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Article citations


S. H. Hung, D. C. Yen and H. Y. Wang, “Applying Data Mining to Telecom Churn Management,” Ex-pert Systems with Applications, Vol. 31, No. 3, 2006, pp. 515-524. doi:10.1016/j.eswa.2005.09.080

has been cited by the following article:

  • TITLE: Replacing the Annual Budget with Business Intelligence Driver-Based Forecasts

    AUTHORS: Lisa De Leon, Patricia D. Rafferty, Richard Herschel

    KEYWORDS: Business Intelligence; Budget; Forecast; Rolling Forecast; Driver-Based; Strategic Planning; Financial Planning

    JOURNAL NAME: Intelligent Information Management, Vol.4 No.1, January 4, 2012

    ABSTRACT: The fixed annual budget process can be a cumbersome and static process, often failing to deliver intended benefits. Typically detached from business operations and strategic planning goals, the annual budget suffers from inherent weaknesses caused by a lack of business intelligence regarding its underlying assumptions. This weakness is well documented in existing literature and there is ample evidence of improved alternatives to static corporate financial planning. One such alternative utilizes business intelligence as an essential component in the annual budget process, along with rolling forecasts as a critical tool. Utilizing business intelligence supported, driver-based rolling forecasting can align an organization’s budget process with strategic objectives and can further the operational and financial strength of an organization, as well as maximize shareholder value. In order to fully explore this topic, this article will present a review of the conventional annual budget process and the manner in which an approach that bases financial forecasts on business intelligence drivers can align operations with strategic objectives and add value to an organization. An assessment of intelligence-supported, driver-based rolling forecasting will also be presented, demonstrating an im- proved approach to the traditional annual budgeting process.