DSDE: In Theory

Equating data to oil might make sense at first glance, given the data-driven success of tech companies, but the analogy breaks down as soon as you dig a little deeper.

The use of technology has helped ensure the profitability of the oil and gas industry despite a 50% fall in prices in 5 years. The key question, however, is whether the digital revolution can answer the sector’s biggest challenge: how to secure future production.

Disruption from artificial intelligence (AI) is here, but many company leaders aren’t sure what to expect from AI or how it fits into their business model. Yet, with change coming at breakneck speed, the time to identify your company’s AI strategy is now. 

One can almost guarantee that every engineer will consistently come into contact with data, no matter the engineer’s focus. Often, the data available to engineers is expansive; yet many are unequipped to handle it. Why then are data scientists not integrated into engineering teams at all levels?

The sixth annual Deep Learning Summit in London saw industry leaders, academics, researchers, and innovative startups present the latest technological advancements and industry application methods in the field of deep learning.

One of the foremost threats companies face today is that posed by cybercriminals, and the unique vulnerabilities of companies in the oil and gas sector create heightened cybersecurity risks for those pursuing transactions in the sector.

Saudi Aramco, BP, and Schlumberger pride themselves on staying at the forefront of digital technology development and deployment. But an equally daunting challenge for the industry heavyweights is keeping their ever-expanding digital systems secure.

Whether thinking about managing oil and gas or other infrastructure facilities or considering industrial efficiency, you may be pondering how the Internet of things can be used. Forward-thinking strategies include not just staying on top of regulatory changes but also influencing them.

Visually displaying data makes it much more accessible, and this is critical for identifying the weaknesses of an organization, accurately forecasting trading volumes and sale prices, and making the right business choices.

At times, it may seem that machine learning can be performed without a sound statistical background, but this does not take in to account many difficult nuances. Code written to make machine learning easier does not negate the need for an in-depth understanding of the problem.