ERT’s Drew Bustos and Dr. Santikary discuss the challenges sponsors face due to increased demands for data, and examine how modern data platforms, cloud-based technologies, and artificial intelligence can provide potential solutions when incorporated into data integration and management plans.

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Drew Bustos, Senior Director of Business Intelligence Products, is joined by Dr. Santikary, Vice President and Global Chief Data Officer at ERT, for a discussion of the role of modern data architecture. Clinical trial sponsors and CROs are facing increasing numbers of complex data integration and quality challenges. Are you struggling to keep up with this exponential data growth? The solution may lie in modern data platform, cloud, and artificial intelligence technologies.

Data Architecture Challenges in Clinical Trials & Healthcare

Data architecture challenges can be significant, and often include data security; data privacy and protection at scale; data integration at scale; real time reporting and analytics at scale; and data governance and master data management at scale. A modern data platform can be built to handle these challenges.

Common Data Integration Problems

Integrating data and serving it to the end user in one, centralized location is easier said than done. Different vendors and technologies, using disparate platforms, make the integration process even more difficult. In addition to a lack of standardization, unstructured data and binary data further compound the data integration and architecture challenges.

Emerging Trends in Data Architecture and Technologies in the Clinical Trial and Healthcare Industries

The rate of change in clinical research and healthcare technology is unprecedented. In particular, artificial intelligence and blockchain technology are making a huge impact in clinical research.

The State of Artificial Intelligence and its Clinical Trial Application

Sponsors and CROs can expect to see a major shift toward embracing AI in the pharma industry over the next few years. In fact, this trend has already begun with the use of predictive algorithms, chatbots, and voicebots in clinical trials. Artificial intelligence has the potential to accelerate drug discovery and increase trial efficiency.



[Intro]: Welcome to the Trial Better podcast series. This week we’ll discuss modern data architecture, the challenges of data integration and the future of artificial intelligence in clinical trials. Join our host, Drew Bustos and featured guest, Dr. Pakriteswar Santikary and stay tuned at Trial Better.

Drew Bustos: Welcome to the Trial Better podcast series. We’re delighted that you are joining us today. I am Drew Bastos and I lead in business intelligence product management at ERT. And with me today, to discuss the role of modern data architecture, is Dr. Santikary, vice president and global chief data officer at ERT.

Santi is a data evangelist, an accomplished technology executive with over 20 years of experience and he’s earned a PhD in computer simulation from the Indian Institute of Science and post-doctoral research at the University of Michigan. Now, Santi speaks regularly, he is published, he is covered everywhere and he travels all over the world speaking regarding these topics. Welcome, Santi. Now, please, tell us about your role at ERT.

Dr. Prakriteswar Santikary: Thanks, Drew. My role at ERT is driving data strategy, analytic strategy, AI strategy, and cloud strategy. I am the chief data officer that oversees the strategy and the execution of these various pillars of our strategy.

Drew Bustos: It’s exciting. It’s an exciting time. We know that sponsors and CROs are facing increasing data integration and data quality challenges while conducting their studies. Study protocols are becoming more complex due to regulatory demand that requires you to collect even larger data sets across multiple endpoints. So, it’s no wonder why we are struggling just to keep up with this exponential degree in life sciences. Santi, what are the emerging trends in data architecture and data technologies in the clinical and healthcare industry?

Dr. Prakriteswar Santikary: Data architecture is evolving really fast because of not only the data volume but also data variety and data veracity. So, it’s a big data problem that requires different kinds of tools and technologies. That’s why we are building all our platforms on the cloud. That way we can build platforms at scale and also employ newer technologies as business problems demand.

And to that end, we are employing AI tools and technologies, we are using different types of big data technologies, different back-ends, not only SQL databases but also non-SQL databases. So, we are fit for purpose. If your business requires certain technologies to be employed, we are ready to employ those technologies, including cloud.

Drew Bustos: That’s great. So, a lot of exciting parts living in our industry, what are some of the data architecture challenges?

Dr. Prakriteswar Santikary: The data architecture challenges are too many. One of the big challenges that we are solving as part of building our data platform is dealing with the data integration from data from various vendors, from various systems, and also bring that data in one place in a common way for consumption. It’s easier said than done because these vendors and their technologies, they don’t cooperate so there is no standard per se.

Then we deal with, not only structured data, we also deal with unstructured data and binary data. So, when we try to bring all these into one place, such as in our case enterprise data link, the architecture challenges get compounded. Another big challenge is making that data available real-time for analytics, for reporting, you name it. And the third big challenge is employing modern artificial intelligence techniques on top of these integrated data sets. So, it requires a very platform-centered approach and which my office has undertaken.

Drew Bustos: What are some of the products that your team is building?

Dr. Prakriteswar Santikary: The data products that we are building, it all starts with integrating the data. So, we are building a data platform at scale in the cloud. We call it operational data store, that brings all internal and external data set in one place. We are also building mass data management because that ties everything together. And then, overall, since we are dealing with not only structured data but also unstructured and binary data, we are also building an enterprise data lake.

Drew Bustos: Yes. I have heard about data lake for many years. So, Santi, what are the challenges in implementing a data lake architecture in healthcare?

Dr. Prakriteswar Santikary: The primary challenge in implementing a data lake architecture in healthcare has to do with making sure the data platform is architected with data security, data privacy and data protection in mind while enabling real-time data transmission, collection, ingestion, and integration at scale. Not to mention, challenges in dealing with unstructured and binary data in the data lake cannot be underestimated.

From the data lake architecture perspective, supporting both batch and real-time data integration and business intelligence are the real practical challenge. Making integrated data available to all constituents in a self-service manner is another big challenge.

Drew Bustos: So, everything that you are saying, Santi, seems to indicate that integrating data is complex. Can you elaborate a little bit more on that?

Dr. Prakriteswar Santikary: Yes. Just to give you an example. Let’s say, a given clinical trial on an average uses six to twelve vendors, on an average. Sometimes it can go as high as 20 even. A clinical trial also gathers not only structured data but also unstructured data, like data from documents, data from images, data from — You name it. All kinds of data sources that a clinical trial needs to get data from.

Now, as a data platform group, we are on the hook to bring all these different disparate data into one place, And one system, while it works very well within the confine of that system, but when you ask that system to work with another system, that’s where the problem arises because they were built in silos, they were built at different times by different R&D teams, by a different product management team.

So, as a data integrator, we always face those data integration challenges because their semantics don’t match, their meanings don’t match, their volumes don’t match. So, that’s basically where the problem arises.

Drew Bustos: Very interesting. Now, you also mentioned master data management as one of the products your team is building. So, how did your team architect the master data management for this new data platform?

Dr. Prakriteswar Santikary: Good question. A master data management is the glue, as I mentioned, that binds all the disparate systems together and it is the key architectural component of all overall data architectural foundation. Our enterprise data lake is a consumer t our MDM platform which collects all master entities, such as study, site, patient and sponsor, customer, etc from all of our operational and transactional systems and masters them in real-time using sophisticated matching and merging algorithms, major data management, and semantic matching.

We also have data stewards from business side from product management group who oversees manual matching and merging data quality-related issues and all data from business honesty and accountability point of view. We have also created a data governance council that is cross-functional in nature. This council draws data expertise from across the organization not just from R&D. MDM is a strategic initiative within our company as is our enterprise data lake.

This data lake platform sums up all business intelligence reporting, analytics and AI across the entire company, across all business lines, enabling us to create smart data products and opening up new revenue channels for that company.

Drew Bustos: Now, we’ve heard about artificial intelligence for a number of years, so where are we now, in terms of the state of artificial intelligence in healthcare and what are some of the benefits that can be derived from AI in clinical trials?

Dr. Prakriteswar Santikary: Artificial intelligence may be a buzz word but it is certainly not a new phrase in the industry. It has been around since the mid-1950s, believe it or not. It underwent what is called Winter AI; nothing more in between, about 30 years. In my experience, I have employed AI while I was leading the data engineering group at eBay, for example, even before that. So, to me, it’s not new.

What is new, however, is employing those techniques within the confine of clinical trials. Because as you know, Drew, this particular industry is very compliance-focused, very regulatory-focused. So it takes time for newer, more cutting edge technologies such as AI to become mainstream.

However, things are changing and changing very fast. There are a slew of start-ups, for example, that are focusing exclusively on employing AI to accelerate drug discovery and increase clinical trial efficiency. Companies such as ERT are employing AI techniques to transform the clinical trial industry as well.

Predictive algorithms are being employed to predict clinical trial risks which patients are likely to drop out of trials, which patients are likely to be non-compliant with the study protocol. We are also doing chat boards and voice boards that are being used to improve patient engagement ad quality of data collection during the course of the clinical trial.

We are also tackling manual over-read process through employing image recognition technique including unsupervised neural network. These are some of the very early examples that are really going to transform the clinical trial industry using AI.

Drew Bustos: So, what do you think AI will go in the next five years in the clinical trials industry?

Dr. Prakriteswar Santikary: I expect a major shift in strategy over the next few years when it comes to pharma embracing AI; it has already began. ERT, for example, is a deserving clinical trials using AI related to centralized trial monitoring, risk indicator analysis, image analysis. Conversational AI, on the other hand, is another area where AI techniques are being employed in areas of patient engagement.

Processing medicine is another area where AI has a huge potential to disrupt. From an operations point of view, ERT is employing, for example, chat board and voice board to streamline internal customer care efficiency as well.

Drew Bustos: Well, that wraps up today’s podcast on the role of data architecture and clinical trials. Our expert, Santi shared some great insight on how today a modern data platform can be utilized by life sciences companies to streamline their collection, aggregation and clinical reporting and analytics.

And look into the future how artificial intelligence and machine learning presents a great opportunity for cutting edge technologies to make data aggregation and analytics more intelligent. Thank you, Santi, for joining us today, and to our listeners for tuning in. Please be sure to share today’s podcast and visit

[Outro]: Special thanks to this week’s host, Drew Bustos and our featured guest, Dr. Santikary, for your discussion on the future challenges and opportunities in clinical trial data. To our listeners, thanks again for joining us. If you heard something you like, please, shoot us a message at [email protected] or leave us a comment below. We’d love to hear from you. And don’t forget to join us for the next edition of Trial Better.


Prakriteswar Santikary, PhD is an accomplished technology executive with over 20 years of experience in building distributed systems, platforms and applications using techniques of modern data architecture, distributed computing and cloud computing. In his current role, Prakriteswar leads the strategy and execution of ERT’s global data architecture, data integration, business intelligence, advanced analytics, data governance, master data management and data science. He earned his PhD in Computer Simulation from Indian Institute of Science (IISc) in Bangalore, India, and post-doctoral research at The University of Michigan and is the recipient of multiple national and international research fellowships.

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