Why a fast food conglomerate chose to focus on data stewardship

IT professionals in the restaurant industry are aiming to clean up their kitchens tech stacks to support AI agents.

Yum! Brands, the holding company for fast food brands like Pizza Hut and Taco Bell, wanted to solidify its data foundation to build complex systems like agentic AI on top of the existing frameworks. It enlisted enterprise software company Informatica to help with that effort.

Kartik Pillai, director of data strategy, master data management, and data governance at Yum! Brands, told IT Brew the decision to focus on transforming that foundation was born out of the need to implement AI more effectively.

“It’s about adding more context to the business operations, so as we start building agentic systems, and at some point, we’ll have to start thinking about building self-healing systems as well,” Pillai said. “And a system cannot self-heal if it doesn’t have context around it…When I say context, it’s not just how data is interpreted, but how does the business actually operate?”

Setting up a data foundation, which includes a comprehensive strategy and governance of information stored within an enterprise, is part of the digital modernization process, Gaurav Pathak, SVP of product management at Informatica, said.

Mini bites first. Instead of building bespoke data projects for every single AI agent in an organization, Informatica advises clients to set up a data foundation.

In the case of Yum! Brands, Informatica helped solidify the data foundation by tackling process standardization first, which involved standardizing data entry and workflows to reduce the variability that comes with manual input. After that, the organization implemented master data management and data governance to define clear data ownership and accountability, while also “establishing a data catalog to transition governance into an automated enabler,” Pillai wrote in a follow-up email to IT Brew.

Pathak wrote in the same email that, as Yum! Brands continued to grow, its operations faced “severe operation inefficiencies” and spent a disproportionate amount of time and effort dedicated to manually “consolidating, cleansing, and reporting on location data.” A revamped data foundation would help resolve some of this chaos while boosting the reliability of reporting data across the company’s various brands and markets.

“The project moved beyond basic data cleanup to deliver the golden contextual records that agentic systems require to operate effectively,” Pathak wrote. “The rich enterprise context ensures that future AI applications have the deep, reliable background necessary to understand operational realities and power self-healing systems.”

What’s cooking? While a digital transformation project that puts data management before bigger AI implementation may not be the most exciting thing for software engineers and developers to focus on, Pillai used his interview to stress the importance of understanding an enterprise before generating code.

“Developers need to move beyond writing code in isolation and instead deeply understand the enterprise context in which that code operates,” Pillai wrote in the follow-up email. “At its core, this is about understanding data lineage—how data is created, transformed, and consumed across systems—which is one of the most powerful enablers of building reliable and scalable software. Without that awareness, even well-written code can unintentionally introduce inconsistencies, duplicate logic, or break critical downstream processes.”

Data quality and data governance is now part of Yum! Brands’ build process, release, requirements gathering, and overall software development life cycle.

“You’re setting up a support engineer on a website, you want it to be as good as your human support engineer or better. If you just give it the product support documents and cases, that does not make it as good as the support engineer that was already there, who knew the customer better, who knew all their projects better,” Pathak said, adding: “All those things are data points that need to be explicitly fed to the agent with all the surrounding technical context, and I think the organizations are starting to get that.”

Takeaways. Pathak added that, while this foundational digital transformation work doesn’t directly impact the bottom line, the C-suite is becoming more interested in it as a means for implementing agentic AI. The data foundation is also scalable, necessary for growth and innovation.

Pillai wrote that IT professionals interested in building data foundations should understand the following:

• Data quality isn’t always a technology challenge; it’s a business discipline. Without trusted data, investments in AI and digital transformation can fail to deliver full value.
• Governance doesn’t mean bureaucracy; instead it should define ownership and quality.
• Building a scalable data foundation necessitates anchoring initiative in clear business value that brings stakeholders into the conversation.
• Execution should focus on baking data quality assurance into workflows through reducing manual effort.
• Consistent and clear communication as well as stakeholder engagement can establish an enduring capability.