Data & Analytics Spotlight: Converting Bytes of Data into Cents of Value with Manik Gupta

How do organizations squeeze value from big, complex, “hairy” data? Ask Manik Gupta, who cut his teeth on 72 petabytes of network data at one of the largest telecommunications companies in the world. If 72 petabytes were a movie, you’d have to watch 24/7 for 680 years to see it end-to-end.

Data has always been at the core of Manik’s career.

At Bayer Consumer Health, he was North America Chief AI Officer, and he oversaw multiple AI Communities of Practice. At AT&T, he led the foray into Big Data Monetization—all before AI became a thing.

Today, Manik continues to convert bytes of data into cents of value at transformative scale as Group Vice President of Data Core at Surescripts.

Our conversation has been edited for length and clarity.

Chris: What attracted you to Surescripts?

Manik: Our noble vision at Surescripts of “helping healthcare heal itself” inspires me every day. I love that healthcare serves the communities we live in, so that people and pets can be healthier and happier.

Chris: You lead our Data Core team. Can you share what that is and why it was created?

Manik: When I started at Surescripts in September 2024, it became clear that we needed to bring disparate technical data teams into a cohesive organization to drive integrated capability across the value chain of data and analytics. So, we brought together our Data Architecture, Database Engineering, Cloud Data Engineering, Data Science/AI and Business Intelligence teams into Data Core. This move helps support our data management framework, which prioritizes accuracy, trust and stewardship.

Our “North Star” in Data Core is to enable authentic intelligence and knowledge across the American healthcare ecosystem and drive 10x value for internal and external customers over five years.

Chris: What are your priority goals in 2025?

Manik: To enable our North Star, we’re leaning into specific foundational and transformative priorities.

Foundationally, we’re reinventing each of our five communities of practice to be modern and fit-for-purpose. For example, our Data Architecture team has recently implemented a scalable, flexible medallion architecture within Google Cloud Platform for developing future-state data products and services. Similarly, our Business Intelligence practice has an ambitious plan to create a new customer-centric portal with user personas and design thinking. We’re also investing in new skillsets on our teams while retraining our existing workforce for cloud enablement.

For our transformative priorities, our Data Science and AI team has built an alpha version of our first market-facing product in record time, and our Data Engineering team has built a DAG (directed acyclic graphs) factory to automate complex data pipelines, again in record time.

Chris: What comes out of achieving these priorities?

Manik: For internal use cases, we expect to see significant improvements in the uptake and usage of data and analytics services to run our core business while delivering cost efficiency.

For external use cases, we’re building data products that address important but unmet market needs for patients and the rest of the healthcare ecosystem. One example is insights into first-fill medication adherence. What’s the fill rate on new prescriptions? Are patients compliant with treatment protocols? These questions matter because poor adherence ultimately drives costs up in healthcare.

In both internal and external cases, our Data Core practitioners follow existing security, privacy and compliance frameworks, which are at the heart of our trusted network.

Chris: How are you leveraging AI so far at Surescripts?

Manik: We’re (1) using AI “co-pilots” to help us code faster and better; (2) building a process to vectorize over 6,000 legal documents to better understand and tag key elements; and (3) testing natural language processing (NLP) capability to read clinical informatics data.

We’re also engaging key stakeholders to reduce our administrative burden, for example, by using AI agents to autonomously approve expense reports that are aligned with company policies and guidelines.

Chris: How are you balancing the risks/rewards of generative AI tools?

Manik: We’re focused on the responsible use of AI at Surescripts to deliver the right data at the right time for the right purpose, grounded in ethical use, privacy and security, transparency and accountability.

Chris: You recently got back from GoogleNext2025 in Las Vegas. What did you learn?

Manik: GoogleNext25 is a marquee event for Google Cloud Platform customers and partners on a wide range of topics in technology, data and analytics, but “agentic AI” dominated the conference. Agentic AI is a significant advancement because it enables systems to act autonomously. Traditional AI often relies on pre-defined rules, whereas agentic AI learns from experiences and adapts in real time. Agentic AI could be a boon for applications that require dynamic responses, as they do in healthcare.

Chris: Was data science always on your radar for a career?

Manik: My undergraduate degree is in engineering and my graduate degrees are in business management. I’ve had the privilege of working in various facets of data and analytics for amazing companies across multiple verticals, but over the past 12 years I’ve been focused on leading large-scale data and analytics transformations in the telecommunications and life science industries.

Chris: What advice would you give to someone who wants this type of career?

Manik: Where do you want to play in the world of data and analytics? There are so many career tracks available today: solutions architecture, data engineering, data science, and so on. Take five to seven years and gain experience in multiple areas to see what you like best. Be curious. Build your professional network. Get deeply familiar with one or two industries and how business is done there. Keep learning. Sign up for harder assignments, do hackathons, and take classes online. Own your career, advocate for yourself, and share your story.

Chris: You’ve got a bunch of books on the shelves in your home office. Are they all about AI?

Manik: My home office doubles as one of my two personal libraries. I have a range of books that help open my mind to different possibilities in life, and yes, my desire to learn goes way beyond AI. I just read The Psychology of Money: Timeless Lessons on Wealth, Greed and Happiness, by Morgan Housel. It’s about the difference between being “wealthy” and being “rich.” These two aren’t the same. As Housel explains, being rich means high income and status symbols. Wealth, on the other hand, created through savings and investments, gives you freedom of choice, with more control over your time. It’s a simple but powerful insight.

The parallel in data and analytics is that many organizations are rich in data but poor in insights. True “wealth” in this context comes from treating and managing data as an asset.