EGUIDE:
In this e-guide, read more about the trends that are shaping the demand for AI and how organizations including healthcare service providers and F1 racing teams are leveraging technology on their own terms.
WHITE PAPER:
Uncertainty makes strategic planning complex. Removing uncertainty can create unlimited business value. There are solutions to help organizations overcome uncertainty and achieve results. Read this white paper to learn how to drive strategic planning with predictive modeling.
VIDEO:
How do we make sense of rapidly growing amounts of business data? IBM is helping CIOs take control of their data and turn it into not just organized information, but actionable intelligence. Access this video to learn more.
WHITE PAPER:
The key enterprise risk management (ERM) issue for many financial institutions is to get enriched data in a single place in order to report on it. Learn best practices for data management that are critical for ERM.
WHITE PAPER:
Read this white paper and learn how the data warehouse, metadata and modeling environment will be transformed in the next few years — and what you need to do to leverage it for your business, the major components of DW 2.0 architectures, and key modeling and metadata management strategies for DW 2.0.
EZINE:
Explore how predictive analytics and data visualization can help you make sense of your analytics, how a security expert/cryptographer is using big data analytics and the cloud to make open source code safe for app developers and off-limits to hackers, and more.
WHITE PAPER:
Discover how a private technical compute cloud can help your business provide access to remote, full 3D technical visualization and rendering capabilities that can help to enhance collaboration and productivity.
WHITE PAPER:
This white paper describes how IBM's Information Server FastTrack accelerates the translation of business requirements into data integration projects. Data integration projects require collaboration across analysts, data modelers and developers.
WHITE PAPER:
In the following paper, we briefly describe, and illustrate from examples, what we believe are the “Top 10” mistakes of data mining, in terms of frequency and seriousness. Most are basic, though a few are subtle. All have, when undetected, left analysts worse off than if they’d never looked at their data.
