shutterstock_784918162.jpgSandeep Varma and Raghav Sharma co-wrote this blog post with Vickye Jain.

Pharmaceutical companies have a reputation for lagging behind other industries when it comes to technology, but that reputation now seems unwarranted in many respects. As technology professionals who work with pharmaceutical clients, we feel an obligation not only to share how pharma is innovating, but also to bring those innovations back to pharma. At two recent industry events, the Strata Data Conference in Singapore and the Spark Summit in Ireland, we learned a great deal from the presentations that we attended, and almost just as much from the audience’s reaction to our own presentations. Here are our top three takeaways from these conferences:

1. Leading pharmaceutical companies have closed the digital gap. In our presentation, we discussed how we used the Spark platform to solve data and analytics problems for one of our pharma clients. In my humble opinion, our use of this platform was just as cutting-edge as many of the solutions discussed by presenters from Silicon Valley. Our session was attended by a room full of people who aren’t in the pharmaceutical industry, but the audience was engaged in hearing about our life-sciences-oriented solution, and we fielded many questions.

The sessions we attended made it clear to us that many companies across industries are adopting the same strategies and solutions that we discussed at these events, so while it may still be true that the majority of pharma companies are lagging, it’s not true anymore to say that pharma is behind on the technology adoption curve. Some companies are right on the cutting edge using server-less architectures for data processing, AI powered apps for a variety of use cases, and even their own Alexa chatbots for small tasks. The cloud has levelled the playing field and pharma companies have access to more data than ever, leading to a much smaller lag in technology adoption.

2. DevOps for data management is about to be a thing. Development and software operations (DevOps) is a method of collaboration and communication that links developers to IT. When it comes to software, DevOps is old news, and the benefits are well-known: The DevOps methodology allows you to make sure that whatever software you’re updating can be changed faster, without compromising integrity.

If you think about a DevOps approach for data management, the number of updates needed to data management systems make the benefits just as significant as in software, but the complex variables involved make it much harder to achieve. Speaking from experience, there are no good online resources that can help you adopt a DevOps approach to data management and no standard practices out there. When we developed a DevOps approach for a recent client, we had to come up with our own methods, and we feel obliged to share. After our session, and even after each conference, we received a lot of interest and follow-up questions from people who have struggled with the same needs. Since the need is incredibly high, we believe that it’s just a matter of time before this becomes a standard practice. 

3. Companies don’t need to be convinced to pursue machine learning and data science. They need help managing their algorithms. While we still see a lot of material out there by companies and vendors about the potential of data science, machine learning, and the algorithms and use cases involved in machine learning, at both events we saw a clear shift in client pains. Generally speaking, many companies are up to their eyeballs in algorithms. They have tens and hundreds of algorithms mushrooming in production already, developed in-house or through established and startup partners for problems as varied as drug discovery, predicting trial site failures, personalizing marketing strategies, and so on. But there's no commonly agreed-upon solution for managing so many algorithms, keeping track of their evolution and collaborating within the enterprise for gaining efficiencies. Almost all major vendors have begun to work on this problem but with no clear leaders yet or any particularly effective solutions. We've begun helping our clients think through workable solutions in this area and will continue to do so in the near future.

Someday, we hope to see pharmaceutical experts presenting alongside Silicon Valley experts, an idea that would have seemed far-fetched a few years ago. But if the ever-closing technology gap is any indication, we see this as inevitable. And once pharma has completed its digital transformation, the impact to life sciences will be felt by nearly everyone on the planet.   


BLOG POST: Paging Dr. Watson: Evolving Pharmaceutical Value Propositions in the Age of Artificial Intelligence

BLOG POST: How Technology and Analytics are Helping Big Pharma Improve Patient Engagement in R&D


Topics: technology, data, Pharmaceutical Companies, data science, pharmaceuticals, manufacturers, technology innovation, machine learning, artificial intelligence & pharma, Spark Summit, Strata Data Conference, DevOps