Data Analytics Chat

 How To Create Data Standards In Pharma

• Ben Parker • Episode 62

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In this episode of Data Analytics Chat, host Ben welcomes Priya Gopal, a seasoned professional who has worked with major pharma companies like Takeda and GSK and recently founded her own consulting firm, Inferential Data LLC. 

 

Priya shares her journey from a hands-on database developer to a strategic leader in the field of clinical research. She discusses the importance of cultural readiness for transformation, the role of data standards in drug discovery, and the challenges in implementing these standards across global teams. 

 

Priya also provides insights into balancing regulatory requirements with internal data governance and the potential for cross-industry collaboration to drive innovation in pharma. 

 

00:00 Introduction to Transformation and Leadership

01:23 Guest Introduction and Career Journey

04:14 Defining Moments and Leadership Insights

10:20 Challenges and Lessons in Pharma

20:34 Creating Data Standards in Pharma

28:42 Balancing Global Regulations and Internal Governance

34:18 The Role of AI and Automation

39:42 Cross-Industry Collaboration and Future Opportunities

44:36 Conclusion and Final Thoughts

Thank you for listening!

Priya:

Transformation isn't always about technology or process. It's more about the team's cultural readiness, organization's cultural readiness. So we'll be building the ship and sailing at the same time. Yeah, so to me, actually, leadership is not a title. You know, it's more of definitely a mindset. What helped me grow is the deep commitment. To continuous learning and, and also genuinely being curious about innovation. The true leadership is not about having answers for everything. It is to enabling others to succeed. Grow. Grow and come along with you in that journey. So drug discovery is a very high stake journey in every clinical trial. It's a story of risk, hope. And cure for a disease that where patients are waiting for those medications and data is the storyteller. Governance is never static. You know, it's always continuously keeps changing

ben parker:

Welcome to Data Analytics Chat, the podcast where we discuss the world of data, ai and the career shaping it. My guest today is Priya Gopal, who's a newly founder and has worked for Global Giants, including Takeda and GSK. In this episode, we'll explore her journey, the challenges and insights she has learned during her career to date. And the data topic will be on how to create data standards in pharma. Priya, welcome to the podcast.

Priya:

Thank you, Ben. Thanks for having me.

ben parker:

Not problem and you've worked for some fascinating companies. So do you wanna share by yeah. List providing your career journey?

Priya:

Sure. So my career has always been anchored in a deep love for data. Especially in the context of clinical research. So I began as a database developer and a SaaS programmer. Back in the days when, you know, data was more structured in clinical trials. Um, so those early years actually taught me to appreciate the regulatory guardrails in clinical research and the importance of data. So over time, as technology evolved. I. Pretty much grew alongside with it. I learned all the new analytics tools, cloud computing, ai, and I stayed close to the details and I always experimented and I was hands-on. So that actually helped me to open doors to lead implementation of data platforms and innovation across clinical trials in major pharma companies.

ben parker:

Okay. But, um, so what was the, how did you get into the industry anyway?

Priya:

So I got into the industry because I, you know, I found this job about clinical research in general. And, you know, so I got in as a programmer and then. Being a programmer in clinical research, you also need to learn the business space because you interact quite a bit with end users. So that actually gave me the, you know, insight to learn the overall end-to-end process in clinical research, especially in the r and d space, and as well as contribute and, create analytics, develop databases. Understand how data management is done and how standards play a role and the regulatory requirements for clinical research.

ben parker:

Okay. And so OC, as you've progressed, how have you sort of learned to lead teams, do the strategic side has that, how have you gone about that?

Priya:

So as as I like to just point out a couple of. Very defining moments in my, you know, career that helped me with that. Because as I first, as I mentioned, I was always hands-on. So when I was more hands-on, it helped me to know what I need and when there is a strategy that's being implemented. I could relate very well with the implementation of that, right? Like how would you implement it and scale it in a sustainable way? So that was very helpful for me because once you, you are in the trenches, you know that, you know, what are the challenges you will face and how to mitigate them while you're working on it.

ben parker:

Okay? And then. I mean, how did you find moving from being the technical person to like leadership? Because obviously that's a big change, isn't it, in career?

Priya:

Yes. So when, from the Post, because I had the insights on the technical space and I understand how implementation works and how the Strat the underlying strategy to do the implementation, that actually helped me to steer myself more towards the strategy space because I knew the. How, what needs to be done? The how part was a little, you know, easier to understand, like, okay, what kind of. The problems are we are trying to solve and how we will go about solving that. So that really helped me to be honest. And then when we started set, creating strategic initiatives, strategic directions it helped me to understand the risk versus the benefits and what kind of strategy would work in long term. And then how do you go about. Putting a process around that strategy so that it would be more sustainable for a major initiative or a big transformation for the industry.

ben parker:

Okay, brilliant. And then, um, obviously you've mentioned some, uh, moments in your career. Is it, could, has there been any other, can you pinpoint any other sort of defining moments in your career that's sort of really impacted you?

Priya:

Yes, absolutely. So the first one was, leading a data standards function in a multinational pharma company. So I actually had an opportunity to build a in-house capability from ground up, uh, by assembling and guiding a global team. Actually this, this experience taught me how to design and scale global strategies, right? And it also helped me to see how to optimize delivery models and be sustainable across the board, especially when you are delivering at scale for multiple trials across multiple therapeutic area. And this also actually I had a great experience in that space because it also helped me to work across team members in different parts of the globe. And it gave me an opportunity to really be part of the cultural diversity. And also see how important it is, especially when you're communicating across cultures to make sure that the communication is at the right. Space and people understand, the team members understand what they need to do and they're, you know, successful, uh, with the, uh, with their work and they're able to implement the strategies as needed. So that was one of the good moments that I had. And the second one was actually came to me, uh, more from a real world, uh, data space. So this is an opportunity that, uh, helped me to develop strategies that bridge the gap between point of care, meaning hospital data, uh, versus clinical research. So it actually helped me to see the insight of how data flows across hospitals or clinics. A clinical trial and it gave me a very different perspective to see as to how the data that we collect in the hospital can compliment the traditional research space and how you can understand the unmet medical needs for the patients, and that can in turn accelerate the drug discovery.

ben parker:

Okay, brilliant. And yeah, I mean the, obviously the farm industry a challenging one isn't our, there's so much red tape, I guess, and it's obviously the data's extra sensitive, isn't it? So it's, you've gotta make sure that is safe.

Priya:

Yes. We, I mean, especially when. When you are trying to look at the data from a clinical research space versus the data coming from a hospital setting, there is a very different perspective there. I mean, all these data is anonymized, you know, uh, especially when it comes from the hospital space. So the da, uh, you wouldn't have all the, any of the patient identifying information, but the underlying in data about like the patient's disease indication and, things that they're going through with their health and how research can really help them to, you know, come out of that and what kind of drugs would help them with that.

ben parker:

So what, um, I guess what's been the tough challenges you faced in your career?

Priya:

Oh, thanks for that question. So one of the most rewarding experience in my role at a major pharma company was leading an insourcing initiative, a strategic insourcing initiative. So part of this initiative was to build a new team, again, ground up, and transitioning some of the critical work streams that has been outsourced for a long time. The effort actually required, not just introducing new technologies and processes, uh, but also to drive alignment with functional teams. Because they're very much deep rooted in the legacy ways of working this implementation actually, you know. The new practices have been successful. We implemented it successfully. But the cultural transformation did not happen as much as we thought it would. And the organization definitely has a lot of potential, but you know, the change was hard for the organization to adapt. Actually this experience. Helped me to understand a key lesson, uh, actually reinforce the key lesson. Transformation isn't always about technology or process. It's more about the team's cultural readiness, organization's cultural readiness. And actually this also prompted me to reflect on my own path. Like where do I want to go? What drives me? And that in turn helped me to launch my own consulting firm, inferential data, LLC, and to help me know the organizations to architect data strategies to implement al approaches and how to accelerate innovation. Because I'm an independent consultant, I also had the freedom to, stay close to the strategy and the execution of it to see how we can make things go faster for my clients in a very sustainable way.

ben parker:

Brilliant. And I, I love your motivation and, um. I guess risk taking to do your own thing because it's obviously, uh, obviously I've, I've got my own business. I know. It's, it's a lot, it's a lot of work. It's challenging, but yeah, so obviously wish you all the best for that and it's enjoy it. Like it's, it's fun. Um, obviously you've got great experience, so it's just, yeah it's the fun and games in the startup.

Priya:

Yes, thank you that it's definitely very rewarding and you see different aspects and different perspectives of, uh, strategies and as well as the implementation approaches that can, could be tailored better and could be done differently for, uh, the different use cases. So it, it's a very, very nice experience to have.

ben parker:

And then, um, so obviously you've got the opportunity to. I guess build a or scale a team up. So that's a big challenge. How, I mean, how did you overcome sort of Yeah, then those hurdles or was it a lot of uh, mentoring with seniors or how did you go about sort of, overcoming that, uh, business project?

Priya:

Yes. Actually it, it was a very interesting approach. So with the transformation initiative, when you know, and I joined the pharma company, uh, one of the thing that was very clear was that yes, we had to do it at pace and we wanted to. Bring in everything in faster. So we'll be building the ship and sailing at the same time. So with that said actually I had very good mentors in the organization who also helped me to. Think with across the board from financial, from operational, from a process standpoint, from cultural standpoint, all the different perspectives. So that actually helped me to put together a proposal, uh, as to what kind of teams would be needed, what kind of team members I would need. What, what are the skill sets? How do you make a nimble team? Deliver on a big initiative. And at the same time, how do we make sure that, you know, we are, we are doing it at pace and it's sustainable throughout the life cycle. So that actually really helped me to see the sustainability as well as the operationalization of a strategy and also think about the downstream impact because I have been doing the work hands-on for a long time I was able to foresee some of the challenges and see and put some risk mitigation before it become a bigger problem to solve.

ben parker:

Yeah, I think it's a amazing experience to achieve in your career because it's a completely different skillset to a, like leading a team, like I guess more management team. Building a team is completely different skillset than like just management, isn't it?

Priya:

Absolutely. I built the team as well as I managed it, so yes, it's totally a different, uh, uh, skillset. For building a team, you need to know. Different aspects of the process, right? Like you need to understand the strategy from different lenses. So only then you'll be able to build a sustainable team from managing a team. You need to know what is the, how is the current team, uh, what is their ongoing structure and. What is their delivery model and what are, what is that they do right. And how they add value to the organization. And then take it from there. So it's slightly different. But I felt like it's, at least, uh, from my perspective, it's very rewarding when you have an opportunity to build something from ground up.

ben parker:

Yeah, definitely. Yeah. So what key skills or mindset would you f. It's helped you progress into leadership.

Priya:

Yeah, so to me, actually, leadership is not a title. You know, it's more of definitely a mindset. What helped me grow is the deep commitment. To continuous learning and, and also genuinely being curious about innovation. I usually don't just look at things only in the pharma space. I look at it across other domains, like what kind of technology process or advancements that are happening you know, in other domains like financial domains or you know, some other domains. So. That helped me to see okay, what, how that could be eventually tailored into clinical research with the guardrails for that is needed for clinical research. Um, so this actually helped me in a lot of ways to think strategically so that strategic. Thought and the strategic mindset definitely helps to move towards a leadership role. But to be, in my mind, if you want to be a leader, you definitely need to have some hands-on experience only then you'll understand the challenges of any strategy that's being crafted by senior leadership, and you'll be able to. Identify the implications of those decisions, how that would affect or help to move the organization forward. So it's more like integrating the vision with execution, and it's always staying close to the signs and the data and as well as the team members, the people who are moving it forward.

ben parker:

Brilliant. And I guess, obviously now you're experiencing leadership. And I mean, what advice would you give to yourself as a technical expert and then for anyone obviously wanting to aspiring to be, become a leader?

Priya:

Sure, yeah. So leadership, I mean, it's a continuous journey. It's, you know, it's of learning and growth. It's always being, staying curious. Uh, trying to learn, learning nervous jobs and exploring like different innovations both in process, in technology and you know, what kind of strategies have worked for other organizations and how that. Uh, be brought into your organization and what is the cultural fit of that? So there are different aspects of it. So to me, if someone wants to be a leader, I would suggest, you know, for them to think holistically, right way in every decision with multiple lenses, like, uh, the financial viability of a decision, how to operationalize it, and what is the cultural fit of it. Be transparent, if the, everyone makes mistakes. So own your mistakes. Be transparent, celebrate your team's wins. Empower your team. Build a culture where people can safely speak up their opinions and ideas and. Your ideas also gets challenged so that way it can be better refined and made more, more viable for the organization. And building trust, having that openness and between the team members, uh, helps you to build trust and it's to be honest, the true leadership is not about having answers for everything. It is to enabling others to succeed. Grow. Grow and come along with you in that journey.

ben parker:

Brilliant. Some great insight there. Okay, so let's move on to the data topic. And it's gonna be looking into how to create a data standard in pharma. I guess it's. Matt is in every industry and it's essential for scaling, amicus, innovating with confidence. So I guess, do you wanna give a bit of context around data data standards, and I guess why do they matter so much in the pharmaceutical industry today?

Priya:

Yeah, absolutely. So drug discovery is a very high stake journey in every clinical trial. It's a story of risk, hope. And cure for a disease that where patients are waiting for those medications and data is the storyteller. And, uh, the data is what tells the story about the whole journey. So data flows in into a clinical research, in a clinical trial from different sources, heterogeneous sources from various hospitals, from labs, even from devices like digital devices. It's all in different formats, and each source has its own formats, its own terminologies, and you know, without a shared language, the story becomes fragmented. So that's where data standards come in. It's a unifying language of. Clinical data, it makes sure that every stakeholder, whether it's a clinician, a statistician, or even the regulatory agencies, everyone interprets the data the same way and it brings in more clarity and consistency to the process. It is, I mean, data is more foundational and that's, they make the story of the new medicine. More reliable and, you know, more readable and make it impactful for the community.

ben parker:

Okay. So is it just making it, I guess just a, like a format, just easy to follow sort of thing?

Priya:

Yes. You know, to me how I see data is, you know, the standards is what would make the harmonized model and the guideline of how that harmonized model should be so that every data speaks the same language and you're able to see and understand it the same way

ben parker:

across the boat. Okay, cool. So what are the. Biggest changes pharma companies face when trying to implement data standards.

Priya:

So pharmaceutical companies you know, understand the value of standards. There, the, you know, the issue is the challenge. It's beyond technology, it is more like cultural. So some things are like, you know, there are some comp, some PLA companies where. Standards is more like a regulatory afterthought because currently most of the regulatory agencies, including FTA mandates that if a new drug date is submitted, a data is submitted to them for approval. It should follow a standards called CDIs or Clinical Data Standards Interchange Consortium. So CDIs is the mandated standard. So it, it should follow that. And so what happens is when a lot of companies, what they do is they do it more like an afterthought just before they go for the submission. They try to retrofit the data into that standards so that it meets the submission requirements. This is a very, you know, reactive approach and the actual. Power of standards is to drive efficiency and increase quality. So if it is embedded much earlier, then it really helps to, bring in more scalable approach, more interoperable and reusable approaches for these study. So that is one challenge that you know, happens in the industry. The second one is of course, you know, even when standards are embraced and used by the organizations it's a constant evolution. So these evolution, of course, you know, there is constant chi churn, there is constant change. And, you know, retraining, retooling, doing impact analysis on the existing standards versus the new ones. So this. Often happens, and there is definitely, you know, if, if the teams are resource constrained this creates more fatigue and, you know, a lot more work for the, uh, clinical study teams. Then the, another challenge could is around the disconnect between the teams that actually design the standards and the team that actually implements it. So if they don't work hand in hand, if the teams that is designing the standards if they don't understand the full clinical workflow. And what are the different types of heterogeneous data that comes in, and if there are any legacy systems, how is that being worked out? Then the standards that gets created is, you know, not very usable for the operational teams. And then there is definitely inconsistencies, rework, and all that. Then one other challenge that I have seen is around like, the goals of different functional teams. Every team, you know, the vision is not shared that. Standardization is needed across the board, within the organization, then it becomes lot more harder to actually implement it right from the get go of any clinical trial. So then it becomes a more of an afterthought, like I said before, and, you know, it doesn't really bring in add value. So having that leadership. Sponsor, uh, sponsoring the effort is very important to see how to make this more holistic and how to make it more useful for the organization.

ben parker:

So sort of ownership's really important then.

Priya:

Yeah.

ben parker:

Yeah. And at this, obviously, obviously you work for some global companies. Is there a challenge. On like location.'cause obviously different parts of the world, you're gonna have different results, aren't you in sort of guess more regional or global? Does that, is that sort of a challenge for businesses?

Priya:

So to be honest, from a standards perspective, um, it, it, I mean there are two types of challenges, right? So one is from a I mean everyone needs to. Know the standards, because especially in the pharma space, it is, it's a mandate by the regulatory agencies. Mm-hmm. Even different agencies, not just FDA. So that piece of it, I think the teams are aligned irrespective of where they are located, in which part of the world. But the challenge could be. When trying to implement it within each groups, like if the implementation teams are globally, are globally located trying to understand that, you know, like the way they understand how the standards are created and how that should be implemented sometimes have that challenges if the, for global teams especially.

ben parker:

Okay. Brilliant. And then I guess, how could pharma leaders balance global regulatory requirements with internal data governance frameworks?

Priya:

Yeah. So actually when we talk about internal governance in pharma we are actually looking at. Two dimensions, two different, two kind of interlocking dimensions. One is like the strategic and the operational governance, so how we design and execute in clinical trials. Then the second is the data governance, like how do you manage, protect. And use the data that's generated by the trial. They both have to work hand in hand only. Then there is, you know, a good implementation and a trustworthy data that could be submitted. For drug approval. So in, um, global regulation especially, right. Those from like, uh, the International Council for Harmonization, IC H guidelines, they do have, provide guidance for clinical trial harmonization. Um, actually these guidelines are particularly, uh, the latest one, I think the iic, HE six, R three it's. It's very good. It's a blueprint of, you know, building some robust, uh, inter internal governance for the organization. They can actually use, use it to build some of the internal governance. There is a, a whole section on how to look at risk-based quality management. And it encourages the sponsors to identify. Critical data and the processes around that and assess the proportional risks so that they can design mitigation strategies. Ahead of time and, you know, it doesn't wait for the trial to be executed to later on understand the risk and then scramble to fix it. Then the other one is, you know, regulations also require the data to be, you know, accurate. It needs to be complete and relatable. This is not like. You know, like a checkbox. Yes, it's accurate or complete reliable. You, you cannot do a checkbox, but it's mostly like trying to see what kind of things might affect these requirements, right? Like in a clinical trial, when a trial is getting designed. Uh, example could be when a clinical trial is being set up, say for example, that trial requires frequent lab visits by the patients. And maybe, you know, there are a lot of patients who face ti travel time, like where they have to go to the lab in person once a week, for example, and it's a patient burden. So if there is a patient burden, there's definitely going to be problem with the data because then you're gonna have incomplete data. Good governance. It should anticipate this risk and mitigate it ahead of time so that when you're designing your trial, you have some sort of mitigation strategy to avoid this patient burden so that you would get complete data. So that is, you know, one way of thinking about it. Also there is, to me these regulatory principles can be used as a good springboard so that the, you can build the internal process for the entire data life cycle from collection. Still, collection, validation, retention policies, everything can be built on top of what was given as a regulation by the, um, in a, by, uh, the regulations that are available at the moment. So those are very good frameworks that could be used. And then on top of it, to build additional insights like organizational specific governances. So that way it is more holistic across the board. And governance is never static. You know, it's always continuously keeps changing. So when you have a good governance, then it definitely helps to bring in more of the you know, like, tools and technologies in place, and you would know how to use that within the organization. Like for example, if Chat GPT is brought in or you bring in a another AI platform so you know what can be shared within that and what is confidential and what could. Inadvertently expose some of the company's assets or a ongoing trial data sensitive information. So those are the important questions that could be looked at. If there is a, a good. Uh, governance in place and how that gets built out. So that's, uh, and this is always a continuous training, uh, making sure the organization is aware of all this, and that is, how do you increase the literacy, like more of the data literacy within different stakeholders in the organization. How do you. Bring in the change management as well as how do you explain you know, the governance and the underlying need for it. That's, it's a very it's different from organization to organization and it needs to be tailored based on the culture of that, any organization for that mattered.

ben parker:

Okay. Interesting. And then, so I guess the rise in sort of automation and AI has really had a dramatic impact on this then.

Priya:

Yes. I mean, you could make it more you know, bring in AI and you know, automation. But as I mentioned, to even bring in ai, you need to know what you can and cannot do. Yeah. You know, so you do not want to take a new clinical trial protocol and feed it to an AI because then your information is public. So things like, that's where the governance comes to play. So that's where you need to know how to use the technology in the best way possible so that you can actually use the power of technology to make things easier. For the organization and for the whole work stream. If that is not understood well enough, then there is more confusion and things don't go in a, things don't go in the best way and it's less than optimal.

ben parker:

Okay. And so where would you see the biggest opportunities for innovation when companies adopt these strong data standards?

Priya:

Yeah. So when companies adopt like, strong data standards, they actually, uh, at least to me, I feel like it's a very powerful engine for innovation. You know, it's not only drug discovery even for the whole research pipeline, right? So because if you have a found data standards, a foundational data standards then it is more easy to scale up. Um, new studies and it is easy for the organizations to, you know, understand how the data is traveling, what can be reused and how you can generate more value out of the data that you have. So that's, that's one of the basics of, having a good data standards. And then because, you know, if you, the another, uh, bigger opportunity is definitely around the efficiency and reuse. So if the data is, you know, if it's standardized, either it's within a particular therapeutic area like, you know, oncology or immunology or, doing it across for all studies across different therapeutic area. It still increases efficiency because you can set up things faster, you can replicate studies faster. You can reduce duplication across the board and you know, you're not reinventing the wheel every time. So that would mean you need less number of, uh, resources working on studies. It could be more optimized. So those are some of the very fundamental advantages of having a good. Uh, data standards. Another important thing that, which I noticed uh, while I was working in one of the big pharma is standards actually help to uncover. Patterns, data patterns. You know, there were, uh, studies in the past where you would've used a medic, uh, a drug was actually tested in a particular disease. Say like melanoma, for example. But if the, you know, when the data is standardized across the board. When you are using different, uh, you know, when you're trying to combine data from different indications, then what happens is you really see that whatever was, a particular drug which is actually helping a disease, like say melanoma. May also be, you know, able to help other cancers, you know, like, head and neck or lung or other, you know, where you could actually reuse the existing medication across cross indication. So to see whether that is possible and what would that mean? You need to have the data speaking the same language. So that is when it is easier. And to your point, you know, that's where actually AI comes to play, because then if you feed in all this information to an AI platform, then you would see those patterns come up, you know, and it would be, uh, it's, it's actually very helpful and it is both for the organization and for the patient community.

ben parker:

Oh, nice. And is there a particular, is there an area that would feel the benefits first, like research and development or clinicals or,

Priya:

um, so usually it's research and development because that's where they first start looking at a molecule level to see whether that molecule has a potential to scale. As well as to see what is the unmet need in that particular therapeutic area and how could a molecule be, used for it, right? So it is very, uh, it'll, it's actually very useful in the, um, research space because, you know, that's, that's where the thing starts. So it's, uh, much useful in that space. Yes.

ben parker:

Brilliant. And then what role should cross industry collaboration play in creating and evolving pharma data standards?

Priya:

Yeah. Actually, you know, it's definitely needed cross industry collaboration is absolutely needed. So it's like, you know, pharma companies, regulators, even technology providers. Everyone, you know, need to come together because each of them have a very unique perspective of how of data, and each of them looks at data with a different lens. So if we are building as holistic standards, everyone needs to come together so that it's meaningful. And, you know, when stakeholders as align and build the standards that works for the whole community. But you know, especially now, we see there's a lot more therapies that are coming into play. We see a lot of digital tools used in healthcare space. So constantly we have to revisit to see what should be done. How do we make sure that. Things come to play and, you know, how do we, uh, help to build a a standards that is holistic and that could be used and understood the same way across the board. One gap actually, to be honest, that I see, which probably has a lot of potential, it is a bit of a disconnect between the standards that is used in clinical trial versus the standards that's used in point of care. You know, I'm hoping that both I mean they both are centered around patient. End of the day, patient is the center for either it is drug discovery or it is. Patient care. But they operate in, of course they operate in different environments. Clinical trial is more controlled. It's a curated data enrollment, it's protocol driven. And the collection is very, you know, structured in how the the protocol mandates it. But once when the drug is in the market. It is the real world that's using it, you know, and it's far less structured. And so then we had to figure out the divergence of, how this medication that is in the real world could. We used in clinical research for long-term purposes, like, you know, if we can uncover any patterns or what could be done differently and how could the, you know, the gap between the point of care and research could be reduced. I think that is definitely a big potential there. And with, uh. Data standards can become a bridge, A good bridge where, you know, the researchers, the clinicians, everyone is looking at it using a common data format, common terminologies. I think that would definitely help to reduce the gaps. And it'll also help to, you know, bring in more of the technology. In place for reviewing the data to identify anomalies, to identify patterns, to uh, look at safety risks, all that. So I think that that is definitely a big potential there. And, and I'm hoping as we go through this journey, there will be a lot more collaboration, cross industry, cross-functional collaboration to. Make this a reality. Okay,

ben parker:

cool. And also, I guess it's constant evolution. So how, I mean, obviously once you've got a data standard created, how often are you reviewing it or ch make it amending it? Is it constant? Like, is it, do you, how's it work?

Priya:

Yeah, I mean it, so it depends on the use case, right? So usually there is at least a some standards it's changed. Like some guidelines have changes or updates on a yearly basis, like, uh, an, you know, every year. But if there are some of them that might. Stay a little longer than a year, it's a very use case driven approach. And to be honest, we don't have standards for all therapeutic area in place yet. So, which means, you know, there's a lot more to develop and that's where we need this collaboration. We need the, everyone coming together and making this a priority. You know, that's, that's the most important thing.

ben parker:

Okay, cool. Fascinating stuff. So yeah obviously it's been brilliant having you on the podcast and yeah, I love obviously the career journey so far, and obviously corporate to the startup world, so I wish you all the best for that. And yeah, it's been a pleasure having you on the podcast today.

Priya:

Well, thank you so much, Ben. Thanks for the opportunity and I really enjoyed it. Thanks so much for bringing me and, you know, working with me on this. Looking forward to, you know, seeing how, how this will be useful for the bigger pharma community as well as for the, um, life sciences domain.