Use Cases

Sectors and Industries

Who is Using Predictive Data Analytics for Commodity Price Forecasts Today?

Predictive analytics are underway for different needs in most, even all industry sectors.

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Energy Industry

The energy industry is going through significant transformation with the rise of smart sensors, smart meters, and IoT-based technologies. This transformation is driven by the opportunity to use the massive amount of data generated from oil wells, generation stations, utility and gas grids and other sensors which helps to derive meaningful insights for operational decision making. Such data is used to build robust, up-to-the-minute models and energy price forecasts.

Food Industry

The food industry is one of the world’s largest and most important business sectors. Big data on specific commodities like wheat and pork is assessed for overall planning and operational and financial optimization. Such data-driven analytics empowers food industry businesses with important decision-making capabilities to improve product innovation, sales, profits, marketing ROI, and greater customer satisfaction and loyalty.

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Metal Industry

Commodity prices go up and down, but traders and producers need a much better analysis of future trends than simply relying on intuition and judgement. For metal commodities like iron ore, big data analytics can bring significant advantages for traders and companies that can make informed commodity prices forecasting that incorporates macroeconomic trends, segment insights, and multiple potential scenarios.

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