The power of AI-driven insights with IBM Cloud Pak for Data. Drive agility, reduce costs, and enhance security with a unified data platform.
IBM Cloud Pak for Data is a unified data and AI platform designed for hybrid and multi-cloud environments. It streamlines data access, governance, and analysis, enabling faster, more accurate decision-making. With AI-driven automation and seamless integration across on-premises and cloud deployments, businesses can optimize data workflows and enhance collaboration effortlessly.
Lightning-fast data access, 8 times speedier, while slashing costs across cloud and on-premises data sources.
Free up data engineers for high-value tasks with 25-65% fewer ETL requests.
Say goodbye to $27 million in manual cataloging costs, just as IBM Global Chief Data Office did.
According to Forrester, enterprises leveraging IBM Cloud Pak for Data achieve an 86% return on investment.
IBM AutoAI independently develops and exports optimized models as Python code.
Execute SQL queries across multiple databases, including Oracle, Hadoop, and NoSQL sources.
A leading telecommunications company aimed for digital transformation using AI and machine learning. IBM Cloud Pak for Data proved pivotal in expediting AI projects by simplifying data stages. The outcome: accelerated innovation, enabling AI implementations within weeks, not months. Telecommunications company now stands at the forefront of agile, customer-centric solutions.
A leading bank leveraged IBM Cloud Pak for Data System to enhance client experiences and streamline data analytics. The solution resulted in a unified platform for end-to-end enterprise analytics, consolidating customer information from various systems and ensuring compliance with privacy regulations like CCPS
A leading Healthcare researcher company, in collaboration with IBM, employed AI solutions on IBM Cloud Pak for Data to predict sepsis among millions of members, reducing the AI development and deployment lifecycle from 12 months to 6 weeks. The integrated platform eliminated data silos, enabled effective model monitoring, and projected cost savings of $48,000 through the avoidance of inpatient sepsis submissions. This case exemplifies the successful application of AI in healthcare for improved predictive capabilities and operational efficiency.
Your data-driven journey starts here.
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