Energy and Utility

The Effect of the IoT and Digitization on Energy and Utility

Energy and utility companies are the most impacted by the revolution in IoT, cloud, and edge computing. Smart grids, distributed generation, connected cities, and renewable energy are changing business models and the regulatory framework. Energy and utility and energy companies are also taking a mobile-first approach to connecting with their customers and internal stakeholders. With the invent of smart cars, smart homes, and other smart products, energy and utility companies depend on advanced analytics for metering, billing, oil exploration, outage management, and other activities. It becomes clear that this industry is being significantly impacted by is the enormous amount of data being generated by machines.

The global energy and utilities analytics market size is expected to grow from USD 2.0 billion in 2020 to USD 4.3 billion by 2025, at a CAGR of 16.3% during the forecast period.
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Data Dependence Can Be Challenging

Analyzing machine generated data has shown tremendous potential for generating business insights and fueling revenue growth. But machine generated data comes at a cost. Connected devices can quickly generate petabytes of data which can easily overwhelm enterprise data warehouses due to the required cost and time. Also, physical data warehousing cannot support real-time insights, so companies are seeking new warehousing solutions.

Data Virtualization to the Rescue

Data virtualization offers the most comprehensive solution to these challenges. Unlike legacy data integration technologies, which depend on physical data movement and store data in expensive data repositories, data virtualization technology can offload large data volumes to inexpensive Hadoop repositories, or combine multiple primary data stores, on the fly; this enables energy and utility companies to decrease TCO while increasing ROI. Because data virtualization can combine data in real time, even across structured, semi-structured, or unstructured sources, it can support real-time analytics based on streaming data, social media data, or sensor based data. The inherent agility and flexibility of data virtualization enables IT teams to alter the data architecture on an as-needed basis, as newer technologies and infrastructures emerge.