Machine learning is coming to analytics but there are hurdles to overcome first
The sophistication of energy firms’ analytics has increased substantially in recent years, thanks to a ramp-up in available data, increased computational power through cloud computing and a hike in statistical power through the integration of offerings such as the open-source language R. The next stage in the race to gain a competitive advantage will involve applying machine learning to data, says Aiman El‑Ramly, chief operations officer at ZE PowerGroup – a data software and consulting firm that specialises in energy and commodities. The firm, which launched its ZEMA data integration and analytics platform 20 years ago, provides across-the-board commodities data and helps firms achieve their analytics goals.
While machine learning offers huge potential for analytics development, it is fraught with challenges at this stage, says El‑Ramly.
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