Rationalising Business Intelligence Systems and Explicit Knowledge Objects: Improving Evidence-Based Management in Government Programs

Carlton E. Sapp (), Thomas Mazzuchi () and Shahram Sarkani ()
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Carlton E. Sapp: National Science Foundation, 4201 Wilson Boulevard, Suit 455, Arlington, VA. 22230, USA;
Thomas Mazzuchi: The George Washington University, School of Engineering and Applied Science, Department of Engineering Management and Systems Engineering, 1776 G. Street, N.W., Suite 145, Washington, D.C. 20052, USA
Shahram Sarkani: The George Washington University, School of Engineering and Applied Science, Department of Engineering Management and Systems Engineering, 1776 G. Street, N.W., Suite 145, Washington, D.C. 20052, USA

Journal of Information & Knowledge Management (JIKM), 2014, vol. 13, issue 02, 1-18

Abstract: Public sector programs often fail to leverage their business intelligence systems and explicit knowledge objects to drive efficiency and effectiveness. Given the current federal fiscal environment and the need for effective government — a catalyst to the requirement to use "evidence and rigorous evaluation in budget, management, and policy decisions" (OMB Memorandum M-12-14) — federal programs look to business intelligence as an evidence-based decision-making practice leading to a more lean government, improving efficiency and effectiveness. However, cost overruns, technical obstacles, and next-generation information challenges stemming from pervasive computing can reduce any perceived value of utilising explicit knowledge systems to support evidence in decision making. Through the evaluation of five diverse projects tasked to address the use of evidence in decision-making practices, this research shows that achieving contextualisation of information requirements, stakeholder alignment, and the complexity/feasibility of information integration are key factors that should be analysed to improve the evidence-based decision-making practice in government programs, and may be accomplished through a systematic approach, such as the rationalisation of business intelligence systems. Thus, a rationalisation framework is provided to facilitate the management of business intelligence systems geared towards a more efficient and effective use of explicit knowledge.

Keywords: Business intelligence systems; explicit knowledge objects; evidence-based management; rationalisation; intelligence systems; government programs (search for similar items in EconPapers)
Date: 2014
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DOI: 10.1142/S021964921450018X

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