‘Tech is now an integral part of how we work as a company’

How has the role of your GCC unit evolved over the years?

Prior to 2014, we (AstraZeneca) were very dependent on external partners to deliver on our IT commitments, and there was an evolving need to uplift the quality of service to the business. This was when the importance of insourcing our IT capability became very clear. Technology has now become integral to the way we work as a company. Post the successful insourcing of IT in 2014, the entity soon expanded by bringing in additional service lines such as Global Business Services in 2017, R&D (research and development) services in 2018, and now, we also deal with core areas such as clinical data management that was established late last year. Today, India is one of the more than 100 markets we serve as a GCC for AstraZeneca.

How did your unit help with the covid-19 vaccine development?

We are proud of the role we’ve been able to play in the development of this vaccine (the Oxford-AstraZeneca Covid-19 vaccine). A typical vaccine development timeline is 5-6 years but this one was developed within a year. This posed a number of technological challenges such as ensuring the scalability of the systems used to manage every element of the value chain — from clinical trials, procurement, supply chain management and commercial operations. GITC played a very key role in integrating dozens of additional supply chain partners, establishing electronic connectivity, stress testing and modifying our adverse events (like, allergies) reporting systems to handle exponentially higher volumes, designing and implementing new technology-enabled process for expiry date management, etc. Co-location of the Global Commercial Operations and Global IT and R&D services in India enabled rapid acceleration of the project.

Given that you are a pharma and life sciences company, how are you also using visualization tools and technologies like augmented reality (AR) and virtual reality (VR)?

We use a number of technologies like Extended Reality (XR), which includes AR and VR. These have huge potential to transform the entire life sciences industry. We are seeing a number of applications. For example, visualizing how a particular drug interacts inside the body, and how it targets certain types of cells etc. Besides, before patients who come into the clinical trials can get a virtual experience of what to expect at the trial site. By putting on a pair of VR glasses, they can see what every step of participating in the trial looks like such as trial site set-up, tests to be administered, the actual administration of the drug as well as ongoing check-ins. This puts them at ease participating in the trial and is truly a patient-centric way to use this technology.

On the manufacturing side, we are seeing huge applications in the way we train our operators – with VR and even with haptic gloves. Hence, before they have to operate the machine, they can visualize as well as feel everything. Physicians now can visualize how the drug works inside the body and get an idea of ​​the symptoms and side effects. So, instead of a medical representative just handing out flyers, you can actually give physicians a complete experience of how that product works inside the human body and what potential side effects may look like.

How does AZIPL work with your CDI division?

That team (CDI) is very highly skilled. They have a lot of experience in the domain on how to extract actionable insights from disparate data sources using artificial intelligence (AI) and machine learning.

We blend that with the deep technology expertise and the data engineering capability we’ve built. On the IT side, today we are much faster in creating data products. Earlier, when we used to get a problem, we would try to solve it by going into what data inputs were required and building a data warehouse or data lake. But today, we have enterprise data products that exist as building blocks, and we are quickly able to bring them together using Application Programming Interfaces (APIs) to solve a specific business question or problem.

We are also working on data mesh technologies, which basically bring all these data products together allowing us to quickly run data science experiments on top of it—modelling and asking questions and trying to get the right sort of insights for those questions.

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