Energy Data Management systems have long been an integral and central part of the operational landscape of energy companies, but their role is now evolving dramatically in response to the growing volume and complexity of data. Let us consider forecasting as an example area. In forecasting applications, this transformation now means that data extends beyond just the traditional weather data historically utilized for power and gas consumption forecasting. Now, it also includes critical production forecasts for wind, solar, and hydro power facilities underpinned by a richer dataset that encompasses not just temperature, precipitation, and wind strength, but also real-time satellite imagery for precise forecasting, for example. The introduction of intraday and continuous power trading now also necessitates the need for near-real-time forecasting imposing more stringent demands on the update frequency and performance of the software processing these critical time series.
