‘Data is our lifeblood; our people the heart’

How big is the CDI globally? And how important is India’s contribution? 

We have about 500 employees, and an additional 900 through our functional service provider partners. In CDI India (sixth global site), we have crossed 100 within one year. India worked in collaboration with Oxford (the covid vaccine is sold under the brand names Covishield and Vaxzevria) on several trials. 

And we performed a lot of the data-related activities—the cleaning, the preparing of regular data for submission to the regulators. It has enabled us to deliver over 3 billion doses around the world, across 180 countries. We will further invest in India, based on AstraZeneca’s business needs. 

What are the strengths of the CDI India team? 

Our India CDI has a strong programming group. This supports us, especially on how we manage data. Also, when I was talking about the integration of data management, this group (CDI India) supports us in terms of setting up of databases, edit checks, and custom functions so that we have our database set for production, and we can start collecting data faster. Thus, we get the patient data early. 

 How many women do you have on the team? 

I’m proud to say we’ve got 42% of our workforce that are females in India. The skill sets mirror those of other employees— programmers, data scientists, data managers, risk-based quality managers, and standards developers. We believe that diversity is our future. And we’ve got people from 22 different states. 

 At AstraZeneca, you have spoken about bridging the gap between science and patients. Can you tell us more about that? 

In terms of data, I can talk specifically about how we’re doing that in our centralized monitoring space. When we design a study, we look at the risks that could occur in that study. And we identify any critical data that needs to be monitored to ensure quality. In each study, we will develop a suite of tailored analytics and dashboards that monitor those critical data points throughout the study. There are predefined flags or triggers if we approach the quality tolerance limits. Over the last eight years of working in this model, we have really developed and honed our analytical capabilities, and this has resulted in being able to improve the quality of our studies. While some studies will still fail because the drug is not efficacious, they shouldn’t fail because your data doesn’t have quality. How is data being used to help in developing medicines and getting them faster to the market? 

Data is the lifeblood of the organization. Our people are the heart. Data is critically important in any medicine that you develop, because if you don’t have good data, you don’t have a product. In recent years, we have focused on making our trials more patient-centric and using technology like digital devices to collect data directly from the patients, in a less intrusive way. Digital presents different opportunities for us on the backend because the amount of data created is exponential, when compared with the volumes that we have been collecting in the past. Here is where AI (artificial intelligence) has already started to have an impact in our daily work.