The need for data science in industry
‘Drill data drill’ has become the new mantra of the oil and gas industry. A report was released by Cisco Consulting Services in April of 2015 surrounding the new reality for the oil and gas industry and how changing marked dynamics are driving the need for a major digital transformation. Cisco’s 2015 study consisted of a survey of 50 oil and gas industry professionals and interviews with various energy consulting and marketing firms.
Due to several key factors such as increased U.S. production and diminishing storage space for crude oil, it is projected that the price of oil may not bounce back to $100/barrel for many years if ever. In any business opportunity, if revenue is not foreseen to increase, the only way to increase or maintain profit margins is to cut costs elsewhere. Rather than implementing layoffs or shutting down sections of production, a digital transformation is on the horizon in order to increase processing efficiency and fully understand business management associated risks.
There are opportunities at every level of the oil and gas industry to increase operational efficiency. Based on an economic analysis performed by Cisco, utilizing operational data will improve upstream processes by reducing production, increasing rig uptime, increasing drilling efficiency, and improving remote monitoring and personnel safety. During midstream and downstream stages, fleet operations, reducing spillage, intelligent lighting, next-gen workforce, and smart refineries are all areas that may be improved through deriving data insights.
According to A CIO’s Guide to Using Gartner’s Digital Oil Framework (2014), currently many oil and gas companies are struggling to improve functional and business capabilities based on real-time operating data analysis. An off shore oil rig produces between 1TB and 2 TB of data per day. This time sensitive data may take up to 12 days to transfer to a central repository and by that time, any operations information that could have been extracted is no longer useful. This opens up the need for “edge or fog computing” in which any operational analysis may be performed on site through smart technology.
In an interview with CNBC, Uptake's CEO Brad Keywell, estimated that only 1% of data derived in oil and gas operations is being presented to and used by decision makers in industry. For more long term analysis needs, data may be virtualized, meaning heterogeneous data types derived from all stages of an engineering process may be collected in one repository or cloud and treated as a logical database for users in any location. This process will help connect all components of a company to ensure efficiency through out. This virtualized data may even take into account external factors such as the economy to be included in the business analytics.
Cisco's survey reveals data and analysis deficiencies in only one industry. If we take a deeper look at clean energy, rail, infrastructure, environmental, and other civil engineering industries, we will hear the same story. There is a need for smart infrastructure to reduce costs and increase efficiency across the nation resulting in direct economic improvement. This opens up many doors for up and coming statistical and data science consulting groups who understand the importance of the engineering process and are passionate about improving our nation and resources.