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

Abstract

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

References

1. Adnan Ahmad Zaelani., et al. "Analysis of Decision Support System with Analytical Hierarchy Process Method”. Journal of Physics: Conference Series. Vol. 1783. No. 1. IOP Publishing (2021).
2. Baesens Bart., et al. "Data engineering for fraud detection”. Decision Support Systems150 (2021): 113492.
3. Wuryani Eni., et al. "Analysis of decision support system on situational leadership styles on work motivation and employee performance”. Management Science Letters2 (2021): 365-372.
4. Voorberg S., et al. "Decisions for information or information for decisions? Optimizing information gathering in decision-intensive processes”. Decision Support Systems151 (2021): 113632.
5. Aggarwal Lakshita., et al. "Multi-criterion intelligent decision support system for COVID-19”. Applied Soft Computing101 (2021): 107056.
6. Coussement Kristof and Dries F Benoit. "Interpretable data science for decision making”. Decision Support Systems150 (2021): 113664.
7. Jabbari Mona., et al. "A collaborative decision support system for multi-criteria automatic clustering”. Decision Support Systems153 (2022): 113671.
8. Topuz Kazim and Dursun Delen. "A probabilistic Bayesian inference model to investigate injury severity in automobile crashes”. Decision Support Systems150 (2021): 113557.
9. Chaudhuri Neha., et al. "On the platform but will they buy? Predicting customers' purchase behavior using deep learning”. Decision Support Systems149 (2021): 113622.

Citation

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

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.