The rapid growth of Internet of Things (IoT) and the advances in mobile network technologies and cloud-based computing offer Mobile Network Operators (MNO) a unique opportunity to monetize their networks and provide a critical link in the IoT value chain. Learn how MNOs can leverage the AI Data Cloud for Telecom to:
Generative AI offers the chance to make AI truly accessible to everyone. But how do you bridge the gap between the model and the user? Applications.
One of the biggest challenges of early applications was their lack of flexibility and portability. When cross-cloud computing became an option, app developers needed a way to scale. Containers were their answer. Now, in the age of generative AI, containers provide lightweight isolation for AI models.
This report answers some of the main questions concerning containers:
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Life sciences organizations continue to face significant challenges. Inflation and increased costs continue to impact the bottom line while supply chain challenges persist. Executives are laser-focused on accelerating drug development and streamlining processes to speed time to market. Life sciences leaders are adopting a robust data strategy, with the modern cloud platform with AI capabilities at its center. As a result, they are eliminating barriers to data collaboration, making smarter, data-driven decisions and speeding innovation.
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Academic medical research centers (AMCs) are essential to healthcare systems worldwide. But they are facing significant obstacles, ranging from long-term financial crises to fierce competition for funding. Now more than ever, the industry is poised to reap the benefits of an effective data cloud solution to meet these challenges and leverage research opportunities.
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Healthcare organizations continue to face significant challenges. Inflation has increased the cost of care for patients, and supply chain challenges persist amidst international conflicts and global financial challenges. Healthcare leaders are adopting a robust data strategy, with the modern cloud platform at its center. As a result, they are eliminating barriers to collaboration, making smarter, data-driven healthcare decisions and speeding innovation.
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A data clean room is more than just a place to share data. Data clean rooms can be used to power in-depth data analysis, measurement and collaboration use cases —all in a privacy-compliant manner— allowing organizations to accelerate drug discovery, improve patient health outcomes and securely use AI. But to realize its many benefits, you need to start with a comprehensive data clean room strategy.
Read this ebook to get practical insights into delivering an effective data clean room initiative, including:
The Data Executive’s Guide to Effective AI provides a roadmap to help data, analytics and AI leaders effectively implement and scale AI initiatives across their organizations. We drew on in-depth interviews with nearly one dozen data executives from Siemens Energy, ServiceNow, State Street and more, working to transform their organizations with new technologies.
This report organizes their experiences around these five milestones:
Updated: May 27, 2025
For financial services and insurance organizations, the more data you have at your fingertips to customize, redefine and improve your business data, the better your decision-making can be.
Whether enhancing quantitative research, improving customer 360 profiling or training large-language models for generative AI use cases, enriching first-party data through data collaboration and sharing can lead your organization to gain an edge over the competition.
In this guide, we’ll explore how financial services institutions can solve critical use cases and leverage Snowflake Marketplace to do just that. Plus, dive into:
The world now generates more data than ever before, and making use of that data hasn’t always been easy. Between 80% to 90% of data is considered unstructured or semi-structured. Data is also the fuel that is feeding the AI revolution. The more high-quality data you can feed into a machine learning (ML) model, the more accurate its outputs are likely to be, which is increasingly critical as ML and AI drive more business decisions.
This is where data engineers come in. A modern data engineering practice produces fast, reliable and quality data for all of an organization’s business units. It can help you easily and securely share data across your organization, ecosystem and more.
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In a business landscape of rapid change and complexity, the imperative to get more value from marketing data continues to drive change. Marketers need to be able to execute full marketing coordination on a persistent data layer in order to strategically segment and plan, drive personalized campaigns and granularly measure their performance — all while protecting customer privacy and preserving customer trust.
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