Acta Scientific Computer Sciences

Review Article Volume 5 Issue 1

Decision Support Systems: A Lifeblood to Organizational Data-based Decision-making

Cheryl Ann Alexander1* and Lidong Wang2

1Institute for IT Innovation and Smart Health, Mississippi, USA
2Institute for Systems Engineering Research, Mississippi State University, Vicksburg, USA

*Corresponding Author: Cheryl Ann Alexander, Institute for IT Innovation and Smart Health, Mississippi, USA.

Received: February 23, 2022; Published: December 13, 2022


Data has become an essential business commodity, from the historical data challenge of an organization and its storage solutions, data management solutions, to algorithms and data solvency considerations. One of the most critical steps in building a competitive organization is finding a functional, data-based, and competent decision-making platform. A good decision support system, therefore, combines historical data with current data, and computational data to empower organizational management to make the most appropriate, and current decisions based on the best possible data for the organization. A decision support system is a systematic tool engineering management may use to make difficult decisions for an enterprise. In this paper, we diagram and outline three possible working decision support systems useful for empowering management with the best tools possible to make key decisions. Firstly, we outline three diagrams of decision support systems and discuss the usefulness, challenges and weaknesses, and strengths. Next, in this review paper, we apply current business situations and look at the usefulness of the models.

Keywords: Decision Support System; Data Warehouse; Big Data; Computational Decision Making; Data Lakes


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Citation: Cheryl Ann Alexander and Lidong Wang. “Decision Support Systems: A Lifeblood to Organizational Data-based Decision-making".Acta Scientific Computer Sciences 5.1 (2023): 67-72.


Copyright: © 2023 Cheryl Ann Alexander and Lidong Wang. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.


Acceptance rate35%
Acceptance to publication20-30 days

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