How Companies Use Big Data Analytics for Business Operations
Big data is quickly becoming an essential component of strategic business planning. IT is the collective use of large-scale data processing and technologies to build, manipulate, and analyze data. As of 2021, it’s estimated that there’ll be over five terabytes (1,500 terabytes) of data generated and analyzed every single day. For many corporations, this number will be closer to seven billion gigabytes every single day. When considering what is possible to store and process through big data analytics, the possibilities are nearly endless.
One of the first industries to take advantage of big data analytics is gas companies. They’ve long known that the way their business operations are currently managed is ill-suited for the types of information needed to optimize their bottom line. To better understand how companies are using big data analytics for optimization, gas companies need to understand how their current processes function. Below, we’ll examine how those processes currently operate, and how they can be improved upon to improve company performance.
One of the primary ways that gas companies utilize big data analytics is through the execution of their on-going analytical processes. In doing so, those processes are optimized for accuracy and productivity, as well as reducing operational costs. For example, oil and gas companies typically rely on a “buying” phase in the production process. During that phase, producers decide which wells to explore and develop, how many to keep open, how to optimize production methods, and how to collect and organize existing resources. As such, these are all “buy-side” activities.
But all of that analysis and optimization isn’t enough. That’s why most companies only perform some of their analysis at the “order” phase. After all, even the best “buying” phase can’t make up for poor “planning” and “strategy” skills. Companies need to apply advanced analytics and computer science techniques in order to maximize throughput and minimize cost. So how do they do this?
Companies use big data analytics to improve efficiency by eliminating unnecessary steps. For example, when oil and gas companies use sophisticated integrated management systems, they can take advantage of existing reservoir, platform, and wellhead information to determine when to drill for the maximum number of wells. They can also pre-empt problems with storage infrastructure by analyzing potential constraint points. Once problems are identified, companies can address them before any costs are incurred. Advanced analytics also allow for accurate reservoir planning and design.
Another way that companies use big data analytics for business operations is through “machine learning”. Machine learning refers to the process of discovering patterns and making inferences from large, often complex, sets of real-time data. Software engineers and IT managers implement this technique by running predictive analytics on large and complex systems, such as supply chains or real-time manufacturing scheduling. They then refine this predictive analysis using experience and intuition to generate “smart suggestions” for improvement in future business operations.
Another way that companies use big data analytics for business operations is through “artificial intelligence”. This emerging field attempts to solve practical problems in industries by leveraging large amounts of accumulated data. Many natural and living sciences, including ecology, energy, health, and mathematics, are making use of artificial intelligence in research and data analysis.
Lastly, big data analytics for business operations is being applied in the realm of “deep neural network technology”. Deep neural networks (DNN) utilize large networks of artificial neurons to collectively achieve a specific goal, such as generating lyrics in a song or hand-written notes in a book. The network uses unsupervised, supervised, reinforcement learning to achieve its goals. Although this technology has not yet been deployed in production environments, many data scientists at Google, Amazon, and Facebook have taken an interest in this area due to their high potential for usage in autonomous vehicles, manufacturing automation, and other areas where humans and machines may work side-by-side.