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Sandeep Murali: Bridging Data Strategy and Business Needs in the Pharma Industry

Sandeep Murali is the Global Director of Data Excellence and Business Analytics at Mundipharma, where he leads global data strategy and insights. With a background in digital marketing and over a decade of healthcare experience, he focuses on performance assessment, commercial excellence, and stakeholder engagement, integrating data to drive business outcomes

 

In advance of his appearance at CDAO Frankfurt, we sat down with Sandeep Murali, Global Director of Data Excellence and Business Analytics at Mundipharma. With a background steeped in digital marketing and over ten years in the healthcare sector, Sandeep offers a unique perspective on integrating data strategy with business needs.

In this exclusive interview, Sandeep delves into the common pitfalls organizations face when aligning data and analytics with tangible business outcomes and shares tailored advice for data leaders aiming to optimize their strategies in the fast-paced world of pharmaceuticals. He also discusses the evolving landscape of AI technologies and their implications for productivity in the pharma industry.

Register now for CDAO Frankfurt to hear directly from Sandeep Murali and other leading professionals about the cutting-edge of data analytics transformation. 

 

C: How does coming from a digital marketing background shape your perspective as a data and analytics leader compared to someone with a different background?

Sandeep Murali: The most important shift is the change in mindset. In digital marketing, you're working with very near-term or even real-time data. With streaming data like this, you can make quick decisions and sometimes even implement automated, data-driven actions based on that real-time data.

But when you broaden your scope to include things like financial performance or long-term customer engagement, you have to zoom out and take time to digest the information and generate insights. It’s not always about making decisions in minutes or seconds anymore.

Another aspect is understanding the scale of leading and lagging KPIs in terms of performance. For example, customer engagement metrics like email click rates are leading indicators. They show you're capturing the customer's attention, and over time, this translates into lagging indicators like market share. Everything else falls in between these points.

C: The panel you’re speaking at CDAO Frankfurt is all about measuring the benefits of data and analytics transformation. What do you think are some of the biggest mistakes people make when trying to connect data and analytics transformation to tangible benefits?

Sandeep Murali: One of the most common mistakes I’ve seen is focusing too much on technology rather than the business need. Often, when people start their data journey, they begin by selecting a technology solution, thinking it will solve all their problems. But it should be the other way around—start with the specific business questions you’re trying to answer.

It doesn’t matter where the data comes from or whether you have the perfect technology in place. It’s about identifying what your stakeholders need to know and how quickly they can access that information. This approach gives you a starting point and ensures that the solutions you develop are truly fit for purpose.

First, as with anything, user experience is crucial. You need to deliver insights in a way that's easily accessible for your stakeholders, using the channels or formats they prefer.

For example, if you’re sharing insights with a senior C-level executive, they may not have the time or interest to navigate a dashboard. Instead, they might appreciate a daily digest—like a newsletter—highlighting the top four or five KPIs they need to focus on. On the other hand, someone in a marketing or sales role might want to dive into the details, so providing dashboards where they can drill down is more effective for them.

For salespeople in the field, they likely won't have time to check a dashboard, so sending notifications directly to their iPads can be more practical. The key is that one size doesn't fit all—you need to understand your target audience and deliver insights in a way that suits their needs. This is where some initiatives fail; they overlook how their audience prefers to consume insights.

 

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C: What advice would you give to a data analytics leader for aligning with business objectives when starting a transformation?

Sandeep Murali: First and foremost, listen. Listen to your stakeholders and really understand their concerns. These usually fall into a few categories. One might be that they don’t have access to the information they need, or it's incomplete. Another could be that they get access to data, but it’s cumbersome—like having to email someone and waiting two days for a response. And sometimes, they have the data but lack the context, so they don't know what actions to take based on it.

That’s where I would start—by understanding the major challenges and expectations. This approach is universal across industries. Begin with providing data in context, which we can call descriptive analytics. This helps people understand what's happening. From there, you empower them to derive insights themselves.

Next, you can move toward prescriptive analytics, where the data suggests potential actions based on patterns. And then, you can explore predictive analytics and AI, where you analyze what might happen if you take a certain action. But it’s important to build those foundational steps first.

C: With AI technologies evolving rapidly, especially in such a fast-moving industry, how do you approach the question of productivity when communicating with your teams about AI's potential impact?

 

Sandeep Murali: Being in pharma, we’re often a couple of steps behind other industries when it comes to adopting emerging technologies, and AI is no exception. There are pilots and proof-of-concepts happening across the industry and within our organization as well. But when it comes to productivity, especially in the pharma context, there are a few considerations to keep in mind.

First, we can’t use open-source AI architectures like GPT models directly because any information put into them becomes part of the public domain. Most of the data we handle is highly sensitive, so we need secure solutions, like edge instances or on-premises deployments, which many in the industry are exploring.

Second, the specialized nature of our work means that large language models (LLMs) may not always deliver the most accurate outcomes. They can be useful for generic tasks like drafting vendor contracts, but for tasks like customer segmentation in the pharmaceutical field, more specialized models could perform better. These models need to be trained on specific parameters to improve their accuracy over time.

From what I've observed, consistency is still a challenge—especially in pharma. The models don’t always produce the same results reliably, though we hope to see improvements with further training and model development.

Lastly, compliance is critical. Any model we use must adhere to strict compliance standards. For example, if a model is deployed in our organization, we have to ensure that sensitive information remains restricted. For example, accidental access to restricted financial data when using the AI. These are the kinds of considerations we need to address when using AI in our field. That said, we’re actively exploring AI’s potential, especially around improving productivity.

There is a cultural aspect to AI adoption too, German companies tend to be cautious with emerging technologies. They prefer to explore and understand new developments before fully committing. You might see more of the "fail fast, innovate quickly" approach in the U.S., the U.K., or Asia, but German businesses typically take a more deliberate approach to implementing solutions. It’s about ensuring a thorough understanding before diving in.

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Register now for CDAO Frankfurt to hear directly from Sandeep and other top professionals about the future of data, analytics, and AI