Sramana Mitra: Are you saying that your primary value add is managing the infrastructure of data processing?
Ashish Sharma: Yes, but a lot more beyond infrastructure. There are a lot more data sources available than ever before. These are growing at an exponential rate especially for life sciences and financial companies. I’m using the term financial companies very loosely. I’m referring to insurance, banking, and capital markets.
You need to be able to experiment and explore these new data sources to uncover whatever value or competitor advantage that those data sets offer you. With deep expertise in pharmaceutical data sources or banking data sources and with deep experience in data science for a very targeted use case and with a very deep technology experience of deploying and operating such Big Data frameworks that facilitate the end outcome, we end up becoming the right partner for majority of the Big Data initiatives for these businesses.
We help them leverage diverse datasets into their studies. How do you drive accuracy and insights using machine learning or the Big Data framework? How do you create operational efficiency so that my analyst can do 30% more work because they’re spending 30% less time on data preparation.
Because of what we have delivered for leading pharmaceutical companies around the world, we have about eight different data projects going on. This is a claim that not many companies in this niche space can make. I’m speaking less as Axtria and more as what I end up driving as one of the principals in the company.
Sramana Mitra: Let me ask you to comment specifically on the needs of life sciences customers. What is special that you’re learning about life sciences customers?
Ashish Sharma: Life sciences is one industry where you have three customers: payer, provider, and patient. If you look at all three customers, patient is the end consumer of what you have to offer, but before they can consume what you have to offer, they need expert advice from the provider.
What the provider can recommend for the patient to consume is influenced by the payer, where the payer is the insurance company. If you look at these three customers, it creates a different dynamics and different needs. The end outcome that we are driving for these industries is that cost of care needs to be reduced. That takes you to the next step of how do you reduce cost of care for a hospital.
One of the ways to look at it is that if there are patient-connected devices, they can be sent home rather than keep them in the hospital because that machine is tracking vitals of that patient. Someone needs to take that data and with their domain knowledge, be able to triage that sensor data and make sense out of it in a way that a physician can make a decision on that.
Enabling such end-to-end use case helps you reduce cost of patient care because that patient can now be at home and get the desired help as it spits out data that leads you to some kind of an alert mechanism, which is enabled by either calling an ambulance or having a nurse call them and advice them on what to do at that time. That’s one use case.
Sramana Mitra: Where are you encountering this use case? Is it the hospital that is implementing the use case? Is it the insurance company that is requiring it? Where is the drive of this adoption coming from?
Ashish Sharma: In life sciences, the early adopters are the providers. When it comes to the space which is more driven by pharmaceutical companies, it’s either more at the clinical trial level or if they have come up with some innovation. They’re testing end-to-end viability and how that will drive any decision-making process. From a payer point of view, it plays an excellent role because payers are the first ones who have skin in the game to reduce the cost of care. It is important to all of them. It requires a certain level of collaboration.