Citation :

Ankit Bansal, 2024. "Unlocking Data Potential: How Data Modelling Enhances Visualization Readiness" ESP International Journal of Advancements in Science & Technology (ESP-IJAST) Volume 2, Issue 3: 40-48.

Abstract :

In the day and age of exponential data, not only has volume increased, but so has demand for meaningful aggregation, processing and representation of information. Data modelling can be recognised as a basic process for improving data preparedness for visualisation. In data modelling, data requirements of a specific domain are depicted based on a structure that can be implemented in a way that is neutral to the implementation scheme. Data modelling formalisms define the elements of the model that have to be developed. With logical data modelling, you have a data model. Data modelling is one of the key procedures in data management as it helps to build up ideas about data items and their relationships. This paper focuses on three distinct stages of data modelling: conceptual, logical, and physical data models and their integration. Furthermore, the paper analyses how data modelling supports and improves data visualisation as it organises data correctly to produce accurate representations and meaningful visualisations. By integrating data models, it becomes possible to convert large datasets into informative visualisations, thus facilitating better understanding and decision-making. The challenges of data modelling, such as data quality and integration from multiple sources, are also discussed, along with their impact on data visualisation readiness.

References :

[1] K. Patel, “Quality Assurance In The Age Of Data Analytics: Innovations And Challenges,” Int. J. Creat. Res. Thoughts, vol. 9, no. 12, pp. f573–f578, 2021.

[2] P. Kantha, V. K. Sinha, D. Srivastava, and B. Sah, “Unlocking the Potential: The Crucial Role of Data Preprocessing in Big Data Analytics,” in 2023 1st DMIHER International Conference on Artificial Intelligence in Education and Industry 4.0, IDICAIEI 2023, 2023. doi: 10.1109/IDICAIEI58380.2023.10406577.

[3] H. S. Chandu, “Enhancing Manufacturing Efficiency: Predictive Maintenance Models Utilizing IoT Sensor Data,” IJSART, vol. 10, no. 9, 2024.

[4] K. Patel, “An Analysis of Quality Assurance Practices Based on Software Development Life Cycle (SDLC) Methodologies,” J. Emerg. Technol. Innov. Res., vol. 9, no. 12, pp. g587–g592, 2022.

[5] S. Dwivedi and L. Chourasiya, “Data Modeling: A Perspective In Changing Database Scenario,” Webology, vol. 19, no. 3, pp. 2151–2163, 2022.

[6] M. R. S. and P. K. Vishwakarma, “THE ASSESSMENTS OF FINANCIAL RISK BASED ON RENEWABLE ENERGY INDUSTRY,” Int. Res. J. Mod. Eng. Technol. Sci., vol. 06, no. 09, pp. 758–770, 2024.

[7] H. Sinha, “Predicting Bitcoin Prices Using Machine Learning Techniques With Historical Data,” Int. J. Creat. Res. Thoughts, vol. 12, no. 8, 2024, doi: 10.3390/e25050777.

[8] L. Zhou, “An Introduction to Data Visualization,” Highlights Sci. Eng. Technol., 2023, doi: 10.54097/hset.v31i.4813.

[9] A. P. A. S. Neepa kumari Gameti, “Innovations in Data Quality Management: Lessons from the Oil & Gas Industry,” Int. J. Res. Anal. Rev., vol. 11, no. 3, pp. 889–895, 2024.

[10] Z. Lingli and Z. Renliang, “Analysis and design for data integration system of urbanization based on GIS open source technology and Arc Engine,” in Proceedings - 2009 International Conference on Environmental Science and Information Application Technology, ESIAT 2009, 2009. doi: 10.1109/ESIAT.2009.497.

[11] T. Hongxun et al., “Data quality assessment for on-line monitoring and measuring system of power quality based on big data and data provenance theory,” in 2018 3rd IEEE International Conference on Cloud Computing and Big Data Analysis, ICCCBDA 2018, 2018. doi: 10.1109/ICCCBDA.2018.8386521.

[12] A. P. A. Singh, “Best Practices for Creating and Maintaining Material Master Data in Industrial Systems,” vol. 10, no. 1, pp. 112–119, 2023.

[13] A. P. A. Singh, “STRATEGIC APPROACHES TO MATERIALS DATA COLLECTION AND INVENTORY MANAGEMENT,” Int. J. Bus. Quant. Econ. Appl. Manag. Res., vol. 7, no. 5, 2022.

[14] S. G. Jubin Thomas, Piyush Patidar, Kirti Vinod Vedi, “Predictive Big Data Analytics For Supply Chain Through Demand Forecastin,” Int. J. Creat. Res. Thoughts, vol. 10, no. 06, pp. h868–h873, 2022.

[15] O. Llaha, “Data Visualization: A brief review,” pp. 1–16, 2021.

[16] I. A. Parfenova, L. Y. Ismailova, S. V. Kosikov, and V. E. Wolfengagen, “Multi-model visualization based on integration of data models in semantic network environment,” in Procedia Computer Science, 2018. doi: 10.1016/j.procs.2018.11.092.

[17] S. G. Jubin Thomas, Kirti Vinod Vedi, “ARTIFICIAL INTELLIGENCE AND BIG DATA ANALYTICS FOR SUPPLY CHAIN MANAGEMENT,” Int. Res. J. Mod. Eng. Technol. Sci., vol. 06, no. 09, 2024, doi: DOI: https://www.doi.org/10.56726/IRJMETS61488.

[18] S. Pandey, “ENHANCING DATA PROTECTION AND COMPLIANCE IN HUMAN CAPITAL MANAGEMENT SYSTEMS THROUGH INTEGRATED ACCESS REQUEST AND APPROVAL WORKFLOWS,” Int. J. Core Eng. Manag., vol. 7, no. 7, pp. 35–41, 2023.

[19] V. K. Yarlagadda and R. Pydipalli, “Secure Programming with SAS: Mitigating Risks and Protecting Data Integrity,” Eng. Int., vol. 6, no. 2, pp. 211–222, 2018.

[20] W. Nie, Q. Zhang, Z. Ouyang, and X. Liu, “Design of big data integration platform based on hybrid hierarchy architecture,” in Proceedings - 2021 IEEE 15th International Conference on Big Data Science and Engineering, BigDataSE 2021, 2021. doi: 10.1109/BigDataSE53435.2021.00028.

[21] A. J. Hanson, “A transparently scalable visualization architecture for exploring the universe,” IEEE Trans. Vis. Comput. Graph., 2007, doi: 10.1109/TVCG.2007.2.

[22] V. Biagi and A. Russo, “Data Model Design to Support Data-Driven IT Governance Implementation,” Technologies, 2022, doi: 10.3390/technologies10050106.

[23] H. J. Schulz, M. Angelini, G. Santucci, and H. Schumann, “An enhanced visualization process model for incremental visualization,” IEEE Trans. Vis. Comput. Graph., 2016, doi: 10.1109/TVCG.2015.2462356.

[24] I. Alexandre, “Promoting insight: A case study of how to incorporate interaction in existing data visualizations,” in Proceedings of the International Conference on Information Visualisation, 2016. doi: 10.1109/IV.2016.15.

[25] E. Pourabbas and A. Shoshani, “The composite data model: A unified approach for combining and querying multiple data models,” IEEE Trans. Knowl. Data Eng., 2015, doi: 10.1109/TKDE.2014.2365815.

Keywords :

Overview of Data Modelling, Data Modelling Enhances Visualization, How Data Modeling Prepares Data for Visualization and Challenges.