Put your Data at Work: Energy & Utilities

How Big Data can improve Customer Satisfaction and Operational Excellence

Customer satisfaction, target marketing for energy efficiency and demand response, evaluation of the effectiveness of demand response and energy efficiency programs, product development of time-based rates or demand response incentives, analytical tools provided to customers to help them reduce their energy bills, understanding how customers prefer to receive communications about outages.

​Improving operations, pinpointing the location of outages for workforce deployment, scanning for potential security breaches, detecting fraud and theft and identifying unbilled accounts, performing analysis of faults or voltage irregularities.

How Big Data can support Utilities plant managers

Advances in instrumentation, process automation, and collaboration multiply the available volume of new types of data like sensor, geolocation, weather and seismic data. These can be combined with “human-generated” data like market feeds, social media, email, text, and images for new insight. Plant Managers may benefit Big Data Analytics such as:

• Outage excellence: the target is the analysis of data coming from machines and planned outages to optimize outage periods based on real parts status.
• Predictive quality management: the target is transforming the actual way to manage quality data as non-conformities and defects to effectively predict, control and eliminate defects that are affecting technical operations.
• Condition based maintenance: the target is the analysis of machines maintenance data to optimize planned events. The purpose of this development is to propose an accurate and dedicated maintenance plan. Traditionally, operators gathered data on the status of pumps and wells through physical inspections (often in remote locations). This meant that inspection data was sparse and difficult to access, particularly considering the high value of the equipment in question and the potential health and safety impacts of accidents. Now sensor data can stream into Hadoop from pumps, wells and other equipment much more frequently—and at lower cost—than collecting the same data manually. This helps guide skilled workers to do what sensors cannot: repair or replace machines. The machine data can be enriched with other data streams on weather, seismic activity or social media sentiment, to paint a more complete picture of what is happening in the field. Algorithms then parse that large, multifaceted data set in Hadoop to discover subtle patterns and compare expected with actual outcomes. Data-driven, preventative upkeep keeps equipment running with less risk of accident and lower maintenance costs.