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

Author

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  • Carlton E. Sapp

    (National Science Foundation, 4201 Wilson Boulevard, Suit 455, Arlington, VA. 22230, USA;
    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)

  • 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)

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.

Suggested Citation

  • Carlton E. Sapp & Thomas Mazzuchi & Shahram Sarkani, 2014. "Rationalising Business Intelligence Systems and Explicit Knowledge Objects: Improving Evidence-Based Management in Government Programs," Journal of Information & Knowledge Management (JIKM), World Scientific Publishing Co. Pte. Ltd., vol. 13(02), pages 1-18.
    • Handle: RePEc:wsi:jikmxx:v:13:y:2014:i:02:n:s021964921450018x
    DOI: 10.1142/S021964921450018X
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    References listed on IDEAS

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    1. Lidong Wang, 2012. "RFID-based information technology and management in agriculture and food supply chains," International Journal of Information Technology and Management, Inderscience Enterprises Ltd, vol. 11(3), pages 225-239.
    2. Lurie, Nicholas H, 2004. "Decision Making in Information-Rich Environments: The Role of Information Structure," Journal of Consumer Research, Journal of Consumer Research Inc., vol. 30(4), pages 473-486, March.
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