Three Mission-Critical Applications of Big Data in Oil, Gas

While many OFS companies are using Big Data to improve drilling operations, a few pioneers are using talent analytics internally to overcome this recruiting challenge: measuring employee engagement, identifying skill gaps, and modifying their recruitment practices as necessary. They’re also using Big Data to improve employee productivity and determine where to invest in training and professional development. Because a single oil and gas company may have employees dispersed all over the world, Big Data offers the ability to integrate siloed data to see trends (Who are the best employees? Why is their performance so impressive? How can that be emulated elsewhere?). These efforts may also improve employee retention, as A Society of Petroleum Engineers Survey, found that 53% of oil and gas workers would consider leaving an employer if training wasn’t provided.

Big Data leaders should also consider partnering with academic institutions to develop a pipeline of talented recruits for both traditional engineering roles and data science roles. NYU, for example, has developed a graduate program within its Center for Data Science that trains students in math/statistics, computer science, and data science, helping them establish a flexible foundation that can translate across industries. Chief Data officers building teams around Big Data initiatives will need to understand how to best recruit talented data scientists and, once they’re on board, how to pair them with employees with more industry expertise in order to create a team that can effectively tackle industry-specific challenges.

Protecting (and Leveraging) Data Assets

Data security is typically one of the first concerns when Big Data becomes a dominant tool in an industry. Since Big Data initiatives in the oil and gas industry aren’t relying on any sensitive personal information – personal identities or financial information, healthcare-related information, etc. – privacy and security is not major issue in that sense. However, security is still paramount because the data that is collected from the field and from the distribution channels is a valuable asset, one that domestic and international competitors could benefit from greatly is they were able to access it.

Big Data pioneers in oil and gas need to establish stringent security policies from the get-go to deter hackers and minimize the risk of security breaches. They need to ensure that the physical assets (like sensors) are just as secure as digital assets.

But leaders must also creatively assess how their data assets can be leveraged to generate additional insights. First, data scientists should think about how they could use data from other players in the industry – distributors, equipment manufacturers, software developers, etc. – to inform their operations, and vice-versa. Developing a mutually beneficial (and secure) data sharing program could give both partners a competitive edge.

Finally, the most effective Big Data leaders in oil and gas will be able to think beyond the possibilities of existing data. They’ll need to tackle a new challenge that we’re calling “Applications Conceptualization” – envisioning what could be done if the right data was available. This forethought requires a keen understanding of the business’s objectives, the economic landscape, and a thorough understanding of what data is available and how it can be collected.

One thing is clear: OFS executives hoping to use Big Data solutions to improve and streamline global operations need to be looking for a dynamic, forward-thinking leader who knows how to prioritize the endless number of potential Big Data applications. It’s important that the leader in charge of these projects avoids some of the mistakes we’re seeing in other industries: over-investing in a particular Big Data solution at the expense of others, failing to apply any gained insights or make any changes to the business, or failing to build a business intelligence team with an optimal mix of technical skills, industry knowledge, and business acumen. The appropriate combination of upfront Big Data investments and long-term application strategy could propel any company ahead of their competitors.

Walter J. McGuigan, Jr. joined Battalia Winston in 2013 through the acquisition of Norm Sanders Associates where he spent over 25 years as a Managing Director and Founder. Walter has extensive experience in recruiting Practice Leaders and Partners for world class consulting firms as well as recruiting senior level systems and technology professionals for clients in the Financial Services sector.

John Ebeling joined Battalia Winston in 1999, as Partner, and currently has over 25 years of North American and international, retained executive search, executive assessment, and management consulting experience. John continues to conduct significant search assignments for clients in Europe, the Middle East, Russia and Africa; recent search assignments within the Oil and Gas industry have been concentrated in Houston, Texas, EMEA, Canada (Alberta and Ontario), and in South America.

Roy Lowrance is Managing Director of New York University’s Center for Data Science, part of the university-wide Initiative in Data Science and Statistics. Lowrance is also Senior Research Scientist in Computer Science at NYU’s Courant Institute of Mathematical Sciences. He is a founder of Advanced Valuation Analytics Corporation and served as the Chief Technology Officer at Reuters as well as Capital One Financial.


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