Data-sharing is an immense enabler of scientific advancements. When researchers choose to make their data publicly available, they are allowing their work to contribute far beyond the original findings. Making research data more accessible also fosters transparency and trust in their work, enables other researchers to reproduce and validate their findings, and ultimately, contributes to the pace of scientific discovery by allowing others to reuse and build on top of the data. Fortunately, cloud technology has made it easier than ever to share data across major research institutions. Empowered by tools that include Microsoft Azure, researchers are relying more and more on digital collaboration and open source software to drive scientific breakthroughs.

Watch this video to learn how researchers at Australian National University (ANU) are using Azure to build on each other’s findings and avoid a siloed approach to data that leads to slower research pipelines.

From autonomous vehicles, predictive analytics applications, and facial recognition, to chatbots, virtual assistants, cognitive automation, and fraud detection, the use cases for AI span dozens of industries. Regardless of the AI application, though, these use cases all have a common aspect. After implementing thousands of AI projects, experts have come to realize that despite all of the diversity in applications, AI use cases fall into one or more of seven common patterns. One of them—the pattern-matching pattern—has allowed machines to digest large amounts of data to identify patterns, anomalies, and outliers in the data, so organizations can unearth previously undiscovered insights in their datasets.

In this article, you’ll learn how pattern-matching is being put to use in today’s organizations to prevent fraud, find the best job candidates, manage inventories in times of crisis, and empower data scientists with new perspectives on how to improve critical processes.

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One of the main challenges that modern researchers encounter centers on the size of their datasets. As research projects become increasingly more digital, the vast volumes of data that research generates demands enormous raw computing power, for both storage and computational capacity, which most organizations simply didn’t have access to. Cloud technology and artificial intelligence (AI) address this need, allowing researchers to free themselves from the constraints of their physical hardware. Cloud-based approaches can also boost the speed and efficiency of publishing, increasing the impact of research publications.

Subscribe now to learn more about the data management and automation capabilities of the Microsoft cloud and how they’re enabling breakthrough research projects around the world.

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This video showcases how Allstate is transforming customer support with artificial intelligence. With Azure Cognitive Services, Allstate is capturing insights from customer calls to enable a more seamless customer experience.