How NOV built data confidence across eight data warehouses

NOV Inc. operates what Adam Nilsen calls "the department store of the oil and gas industry", instead of selling shoes and appliances, they supply cranes and drilling rigs to energy companies worldwide. Behind this global operation sits a data infrastructure that Nilsen has watched evolve for two decades.

As Business Intelligence Operations Manager, Nilsen oversees eight different data warehouses, each one representing years of technological evolution and business growth. By 2023, this complexity had created a crisis: data issues were taking days to resolve, and Nilsen's team found themselves constantly defending their data rather than confidently presenting it.

Then, NOV implemented data observability with Bigeye, and the results were immediate. Their first alert from Bigeye cut straight to the heart of an accounts receivable issue, providing not just the problem but the historical context and potential fixes. Within minutes, what used to take Nilsen's team days to diagnose was being discussed across the organization.

The Challenge

NOV's data landscape tells a story familiar to many large enterprises: growth that outpaced infrastructure planning. "Over the years, going back 15, 20 years ago, we built data systems from the first iterations," Nilsen explains. "The technology has changed over and over again. In many cases, some of those older systems are still live but upgraded, so it's a hodgepodge of logic, a hodgepodge of tool sets."

NOV had also recently merged four different business units, each bringing established processes and data management approaches. The result was functional but fragmented: each data warehouse served different needs, with data quality issues often going unnoticed until they had already caused downstream problems.

"We knew that we had holes in our data process," Nilsen admits.

The team was reactive by necessity, spending much of their time tracking down issues that had already impacted business operations. Data quality couldn't be prioritized beyond critical customer and supplier information, leaving gaps that grew more problematic as data volumes increased.

"Failures became apparent and the data quality did suffer for it." Nilsen explains.

Why Bigeye

When NOV began evaluating data observability platforms in 2023, the team wasn't looking for the most feature-rich option. They wanted something their administrators could implement quickly and their teams could understand immediately.

"When we sat down and I started going over the demonstration, it was the ease of use," Nilsen recalls. "We looked at a couple of different tool sets. Some were more complicated, they had a lot of information in the interface, but it was more jumbled."

"How Bigeye is architected and how these issues are presented were far superior than any other tool at the time."

Bigeye's user friendly interface made sense to NOV's team: how issues were detected, how they were presented, and how the information flowed through their existing processes were all intuitive.

The First Alert: Ticket 001

The validation came quickly. NOV's first ticket from Bigeye, NOV-001, dealt with an accounts receivable issue. But what impressed Nilsen wasn't simply that the system caught a problem; it was how the information was presented.

"Bigeye had it presented in clear text, exactly what was wrong," he says. "It gave very, very good information on what it was. It gave the historical timeline, plus potential ways of how to fix it."

Nilsen shared the alert with his leadership team, and they were so impressed the distribution list began growing organically as team members added their colleagues to the conversation.

That moment represented something NOV had been missing: the ability to communicate about data issues with confidence rather than uncertainty. Now, the entire organization had comprehensive context within minutes of detection. "We'd never had that much data just from the notification itself."

From Reactive to Proactive

Before Bigeye, data problems followed a predictable and painful pattern.

"When an issue occurs, instead of it turning into a timeline issue, and after an hour, after two hours, more issues start to occur and it gives it a snowball effect, we're able to much reduce that entire process now," Nilsen explains.

This "snowball effect" was NOV's biggest operational challenge. A single data quality issue would cascade through downstream systems, creating multiple problems that compounded over time. By the time Nilsen's team identified the root cause, they were often dealing with several related failures across different systems.

The transformation is measurable:

"We used to count this [time to resolution] by days, and the metric itself has completely changed. Now we're able to resolve these issues before they get out of hand."

The dynamic has completely reversed. Bigeye's automated monitoring watches data across all of NOV's most important pipelines, catching quality issues before they can cascade.

Column-Level Lineage

One of the most transformative capabilities for NOV has been Bigeye's lineage functionality, which provides visibility into data relationships at a granular level that matches the complexity of their infrastructure.

"Lineage was a game-changer. It was very, very exciting," Nilsen explains.

"The idea of being able to see not just at a table or an object level, but an individual column level, that there was a problem, and all the downstream consequences to that problem."

This level of detail transformed how NOV's team prioritizes and responds to issues. Instead of treating all data problems as equally urgent, they can understand exactly which data elements are affected and what downstream systems might be impacted. The visibility extends beyond technical systems to business processes, helping teams communicate more effectively with stakeholders about the scope and urgency of different issues.

For an organization managing eight data warehouses with decades of interdependencies, this granular visibility has been essential for maintaining operations without constant escalation.

Partnership Beyond Technology

Beyond the technical capabilities, Nilsen highlights the collaborative relationship with Bigeye's team as a critical factor in their success.

"The partnership with Bigeye is second to none," he says.

"We have a customer success rep who is side-by-side, shoulder-to-shoulder with us in our meetings, with the build process, with error resolutions." "She really feels she's walking the path with us to be able to improve our systems and always offering ways for us to be able to target issues and make our processes better."

For organizations managing multiple data warehouses, legacy systems, and complex data flows, NOV's experience demonstrates how the right data observability platform can transform teams from a defensive position of constant explanation to confident data stewardship, turning data quality from a constant concern into a competitive advantage.