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This white paper discusses: What is an industry model? What is the value of industry models? Considerations for building or buying data models IBM Industry Models—business and technical blueprints Reducing time to value with IBM Industry Models
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This paper looks at the historical development of BI applications and BI technology, and concludes that five common Styles of BI have evolved during the past decade - each style representing a certain characteristic usage.
WHITE PAPER:
A well thought through backup plan and configuration will go a long way to ensure that you can recover your database - when a system or user error deletes important data stored in MySQL - without impacting your business.
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This White Paper explores how traditional approaches fall short, what the automated solution and its benefits look like, how the on-demand delivery model best represents the integrated stack necessary for automation, and how the automated, on-demand model brings the benefits of BI to a far broader audience than ever before.
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This white paper identifies seven levels of claims automation and provides a roadmap that can be followed to enhance claims processing for insurance companies worldwide.
WHITE PAPER:
Learn from the success one of the largest U.S. retailers who implemented an integrated global HR outsourcing solution that combined process, technology, domain, and service delivery expertise.
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Use insight produced through analytics to improve performance and gain a strategic advantage. Learn how you can employ an enterprise-wide approach for maintaining data and using analytics for improved decision making.
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In this paper, learn how retailers are using predictive analytics to uncover patterns in the products purchased together to differentiate assortment, merchandise stores and develop combined product offers to drive sales and profits.
WHITE PAPER:
Access this white paper to learn the benefits of advanced data mining, including a greater return on investment. It offers practical guidance for incorporating additional data types and sources, expanding the scope of data mining projects and deploying results more effectively throughout your organization.
