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.
EZINE:
In this week's Computer Weekly, we talk to PepsiCo's digital director about delivering innovation in customer experience. Our first buyer's guide of 2022 examines hybrid cloud storage. And we find out how Arkwright and Granville from the BBC sitcom Open All Hours are inspiring retailers 40 years on. Read the issue now.
EGUIDE:
In this e-guide, gain essential knowledge of recent developments around IT services. Read on to learn why success entails more than delivering snazzy services, and why enterprise architects are working to bring SOA and master data management (MDM) efforts together.
WHITE PAPER:
Many organizations have reconsidered their commitments to data modeling in the face of NoSQL and big data systems, as well as XML information management. However, should you really be shifting focus away from data 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.
EGUIDE:
Building predictive models is a complex, time-consuming process that demands a lot of skill. This expert e-guide reveals key steps to develop and implement a successful predictive analytics initiative. Discover how you can monitor the accuracy of predictive models, identify ideal candidates for predictive analytics teams, and more.
WHITE PAPER:
This paper highlights the performance and scalability potential of InfoSphere DataStage 8.1 based on a benchmark test simulating a data warehouse scenario. The benchmark is designed to use the profiled situation to provide insight about how InfoSphere DataStage addresses customers' key FAQs when designing their information integration architecture.
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.
