Analytics Data science Libraries Security 
Thales Cloud Protection & Licensing
NOVEMBER 19, 2018
The age of Big Data is upon us. And, as more data is available for analytical purposes, more sensitive and private information is at risk. Protecting the confidentiality and integrity and of warehoused data and ensuring that access is controlled is vital to keeping that data secure. respondents.”.
Collibra
MAY 23, 2022
This is, hands down, the most frequently cited barrier to data democratization—especially among organizations lagging behind the industry leaders. As with any organization-wide change, there will be hurdles to overcome as you train your team on new data-related tools and processes. Security risks. Secure buy-in.
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ARMA International
SEPTEMBER 13, 2021
Some technology trends such as real-time data analytics are on-going, while others are more recent, such as blockchain. Organizations use DRM technologies and solutions to securely manage intellectual property (IP) rights and monetize the content. AI using machine learning (ML) involves processing samples of data to learn.
IBM Big Data Hub
OCTOBER 20, 2023
AI platforms offer a wide range of capabilities that can help organizations streamline operations, make data-driven decisions, deploy AI applications effectively and achieve competitive advantages. Visual modeling: Combine visual data science with open source libraries and notebook-based interfaces on a unified data and AI studio.
IBM Big Data Hub
MAY 9, 2023
It provides self-service access to high-quality, trustworthy data, enabling users to collaborate on a single platform where they can build and refine both new, generative AI foundation models as well as traditional machine learning systems. Watsonx has three components: watsonx.ai , watsonx.data and watsonx.governance.
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