“Technology is now an integral part of how we operate as a company.”

How has your GCC class evolved over the years?

Prior to 2014, we (AstraZeneca) were heavily dependent on external partners to fulfill our IT commitments and there was a desire to increase service quality to the business. It was at this point that the importance of insuring our IT capacity became clear. Technology has now become critical to the way we operate as a company. In the year Posting a successful IT insurance in 2014, the company expanded by bringing additional service lines such as international business services in 2017, R&D (research and development) services in 2018, and now, we deal with core issues such as clinical data. The administration was established at the end of last year. Today, India is one of the more than 100 markets we serve as GCC for AstraZeneca.

How has your department helped in the development of a vaccine for Covid-19?

We are proud of the role we were able to play in the development of this vaccine (the Oxford-AstraZeneca Covid-19 vaccine). The usual vaccine development time is 5-6 years but this one was developed in one year. This has led to numerous technological challenges, such as ensuring the scalability of systems used to manage each element of the value chain – from clinical trials, procurement, supply chain management and commercial operations. GITC has been instrumental in integrating dozens of additional supply chain partners, creating electronic communications, streamlining our stress testing and adverse event (such as allergies) reporting systems, to accommodate higher volumes, and designing and implementing a new technology-enabled process for expiration dates. Management etc. The co-location of global business operations and global IT and R&D services in India enabled rapid acceleration of the project.

As a pharma and life sciences company, how are you leveraging visual tools and technologies such as augmented reality (AR) and virtual reality (VR)?

We use several technologies such as Extended Reality (XR) which includes AR and VR. These have the potential to transform the entire life sciences industry. We are seeing multiple applications. For example, seeing how a certain drug interacts in the body and how it targets certain cell types, etc. Additionally, patients who come to clinical trials get a virtual experience of what to expect at the trial site. Wearing a pair of VR goggles, participants can see what each step of the trial looks like, such as setting up the trial site, the tests to be administered, the correct administration of the drug, and the ongoing checks. This makes it easier for them to participate in the trial and is a patient-centered way to use this technology.

We’re seeing huge applications in the way we train our operators on the manufacturing side – even with VR and haptic gloves. So they can visualize and feel everything before they operate the machine. Doctors can now visualize how the drug works in the body and the symptoms and side effects. So, instead of a medical representative handing out pamphlets, they can give physicians a thorough experience of how the product works in the human body and what the side effects look like.

How does AZIPL work with your CDI department?

That group (CDI) is very highly skilled. He has extensive experience in the domain of how to extract actionable insights from various data sources using artificial intelligence (AI) and machine learning.

We combine that with the deep technology expertise and data engineering capabilities we’ve built. On the IT side, we are very fast at creating data products today. In the past, when we had a problem, we would go into what data resources were needed and try to solve it by building a data warehouse or data lake. But today, we have enterprise data products like building blocks, and we can quickly bring them together using Application Programming Interfaces (APIs) to solve a specific business question or problem.

We’re also working on data mesh technologies, which basically bring all these data products together and allow us to quickly run data science experiments — modeling and asking questions and trying to get the right insights for those questions.

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