Next week, GTM Research gathers a group of experts in grid advancement and distributed power systems to discuss the evolution of big data and analytics applications at the grid edge. The conversation will focus on IT architectures, business processes and the future of software in the electric power industry.

Ben Kellison, Director of Grid Research, will facilitate the meeting with the goal of identifying the most important current and near-future applications in grid-edge analytics among customer, grid, enterprise and portfolio analytics for the utility industry. These discussions will be underpinned by GTM Research's internal reports and data, as well as a perspective provided by Pivotal, a growing player driving value from big data and analytics across many sectors of the economy.

"The council will discuss the shifts in utility software architectures that are reshaping relationships between siloed utility groups, energy service providers, market operators and consumers," said Kellison. "These architectures, coupled with two-way communicating intelligent devices in the field and major integration and data-cleaning efforts, are enabling utilities to explore a multitude of use cases, unlock value and comply with changing regulatory environments."

Representatives from more than 30 companies will come together in Menlo Park next Tuesday, Sept. 9, the day before Soft Grid 2014, Greentech Media's annual conference exploring strategies for utilizing the data generated by grid networks and efficient, intelligent endpoints. Attending organizations will include PG&E, E.ON, Itron, Schneider Electric and more.

The Grid Edge Executive Council meets in person four times per year, covering grid modernization and distributed energy's biggest challenges and opportunities. Currently, it includes more than 65 companies represented by over 100 members. Click here to learn more about the Council.


To learn more about market disruptors and the grid edge, download the free report The Grid Edge: Grid Modernization in the Age of Distributed Generation.