Recent declines in oil prices have hit the world economy hard. Alberta, Canada\u2019s major oil region, has witnessed increased unemployment due to declining commodity prices. In January 2016, Saudi Arabia increased the price of gasoline for its citizens by 50 percent given the situation. With major fluctuations in prices and the high cost of energy projects, quality information has never mattered more.\nThe energy industry uses data science to cut costs, optimize investments and reduce risk. Reducing costs with data science is a popular application in the industry: much work has focused on improving maintenance and equipment monitoring. Optimizing investment decisions takes several forms including better internal resource allocation and assisting investors. Data science also contributes to improving public safety by providing better monitoring and oversight.\nDelivering innovation by borrowing ideas from other sectors\nTransferring ideas and techniques across industries is a tried-and-true innovation method. \u201cThe energy industry has recently started to adopt the survival analysis concept from the medical field,\u201d says Francisco Sanchez, president of Houston Energy Data Science. In medicine, survival analysis is a statistical method to estimate survival rates for patients based on their condition, treatments and related matters. In the oil and gas sector, this concept has been applied to field equipment.\n[Related: 10 boot camps to kick start your data science career]\n\u201cSurvival analysis is used to predict the maintenance requirements for field equipment such as compressors through monitoring and modeling,\u201d Sanchez says. Instead of taking an oil well offline for three days to repair damage from equipment failure, proactive action enabled by data science can reduce downtime to a single day, he says. Saving a day of downtime is valuable. A day\u2019s production at a small site \u2013 1,000 barrels of oil \u2013 represents $30,000 of revenue at current prices.\nBP leads in data science and analytics\nBritish Petroleum (BP), the U.K.-based energy company, has long been a leader in IT and related disciplines. The company\u2019s drive to invest in this area is driven by several factors. In terms of safety, the company\u2019s 2010 Deepwater Horizon disaster led to $18 billion fine in 2015 and other damage to the environment. Preventing such a disaster through better information is important rationale for the company. In 2013, the company established a Center for High- Performance Computing in Houston, Texas to connect with leading American institutions such as Rice University.\nBP\u2019s analytics capabilities\nBP\u2019s commitment to improvement through analytics shows an end to end commitment. The process starts with investment in high quality data and monitoring capabilities.\n\nData analytics in the field. The BP Well Advisor provides operational support for oil sites. This information is fed into several dashboards at the production site and at the corporate offices. The Well Advisor is now in use at more than one hundred 100 offshore wells.\n\n\nImproving production. BP builds models and analytics to improve the efficiency of its refineries. This approach optimizes the refinery\u2019s production capabilities. Analytics plays a role in directly improving production.\n\n\nPartnerships and talent. BP\u2019s direct investments in technology are only part of the data story. The company also works closely with IBM to improve its capabilities. BP has also recognized the importance of staff \u2013 Charles Cai, head of data science technology at BP, has been recognized as one of the Top 50 U.K. Data Leaders.\n\nNot every energy firm operates at BP\u2019s scale with operations scattered around the world. Fortunately, there are other ways to get started in analytics.\nInside the data science toolkit\n\u201cBefore we dive into tools and techniques, it is vital to start with the business problem,\u201d says Francisco Sanchez. Typical business problems in energy include predicting production, improving field efficiency and understanding geological activities. \u201cLarge firms such as BP and Halliburton have adopted data science methods already. I see a great opportunity for small companies with less complex data to achieve wins by bringing one or two specialized data scientists on board,\u201d says Sanchez.\n\u201cIn oil and gas, you have a wide variety of data to work with and it takes time to bring this all together. I have seen projects where some data are in Oracle databases, other databases have drilling data and there are yet other systems for economic and seismic data. Bringing all this data together requires tools such as Hadoop and NoSQL,\u201d Sanchez says.\n[ Related: Top tech salaries in 6 industry verticals]\n\u201cRegarding specific tools, it will depend on the complexity of the problem. If you are working on a problem with over 50 variables, I suggest looking into machine learning tools. Random Forest, produced by Salford Systems, is one option to consider,\u201d he says. For other projects, the data science toolkit includes tools such as R and Python. \u201cTibco and Tableau are useful visualization tools to present the data,\u201d he add.\nOpportunities for data analytics\nConsulting firms and analysts have long added value to the industry through their specialized knowledge \u2013 the same holds true for analytics and energy.\u00a0 Organizing data and presenting it in a useful way is another way to add value with data science.\n\u201cIn my role as a data analyst, I primarily spend my time visualizing rig performance and drilling performance. I have created data gathering routines that help bring together hundreds of data sources into neat packages for presentation and performance review. My firm then sells this material at above market average rates. The market pays a premium for explanatory data visualizations because many organizations lack in-house capabilities for these activities,\u201d says Graham Eckel, a former analyst at Precision Drilling in Calgary, Alberta.\n\u201cIn the energy sector, there are still plenty of data opportunities. It starts with implementing systems and processes for data collection, cleaning and storage. Hiring a data scientist to build the architecture and guide the implementation is one way to start. With that in place, you can start to generate predictive insights,\u201d Eckel says.\nThe future for energy data science\nThe use of data science and analytics is expected to grow in the energy industry. In a low oil price environment, management will seek cost reduction insights from data. During growth periods, data science will guide management decision making with better insights to improve production and adjust to market demand. The continued growth of data science tools and vendors will also support the trend.