Question 3 Assignment 2 Deadline: March 24, 2019

Pg. 01 Question Three Assignment 2 Deadline: Day 24/3/2019 @ 23:59 [Total Mark for this Assignment is 6] Decision Support Systems IT445 College of Computing and Informatics

Identify the key issues involved in the management and development of Decision Support Systems (DSS). Discuss the importance of data preparation in data mining, specifically differentiating between data cleaning, data integration, data selection, and data transformation. Provide detailed explanations of each aspect, their roles in ensuring effective data analysis, and how they contribute to the successful development of DSS.

Paper For Above instruction

Decision Support Systems (DSS) are computer-based tools that assist managers and decision-makers in making informed decisions by analyzing large volumes of data. The effective management and development of DSS involve addressing various challenges related to data quality, system integration, user interaction, and ongoing system maintenance. Central to these challenges is data preparation—the process of transforming raw data into meaningful input for analysis. Data preparation encompasses several critical steps, including data cleaning, data integration, data selection, and data transformation, each playing a vital role in ensuring the accuracy, efficiency, and reliability of DSS.

Issues in the Management and Development of DSS

Developing a robust DSS requires navigating multiple complexities. One of the primary issues is ensuring data quality. Raw data often contains inconsistencies, inaccuracies, and missing values that can compromise decision-making. Data cleaning addresses this by identifying and correcting errors, removing duplicates, handling missing data, and standardizing formats (Rahman, 2018). Poor data quality can lead to flawed analysis and unreliable outputs, thereby undermining the system’s purpose.

Another significant challenge is integrating data from diverse sources. Organizations typically gather data from various internal and external systems, which may have different formats, standards, and levels of completeness. Data integration involves consolidating these heterogeneous data sources into a single, coherent dataset. Effective integration ensures comprehensive analysis and prevents data silos that can impede decision-making processes (Katal et al., 2013).

In addition, data selection is crucial. DSS often deals with large datasets, but not all data are pertinent to a specific decision context. Data selection involves choosing relevant data subsets based on the analytical needs. Proper data selection reduces computational overhead and improves the relevance and clarity of insights derived from the system (Chandola et al., 2017).

Data transformation prepares raw data for analysis by converting it into suitable formats. This process includes normalization, aggregation, and encoding categorical variables, which facilitate statistical modeling and machine learning algorithms. Transforming data correctly enhances the performance of analytical models used within DSS (Han et al., 2011). Effective data management along these dimensions ensures that DSS remain accurate, reliable, and capable of supporting complex decision-making tasks.

Role of Data Preparation in Data Mining

Data preparation is an integral stage within the data mining process, directly impacting the quality and usefulness of analytical results. Proper data cleaning minimizes noise and errors that can skew analysis. Data integration ensures that all relevant information is accessible in a unified format, reducing redundancy and inconsistency. Data selection focuses the analysis on relevant data subsets, which streamlines computational efforts and improves result interpretability. Data transformation allows the data to conform to the requirements of various analytical models, such as normalizing features or encoding categorical variables for machine learning algorithms (Wang & Wang, 2019).

Without thorough data preparation, the outcome of data mining efforts can be compromised, leading to inaccurate insights, poor predictive models, and ultimately, flawed decision-making. Quality data is foundational to the success of any DSS, making data preparation an essential component of the development and management process (Fayyad et al., 1996).

Conclusion

In conclusion, the development and management of Decision Support Systems are complex processes that require careful attention to data quality, integration, relevance, and representation. Addressing issues related to data cleaning, integration, selection, and transformation ensures the system's reliability and effectiveness. Ultimately, meticulous data preparation enhances the decision-making capabilities of DSS and facilitates more accurate, timely, and informed organizational decisions.

References

• Chandola, V., Banerjee, A., & Kumar, V. (2017). Anomaly detection: A survey. ACM Computing Surveys, 41(3), 1-58.
• Fayyad, U., Piatetsky-Shapiro, G., & Smyth, P. (1996). Knowledge discovery and data mining: Towards a unifying framework. In Proceedings of the 2nd International Conference on Knowledge Discovery and Data Mining (pp. 82-88).
• Han, J., Kamber, M., & Pei, J. (2011). Data mining: Concepts and techniques. Morgan Kaufmann.
• Katal, A., Wazid, M., & Goudar, R. H. (2013). Big data: Issues, challenges, tools, and practices. In 2013 Sixth International Conference on Contemporary Computing (IC3) (pp. 404-409). IEEE.
• Rahman, M. (2018). Data cleaning techniques in data mining. Journal of Data Analysis, 17(2), 45-58.
• Wang, J., & Wang, J. (2019). Data mining and analysis: Fundamental concepts and algorithms. Wiley.