Deploying Data Visualization in Oil and Gas Company

By CIOReview | Monday, August 27, 2018
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With technology at the core, oil and gas companies are trying to optimize their processes and methods. The industry witnessed a number of changes over the decades. However, they are facing a wide array of challenges in especially adopting cloud, data analytics, and visualization. Especially because the industry involves substantial amounts of data, research, and need for reporting. Compiling these vast piles of information and making it digestible and transferrable to stakeholders is never an easy job. As a solution to these pressing challenges existing in the industry, data visualization can help bridge the gaps.

Unlike retail or small businesses, oil and gas industry relies on significant capital investments. Conventional data gathering techniques make it difficult to retrieve the pertinent information that is needed to help address the industry’s needs. In this regards, data visualization has become one of the strategies to help aggregate the critical information required, for data to be disseminated across several multimedia forms. This could efficiently improve business processes without affecting the data safety in any way. Through the use of images, these industries can easily benefit and also enable stakeholders to understand large amounts of data in a more comprehensive format.

As per various studies and research reports, most people are visual learners, and this makes it important to deploy visual data to help in businesses, especially in oil and gas. When you present meaningful information using engaging graphics, you are able to present the information in a more understandable manner. This can greatly improve the data analytics capabilities of businesses like oil and gas.

For the machine learning technology to work effectively in the oil and gas industry, it needs to modify some of the existing patterns as a part of its central nervous system using the controlling apparatus of the particular business. Algorithms must be built accordingly so that controller units can automatically modify the programs based on new data, allowing for human-like reasoning to be built into the minds of machines.

Some of the sophisticated industry-related tasks such as balancing a glass can be “taught” to a robot by building its control system with an observation unit. This unit uses AI and machine learning techniques to constantly modify the angle (movement, vibration, etc) at which a glass is held. Every time some type of an external force destabilizes the balance, the observational unit learns to compensate and balances the glass. In a similar manner, machine learning is deployed in many industries such as oil and gas, and is used to perform various predictive tasks.