Data Quality is the Core of Risk Mitigation

There’s a conversation the energy industry needs to have about data (and data quality), and urgency is growing as producers and adjacent companies adapt to an evolving regulatory landscape.

Why? Data is a critical component of how the industry is regulated and how energy producers analyze efficiencies and plan future projects. If you’re attempting to execute in these areas and your data isn’t accurate or up-to-date, you could have grave missteps in your planning. Or worse, you could have more risk exposure than you realize.

“A producer’s core competency is in finding oil and gas and operating their assets safely and effectively. That’s where their focus rightfully should be. They rely on companies like GDM to focus on data management, which is our core competency,” says Brian Hall, president of GDM, Inc.

Trustworthy data is essential for company leaders to make optimal decisions and improve operational efficiency. If you don’t have access to a reliable source of in-depth industry data, your ability to protect and grow your company could be significantly impeded.

What’s missing from the conversation about data?

When the topic of data comes up in energy circles, the conversation often lacks nuance, putting producers and other companies that support the energy industry at a disadvantage — and at risk.

Why does data need to be at the centre of these conversations? Because ultimately, if your data is inaccurate, your analysis will be inaccurate.

Data doesn’t just affect your internal planning. As time passes, regulatory bodies will continue to ask for more accountability and compliance; some companies will be proactive about it, and some won’t. And when it happens, smaller companies will be scrambling.

If you’re an environmental or service business supporting these producers, accurate assessments, planning, and costing can help ensure a competitive edge and successful project outcomes.

Data quality must be at the heart of best practices.

It’s crucial for producers to understand that their decisions can only be as certain as the data they use for analysis.

“We’re always cautious when we see companies focusing more on platform functionality and downplaying the need for accurate data at the core,” says Hall. “Platforms that generate insights for your business cannot add value unless they base those insights on accurate, up-to-date data.”

Companies make financial decisions based on data and then direct resources toward executing their plans based on that data. To ensure the best use of resources, these companies would be wise to leverage the experience of third-party experts to produce the quality data they need.

Accurate data is also critical for mergers and acquisitions, as new ownership usually includes responsibility for all assets, regardless of location or condition. Being able to clearly detail asset data is imperative when these assets change hands.

What happens when data quality is left out of the equation?

“Data is the lifeblood of so many industries. You need to be able to trust in the data and have confidence you can assess and analyze the data and make decisions,” says Hall. “Without that trust, you’re gambling.”

If you’re working from inaccurate data, you could inadvertently funnel company resources into the wrong solutions. Using poor data and executing high-value projects based on that data can be a significant waste of resources and a source of missed opportunities for growth or profit.

“There’s a sequential nature to data that can’t be left out of the equation,” says Hall. “There’s a saying that data becomes information, which becomes knowledge, which then becomes wisdom. You don’t get to jump to wisdom.”

As industry standards evolve, companies must meet expectations for asset data, including assets they’ve purchased over time. A comprehensive collection of data that has been carefully compiled and tested over time will become more and more valuable as expectations change.

The impact of accurate data.

There have been many instances where accurate data could have helped energy producers prevent everything from small but significant mishaps to critical and expensive incidents.

For example, most industry veterans know how orphan wells are handled if a company goes bankrupt and how those rules have changed over the past few years. As industry regulations evolve to mitigate and reduce these incidents, there will be a growing need for companies to accurately account for their assets and any relevant data.

When the Saskatchewan government implemented a retroactive flowline licensing program, it became clear that companies didn’t just need current data but also a complete picture of how assets have changed hands and the life story of those assets.

If there’s ever an incident, such as a rupture in the pipeline or spill, you must promptly submit information to the government. Having a trustworthy source of accurate data allows you to respond quickly and appropriately, even when things go wrong.

As an industry, we can work together to increase transparency, accountability, and responsibility throughout the sector. A collective effort can protect companies, communities, and people.

Entering a new era of data with AI.

It will be essential for business leaders to understand how AI impacts energy data, especially as the industry races to include AI in its solutions. Data hygiene and a commitment to accurate data that is regularly analyzed, tested, and retested must always remain at the core of any data solution.

“More data doesn’t mean better data,” says Jennifer Shea, data operations director for GDM, Inc. “Machine learning may be able to predict things basically, statistically, but if the data quality isn’t carefully managed from the first step, the insights will be off.”

It’s vital to recognize that AI enhancements such as predictive analytics are ineffective if the data is inaccurate. While AI seems a popular topic of conversation in the energy industry, company leaders must focus on the practices that deliver optimal outcomes and mitigate risk.

“Despite the buzz about AI, 35% of data professionals say the major initiative their organization will take on in 2024 is implementing stronger governance and security controls, compared to 20% that said integrating AI into business processes,” say data security experts at Immuta in its 2024 State of Data Security Report.

There can be no discussion of AI without a deep understanding of the difference between machine learning, predictive analytics, and actual artificial intelligence.

“Regulators are requiring more accountability in many ways, and companies are having to go beyond simply meeting minimum requirements,” says Shea. “The landscape is changing, and we need to take deeper dives into assets and have a deeper understanding of it all in order to take accountability.”

Most AI solutions don’t encompass best practices for data hygiene, data protection, governance, and storage. If you aren’t having regular conversations about those topics, your AI solution is likely not offering what you think it is.

AI is used in the energy sector to increase efficiencies and reduce costs in many ways, including predictive maintenance, oil and gas exploration, and demand response management, according to IT and business solutions experts FDM.

There is a place for AI in energy, just like in any industry. However, it does not replace accurate data collection, testing, and analysis. Hoping it will can result in incalculable risk.

The energy industry faces mounting pressures for transparency, compliance, and innovation, and data quality is front and center. Accurate data enables producers, regulators, and service providers alike to navigate challenges and confidently seize opportunities. While AI offers exciting potential, it is only as effective as the foundational data it relies on. In this rapidly evolving landscape, focusing on data integrity isn’t just a best practice—it’s essential to minimizing risk, improving efficiency, and ensuring long-term resilience.

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