Energy sector analytics

Thanks to big data analytics, a makeover for the energy vertical is in order to prepare it for the digital economy. Senior leaders at energy companies are committing to fact-based decision making, investing in analytics talent, resources, tools and technologies. Slow to pick up, but fast to implement, the energy sector is beaming thanks to the refurbishing by advanced analytics that has quickly transformed its landscape. According to Deloitte, “Applying analytics to the vast amounts of useful data utilities collect offers an opportunity to uncover new customer usage patterns, to forecast demand better, to manage energy constraints more effectively, to improve compliance with regulatory requests, to prevent fraud and reduce loss, and to enhance customer service.” So, Analytics has served to be no less than an epiphany for the biggest players in the domain and here is how:

  1. Resolving the Oil Industry Hiccups – The oil industry often saw downtime due to pipes being stuck within the wellbore. A common problem like this occurs with changes in the physical state of a well and it becomes difficult to carry out normal drilling operations at the site. Oil sites are particularly vulnerable to land slides or mineral deposits. With predictive analytics solutions, oil companies are able to assess the conditions of a well real-time and build an almost accurate picture to predict if operations in a well would be conducive. Engineers’ expertise with physics-based models is combined with statistical models to predict the most likely occurrences of non-productive time (NPT). Additional insights such as how many new wells to build, the average lifetime of an existing well bore, selecting sites also become help in building reports. Popularly known as survival analysis in the oil industry, this type of analysis serves as an indispensable tool designed to analyse censored data by estimating the lifetime through the failure rate. This also helps the companies to identify wellbore sites most likely to cause problems and delay operations.
  2. Data Integration in Physical Systems – The energy sector has majorly relied on physical systems. But data governance and data integration with these systems are mammoth tasks. According to a survey by Accenture, 63% of energy respondents feel that data integration is a roadblock as the quality of data, and the ability to analyse it are not at par with other industries. But, energy companies are resolving these issues by making sure that data collected is highly relevant to the businesses and proprietary data that helps differentiate between these companies is recorded and analysed effectively. Complete data integration with existing systems will not eliminate them but help reduce the decision making time, increase efficiency and productivity at lower costs with adequate focus on the insights generated through this data.
  3. Energy Crisis and Solutions – The energy sector is experiencing dramatic changes thanks to innovation in the way electricity is generated, distributed, and consumed. Using sustainable sources of electricity generation, storing it to meet consumer demands, and preparing for planned and unplanned outages or unprecedented consumption are all top priorities but the biggest challenge is that all these need to be achieved at a manageable cost. A rise in global energy consumption requires an understanding of the balance between conventional and renewable energy sources. On the consumer’s end, smart metering is required to optimise electricity. Advanced analytical systems help monitor patterns in energy consumption and generation. Real-time data must be captured to predict surges and shortages that can equip companies to automatically respond with consistent and affordable electricity supply. According to IBM, “Such systems would revolutionise this market, manage costs more effectively and provide a cleaner industry overall. The energy providers that employ this level of intelligent grid applications will competitively differentiate themselves for consumers and provide higher profits to shareholders.”
  4. Maintenance Issues- A major pain point for the energy and utility sector is maintenance. Unanticipated pipeline bursts, shut downs, interventions, and outages disrupt service and cost millions for organisations. This is where advanced analytics comes to rescue. Sensory data can be analysed to develop a precise approach and proactive (predictive) attitude to maintenance. Unusual stress or load, equipment failure, and shutdowns can be prevented with better data and advanced analytics capabilities. Companies use smart systems and data models for setting up processes and performance metrics that help prepare better.


  1. After reading your article I was amazed. I know that you explain it very well. And I hope that other readers will also experience how I feel after reading your article.


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