Eaton is an intelligent power management company, with a focus on ensuring the stability and efficiency of global power systems. Specializing in the management of electrical, hydraulic, and mechanical power—in industries from automotive to aerospace and power distribution—the 113-year-old company helps businesses operate more sustainably and cost-effectively. With a footprint in more than 170 countries and $23.2 billion in sales in 2023, Eaton’s nearly 100,000 employees work to meet the growing demands of the global energy landscape.

To fuel its commitment to innovation and optimize its global operations, Eaton harnesses AI and data analytics to manage its extensive power management systems. Leading the charge is Ross Schalmo, Chief Data Officer, who has spearheaded the company’s digital transformation over the past two years. His efforts include driving a comprehensive enterprise data and AI strategy, establishing a global Data Governance program, and implementing Eaton’s first cloud-based data lake house.

Schalmo spoke with Ben Blanquera, Vice President at Rackspace Technology and a member of the CDO Magazine Global Editorial Board, about the challenges of reorganizing his IT team and the importance of establishing a data-driven culture throughout the company.

Playbook takeaways:

1. Collaboration with business teams is essential: Embedding data engineers within business teams instead of siloed functions creates a direct link between technical work and business needs ensuring more relevant solutions that accelerate value delivery across the company.

2. Develop existing talent rather than hiring new teams: Reshaping processes and setting clear, challenging goals, can improve organizational processes which can lead to significant performance gains.

3. Data as a reusable asset enhances internal and external value: Treating data like a physical product — complete with ownership and lifecycle management — creates maximum value and new revenue-generating opportunities.

4. Strong data governance supports AI and innovation: To fully capitalize on the potential of generative AI (GenAI), it’s important to implement a robust data governance framework to ensure consistency across the organization and improve data quality.

Organizational redesign key to transforming value delivery approach 

When Schalmo took on the role of CDO at Eaton in 2022, he spearheaded an organizational redesign to revolutionize how the company delivers value. Previously, the team’s structure involved multiple personnel handoffs, which caused delays and inefficiencies. Often, end users’ submissions of specifications were subject to a cumbersome series of steps — Structured Query Language development, followed by scheduling, etc. — before anything was delivered. Schalmo describes the process as “exhausting” and acknowledges it needed a significant overhaul.

He started by prioritizing rebuilding relationships between the data and business teams, a process he acknowledges took time. “In the first six months, there were moments when we questioned whether we had made the right choices,” he says. Today, team members who previously had little understanding of areas like supply chain management are now collaborating more effectively with business teams.

Rather than bringing in an entirely new team of talent, Schalmo focused on developing and refocusing the firm’s existing workforce. “As leaders, it’s our responsibility to shape the organization, set ambitious goals, and provide the necessary coaching,” Schalmo says.

Data engineers integrated into value streams to drive business impact

At Eaton, data engineers aren’t isolated in separate teams but are integrated into value streams. Each leader oversees an enterprise data platform—whether it’s data science workbenches, visualization technologies, or data warehouses. In addition to managing these platforms, each leader collaborates with one or more business teams to ensure effective delivery. For example, one leader manages the master data management technology while also working closely with Eaton’s electrical sector and marketing functions, creating a direct link between data management and business needs.

This structure enables continuous feedback from engineers using the platforms, as well as swift adjustments and improvements. The feedback loop enhances the ability to deliver products and services that meet the needs of Eaton’s users and external customers. From a data governance standpoint, Schalmo says that Eaton establishes rules that facilitate data sharing and usage of assets such as sales orders across departments, including finance and supply chain planning. His team focuses on building core data assets, while business teams contribute their expertise to refine and optimize them.

The close partnership between data engineers and business teams ensures that engineers not only grasp the technical aspects but also the broader business context. “It’s not just about moving data or writing complex queries,” Schalmo adds. “It’s important for engineers to understand the business processes generating that data and how it will be used. By documenting data flows and understanding the objectives of our reports, we can better align our efforts with business needs and effectively measure the impact of our initiatives.”

Treating data as a key asset through GenAI

Schalmo also discussed how GenAI is reshaping Eaton’s approach to building trust in data and managing technical debt. Under his leadership, the company launched a data literacy initiative to encourage teams to handle data with the same care as physical products. This includes assigning ownership and lifecycle management to data sets to ensure they deliver real value. By treating data as a reusable asset, they have been able to boost productivity and open new revenue opportunities.

An example is Eaton’s partnership with Palantir, which highlights how GenAI is helping create a sustainable data infrastructure. Schalmo likens the initiative to a city plan, where Eaton builds different data assets over time. When facing inventory challenges on the shop floor, they used GenAI to help planners manage material shortages more effectively.

One use case involved shortages that delayed order fulfillment. A GenAI application provided planners with solutions, leveraging their expertise to resolve issues creatively. When a 20-foot tube was unavailable, AI-enabled data helped planners determine that a 25-foot tube could be cut down to meet the need, capturing this solution for future use.

This system not only provides recommendations but also generates narratives and automates communication, closing the feedback loop. “As a result, we’ve built a strong bill of material assets that improves our ability to achieve outcomes, demonstrating the effectiveness of our data strategy,” says Schalmo.

Improving data quality with reusable data assets and AI innovations 

Schalmo has also focused on building a culture around reusable data assets that can be applied in multiple ways. He established data domains aligned with corporate functions such as marketing, sales, supply chain, and HR, and set up formal data governance structures within each domain.

Guided by a playbook, this approach moved Eaton from no governance to implementing data quality dashboards and scorecards. “These tools help us track performance and connect data quality improvements to key business outcomes like sales growth and efficiency,” says Schalmo.

Because everyone in the company is responsible for data quality, employees who create, modify, or delete data—especially critical master data—are accountable and must complete training to treat data as a valuable asset. Schalmo believes this sense of responsibility leads to better outcomes from the data collected.

By developing reusable data assets, Eaton is embracing concepts like the “digital thread,” which connects data points and processes, and using GenAI to simplify the management of customer specifications, reducing manual work and improving sales efficiency.

Schalmo says the main goal is to create a seamless customer experience powered by real-time data and feedback loops. By using AI to analyze customer feedback and incorporating it into product design, the organization can quickly identify quality issues and enhance products. “This data-driven approach improves operations and customer service, and delivers real business value,” says Schalmo.

Conclusion 

While Schalmo says Eaton doesn’t consider GenAI the solution to every problem, it does play a significant role across the enterprise, enhancing services, strategy, and offerings. To get the most out of AI investments, he says, it is essential to understand business processes and customer workflows. When encountering setbacks in AI initiatives, it may be worth reevaluating the deployment methodology and clarifying the specific business problems the organization is aiming to address.

About Ben Blanquera

Ben Blanquera is a Vice President with Rackspace Technology. Rackspace is a global leading hybrid cloud and AI services provider.. Ben is passionate about creating amazing business outcomes by leveraging data and analytics. He is on the CDO magazine editorial board and is interviewing global CDOs to gain their insights to create a ‘playbook’ for the industry.