Lowe’s is one of the largest home improvement retailers in the world, serving millions of homeowners, DIYers, and professionals across more than 1,700 U.S. stores. With a mission centered on helping customers “solve problems and fulfill the dreams they have for their homes,” Lowe’s sits on a vast universe of operational, product, customer, and spatial data. As digital expectations rise and AI begins reshaping how customers interact with brands, the company is doubling down on data as a strategic differentiator in its next chapter of growth.

In this first installment of CDO Magazine’s three-part series, Chandhu Nair, Senior Vice President, Data, Artificial Intelligence & Innovation at Lowe’s, speaks with Rohit Choudhary, CEO of Acceldata, about what it means to lead data transformation at scale — and why the future of enterprise AI depends on mastering three foundational layers of data.

Redefining the role of a data leader in the age of AI

Nair reflects on how repeated digital transformation waves shaped his philosophy on data leadership. “Digital-native businesses can harness data, make quick business decisions, and drive outcomes with it. That gave me my initial thinking around putting data at the center of the equation.”

But with AI accelerating at unprecedented speed, the data leader’s mandate is expanding far beyond platforms and pipelines.

“The impact of AI is truly around creating specialized intelligence,” Nair says. “That happens through your data that is core to your business, your customers, and your associates.”

He emphasizes that the transformation is far more holistic than most teams initially expect.

“It’s not just a technology journey or a data-management journey. It is a leadership and change-management journey. Especially with data, you have to test your way into a lot of things. That’s what makes the work exciting.”

Why data is mission-critical for Lowe’s right now

For a company whose purpose is rooted in helping customers repair, build, and dream about their spaces, Lowe’s sees enormous value trapped within underutilized data assets.

“There is a lot of whitespace opportunity,” Nair notes. “Some data is captured but not used, and some isn’t captured at all. On top of that, you have huge volumes of existing data that can help drive intelligent business decisions or create the best experience for customers and associates.”

“Take the simple act of fixing a leaky faucet. That expertise sits in a lot of unstructured data,” he explains. “We’ve had it, but we weren’t truly able to utilize it until the power of generative AI came to fruition.”

The same applies to more immersive customer experiences. “If a customer is dreaming about a kitchen, how do you make that dream come to life? Visualizing that requires spatial data about the home. There is so much untapped potential,” Nair elaborates.

By combining structured retail data with spatial, visual, and expert knowledge, Lowe’s sees AI as a multiplier for both operational efficiency and customer inspiration.

Lowe’s 3-layer data mix: Structured, Unstructured, and Uncaptured

When asked how the company’s data breaks down across different types, Nair offers an approximate distribution — one that reflects both opportunity and challenge.

“About 30 to 40 percent of our data is in structured databases that you can harvest,” he says. “Probably another 30 percent is unstructured. And then there is probably 30 percent more that is uncaptured — in the tribal knowledge of our associates in stores or operations centers.”

The biggest unlock, he argues, is in bringing these layers together. “How do I collect this uncaptured data through experiences and then combine the power of structured and unstructured data? That is the total data transformation.”

All three streams are evolving and accelerating

Across the enterprise, Lowe’s is driving simultaneous modernization efforts across structured, unstructured, and uncaptured data. On the structured side, Nair says, “We had to modernize our platforms, move to the cloud, and empower traditional AI, machine learning, and deep learning.”

On the unstructured side, Lowe’s is now unlocking decades of operational expertise previously buried in documents or human memory. One example is the Mylow Companion, an AI assistant for store associates that blends SOPs, sales guides, and structured operational data to deliver real-time recommendations.

“You can now take all of that unstructured information along with structured data about how your store is running today, and combine it to provide the right recommendations,” Nair explains.

But the real leap — the one that separates AI-enabled companies from AI-native companies — lies in harnessing the third layer.

“If you truly want to be AI-native and think like a CEO starting a business today, you need to look at all the unstructured, unpacked, or uncaptured data and combine all three. You need an AI strategy that covers all aspects.”

He summarizes Lowe’s framework succinctly: “That’s the way I look at the three layers of data — from doing things, to embedding things, to truly being AI-native.”

CDO Magazine appreciates Chandhu Nair for sharing his insights with our global community.