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IBM and TechD partner to securely share data and power insights with gen AI

IBM Big Data Hub

A coordinated partnership: How these tools work together By using IBM Db2, IBM watsonx Assistant and NeuralSeek, we offer a comprehensive solution that streamlines data management, enhances accessibility, and helps to ensure security and integrity across your enterprise.

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How Secure Is Cloud Storage? Features, Risks, & Protection

eSecurity Planet

When assessing the overall security of cloud storage and choosing a solution tailored to your business, it helps to determine its features, potential risks, security measures, and other considerations. It excels in remote access, scalability, and security, with distributed storage options and privacy adherence capabilities.

Cloud 125
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Reimagine information with Cloud Editions (CE) 24.3

OpenText Information Management

We continue to innovate at a record pace by integrating cloud, security, and AI - plumbing the ecosystem together to enable seamless scalability - to help our customers stay ahead of future business needs. Enhanced efficiency, productivity, user experience, and more effective new hire training.

Cloud 76
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Security Data Lakes Emerge to Address SIEM Limitations

eSecurity Planet

Every security team craves clear visibility into the endpoints, networks, containers, applications, and other resources of the organization. To address that limitation, a new tool is emerging: Security data lakes (SDLs), which might provide a solution that enables unfiltered visibility for security teams. What is SIEM?

Security 117
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The most valuable AI use cases for business

IBM Big Data Hub

But right now, pure AI can be programmed for many tasks that require thought and intelligence , as long as that intelligence can be gathered digitally and used to train an AI system. Generative AI can produce high-quality text, images and other content based on the data used for training.

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The 7 most common data quality issues

Collibra

Data-driven organizations are depending on modern technologies and AI to get the most out of their data assets. But they struggle with data quality issues all the time. Incomplete or inaccurate data, security problems, hidden data – the list is endless. Ambiguous data. Hidden data.

Analytics 106
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Data science vs. machine learning: What’s the difference?

IBM Big Data Hub

It uses advanced tools to look at raw data, gather a data set, process it, and develop insights to create meaning. Areas making up the data science field include mining, statistics, data analytics, data modeling, machine learning modeling and programming.