Latest Blog Posts from NSG
Making Sense of Complex Problems: My Experience with Strategic DQO at NSG

Author: Ben Mundow, Consultant

When I first joined NSG back in 2020, one of my earliest tasks was diving into something called a Data Quality Objective exercise—more commonly known as a DQO. I didn’t realise at the time just how important this process would become to the work we do, both technically and strategically.

So, what exactly is a DQO? In short, it’s a structured, seven-step process originally developed by the U.S. Environmental Protection Agency (EPA) to help define the type and quality of data needed for environmental decision-making. Since then, it has been widely adopted across the nuclear industry—not just for characterisation work, but increasingly for high-level strategic planning.

At NSG, we use a version of this process called Strategic DQO, which supports key decision making like waste management strategies, research priorities, and long-term site development. We’ve delivered these exercises for major organisations including Sellafield Ltd, AWE, and newcleo Ltd.

Why Strategic DQO Works

What makes Strategic DQO so effective is its interactive and collaborative nature. Typically, we run these as workshops involving key stakeholders—people who have a vested interest in the topic. This approach ensures early buy-in and helps produce outcomes that are not only relevant but realistic.

The process begins with clearly defining the problem—arguably the most important step. If you don’t understand what you’re trying to solve, any work that follows could be misaligned. Alongside the problem, we establish boundaries: constraints such as time, budget, or scope that frame the discussion and keep the process grounded.

Let’s say the problem is: “We need to build a factory to mass-produce Mars Bars.” A relevant boundary might be: “Fruits such as coconut will not be considered as ingredients”—unless, of course, Mars wants to venture into Bounty territory!

From Questions to Action

Once the problem and boundaries are nailed down, we move on to identifying key decisions or challenges that need resolving. We typically phrase these as “Do we understand…” questions to stimulate discussion. For example: “Do we understand how the sweetness of the chocolate will be controlled?”

From there, we define parameters—quantitative or qualitative measures that support decision-making—and establish performance criteria. Using our chocolate example, the parameter might be the ratio of sugar to cocoa, and the performance criteria could be a 3:1 sugar-to-cocoa ratio.

Next, we determine the information inputs needed to answer our key questions. Some of this data might already exist; some may require new work. Identifying these gaps leads us to the final output: a list of activities or work packages designed to close the information gaps and drive the project forward.

Real-World Impact

Over the years, I’ve supported many Strategic DQO exercises, and I’ve seen firsthand how invaluable they can be.

One particularly memorable example involved a customer looking to improve thermal monitoring on radioactive waste. The DQO process revealed that they’d already reached the limits of what could be achieved in that area. Rather, the process redirected attention to alternative approaches—eventually leading to a new strategy focused on hydrogen generation monitoring. Although this wasn’t the outcome that the customer foresaw, the DQO proved invaluable as it steered them away from pursuing something with little benefit, and instead guided them towards something that would actually help them. This pivot saved time, money, and effort—and opened a more beneficial way forward.

Another great case was a DQO aimed at developing a waste disposal strategy. This customer was keen to explore non-technical factors like health and safety, environmental impact, and socio-economic concerns. By using the NDA Value Framework and working closely with stakeholders, we created an output that not only addressed their technical needs but also captured those wider considerations. When the plan was presented to a broader forum, it was well received—with only minor tweaks needed.

Final Thoughts

From my own experience Strategic DQO can seem complex at first glance —even overwhelming. There’s a lot of information to consider, from technical data to stakeholder priorities. But once you grasp the logic, it all starts to click. It’s a rigorous, step-by-step method that brings clarity to messy problems and helps teams make informed, evidence-based decisions. It’s not just about getting to an answer—it’s about getting to the right answer, efficiently and collaboratively.

CONNECT WITH BEN