

Exploring Python Objects

Introduction When we teach our foundational Python class, one of the things we do is make sure that our students know how to explore Python…

Read More

Choosing the Right Number of Clusters

Introduction When I first started my machine learning journey, K-means clustering was one of the first algorithms I was introduced to – and it is…

Read More

Prospecting for Data on the Web

Introduction At Enthought we teach a lot of scientists and engineers about using Python and the ecosystem of scientific Python packages for processing, analyzing, and…

Read More

Join Our Mailing List!

Sign up below to receive email updates including the latest news, insights, and case studies from our team.