Transformational Analytics in Energy & Mining

Discovery in Energy & Mining

Energy leaders are harnessing AI analytics for creative solutions to improve efficiency, drive down costs, maximize asset values and unlock new operating models.

More predictable, more productive

Identify high-risk assets and recommend prescriptive actions before failures occur. Use sensors and digital twins to minimize asset downtime and define optimal maintenance patterns.

Don’t compromise when meeting emission goals

Integrate diverse datasets to improve climate event impact-forecasting, ensure accurate emissions calculation, and prioritize reduction measures using continuous operational data.

Improve workplace safety with root-cause analysis

Connect relevant internal and external data sources to uncover risk contributors commonly missed by classic injury reports.

Improve fuel efficiency across the entire fleet

Join never-before-connected data sets (tyre pressure, refueling data, fuel & oil quality, road quality and gradient) to modify operator behavior and generate significant fuel savings.

AI: the power sector’s untapped resource

There are many challenges facing the power sector – but changes in operating models, development of new capabilities, and innovative technologies all offer pathways to competitive advantage.

New data models for a new world

Nearly all existing models have been invalidated by the coronavirus pandemic. They will presumably be invalidated again as the situation evolves as the world recovers. Even after recovery, it is unlikely that we will return to where we started before the crisis began. So how does that affect our data models?

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Applications for Discovery in Energy & Mining

Location Optimization

Pinpoint the best locations to yield the most optimal customer experience and business results.

Operational efficiency

Analyze operational management — supply chain, logistics, field — and determine new factors that affect performance.

Demand Forecasting

Analyze enormous volumes of various data to forecast demand, supply, quality issues, and market trends.

Customer retention

Accurately identify customers at the highest risk of lapse for effective, personalized retention campaigns.

Automated Hypothesis Generation

When machine-learning systems produce ideas, not just test them.

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Generative AI for data analytics: the future of enterprise sense-making

In the case of enterprise data analytics, generative AI will radically change the way we interrogate our data to explore, react to and shape our business realities.

Generative AI for data analytics: the future of enterprise sense-making

New data models for a new world

Nearly all existing models have been invalidated by the coronavirus pandemic. They will presumably be invalidated again as the situation evolves as the world recovers. Even after recovery, it is unlikely that we will return to where we started before the crisis began. So how does that affect our data models?

The Game Changers: AI in Banking

AI analytics helps major banks stay connected to their customers – especially during the dramatic rise of online financial management use. As leading banks apply a range of strategies to navigate the new normal, AI analytics is opening greenfield opportunities to raise profits, combined with agility and ethical responsibility.