Data & Analytics

Industry Thrives on Data but Struggles To Quantify Its Value

Data undoubtedly has value, agreed participants at a recent symposium held by the Professional Petroleum Data Management Association in Denver. Quantifying that value—and communicating that value to decision-makers in a company—however, is tricky if not impossible.


Data undoubtedly has value, agreed participants at a recent symposium held by the Professional Petroleum Data Management Association in Denver. Quantifying that value—and communicating that value to decision-makers in a company—however, is tricky if not impossible.

“It’s kind of nonquantifiable,” said Tim Coburn, professor of energy and operations management at the University of Tulsa.

That inability to nail down the value of the data keeps some in management from making decisions that would boost data maintenance and stewardship. Jim Soos, a partner with Infosys Consulting, repeated the refrain for those attending his presentation: Data is an asset. Everyone present nodded; they had heard that a million times. The attendees, however, were not the ones who needed to hear it. “That message really wasn’t fully resonating with a lot of the business folks,” Soos said.

He was at the symposium presenting a piece of software, a concept really, that is intended to bring the value of data a bit more in to focus for those holding a company’s purse strings. The initiative follows a simple formula to assign a number to the nebulous value of data.

“What we want to be able to do is say, ‘What’s that value of that data?’—trying to put a number to it, a dollar figure to what the data is,” he said. His presentation partner, Sachin Padhye, a digital leader at Infosys, laid out the formula. The value of a piece of data, he said, is the revenues predicted by that data element, minus the expenses, plus the risks mitigated by that particular data. Although not precise, this formula perhaps can give management more to work with, open their eyes a little wider. 

Padhye, however, pointed out that this value is not static. “The value of data increases as it progresses through the value chain,” he said.

A mantra repeated throughout the symposium and hammered home by the keynote speaker, Kentaro Kawamori with Rice Investment Group, was that, for data to be valuable, it must be “clean, accessible, and used.” The “used” part of that mantra garnered a lot of attention. In fact, Coburn suggested that data has no value at all until it has moved along the value chain. “Data by itself has no value,” he said. “The number ‘3’ has no value. Where you get value, pro or con, is when you use the data to do something else.”

“The value of the data is not realized until some time down the road from when it has been collected,” he added.

Awareness of the fluidity of data’s value is important, said Amelia Webster, vice president of product management at EnergyIQ, as she put an example to the point. “That’s absolutely critical,” she said. “When you’re planning a well, you have a proposed surface location. When regulatory goes out and tries to survey that location and it moves or the permitting isn’t possible and an alternate location is found, then, when the well is actually drilled, the bottomhole is slightly different than the proposed. If you’re still using the initial planned locations, then any analysis done with that downstream is invalid. Allowing attributes of a well that evolved during its life cycle to become more-accurate and more-time-specific as you progress through the well’s life cycle, that’s absolutely core.”

That fluctuating value of data throughout its life is one of the reasons decision-makers have such a hard time. “When you say, ‘What does the management team expect from the values of data?’ I think they expect a KPI [key performance indicator] that can give them the information to make decisions,” said Tyler Craig, information technology applications manager at QEP Resources, “but I don’t think they have the value of how to get there.”

Planning for data-value fluctuations is critical to making the most use of the information. Ray Obuch, information technology specialist for data management with the US Geological Survey, spoke about the “rush to analytics” and how that rush may cause problems.

“It’s very critical to plan ahead of time all of this,” he said. “It’s really boots on the ground with data management. It takes eyes on it, and it takes data stewardship.”

Obuch continued by saying that anybody who touches data is a data steward “and is responsible for that realm of data that they play with every day.” He said everybody needs to be aware of data quality and understand that what they touch is really important downstream. “It’s really important,” he said, “because I think upper management see something in digital format and they think it’s already cleaned up … . But, really, you need to clean things up for them. And that’s where the work is.”

The value of data also comes from the other side of the equation. Bad data can have value, too, as Obuch pointed out. “I think it’s really hard to put metrics on making good decisions from good data,” he said, “but I think we can do the risk analysis and we do have enough history on where there were bad decisions made on bad data.”

Jim Crompton, adjunct faculty member at the Colorado School of Mines presenting a graduate course in petroleum data analytics, boiled it down: “So, sometimes, maybe our assessment isn’t on value created but lack of value destroyed.”