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Sensors, signals and synergy: Enhancing Downer’s data exploration with IBM

IBM Big Data Hub

With over 200 trains and a multitude of sensors, Downer has accumulated a vast amount of data. Fine-tuning data insights with enhanced collaboration and expertise Downer continuously works to enhance operational efficiency, minimizing maintenance costs and impacts to transportation schedules.

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Using generative AI to accelerate product innovation

IBM Big Data Hub

Stacking strong data management, predictive analytics and GenAI is foundational to taking your product organization to the next level. Addressing customer inquiries with an AI-driven chatbot ChatGPT distinguished itself as the first publicly accessible GenAI-powered virtual chatbot.

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Will generative AI make the digital twin promise real in the energy and utilities industry?

IBM Big Data Hub

It uses real-world data (both real time and historical) combined with engineering, simulation or machine learning (ML) models to enhance operations and support human decision-making. We use the models to create individual twins of assets which contain all the historical information accessible for current and future operation.

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How to achieve high-impact personalization at scale with managed marketing services

IBM Big Data Hub

IBM Consulting offers managed marketing services that help clients improve their customer journeys by bringing together strategy, experience, technology and operations. These components come together to create end-to-end operations efficiency, experience optimization and data optimization. Did that action turn into a lead?

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AI in commerce: Essential use cases for B2B and B2C

IBM Big Data Hub

Poorly run implementations of traditional or generative AI in commerce—such as models trained on inadequate or inappropriate data—lead to bad experiences that alienate consumers and businesses. This includes trust in the data, the security, the brand and the people behind the AI.

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Decision Making with Uncertainty Requires Wideward Thinking

Andrew White

As a result, Data, Analytics and AI are in even greater demand. Demand from all these organizations lead to yet more data and analytics. In the realm of AI and Machine Leaning, data is used to train models to help explore specific business issues or questions. With data comes quality issues. Everything Changes.

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IT leaders get creative to fill data science gaps

CIO Business Intelligence

For the past few years, IT leaders at a US financial services company have been struggling to hire data scientists to harness the increasing flood of incoming data that, if used properly, could improve customer experience and drive new products. It’s exponentially harder when it comes to data scientists.