Data management practices aligned with LGPD in the data science context

Authors

  • Elder Elisandro Schemberger

DOI:

https://doi.org/10.55905/oelv22n2-025

Keywords:

data science, innovation, LGPD, technology, interdisciplinary

Abstract

Data management is a comprehensive process that includes data collection, storage, organization, and maintenance for efficient data analysis and use. With data generation at unprecedented volumes and at breakneck speed, effective data management has become an operational necessity and a competitive advantage. In the context of Data Science, data is the raw material for insights and innovations, making its proper management fundamental to ensure reliability and accuracy. This article stresses the importance of data governance, security and privacy, especially in view of the increase in data protection regulations such as the LGPD. It also addresses efficiency in data management as essential to driving innovation and facilitating data-driven decision-making in organizations and academia. The ability to manage large volumes of data is unique because well-managed data provides a valuable source of information, enabling trend identification, process optimization and strategic decision making, leading to innovation and competitive advantages. Also, this article discusses LGPD compliance, not only as a legal issue, but also as an opportunity for companies to build trust with their users by demonstrating a commitment to data security and privacy. Efficient data management also involves choosing appropriate tools and technologies and implementing policies that ensure data compliance and security.

References

CUZZOCREA A. Big Data Lakes: Models, Frameworks, and Techniques. IEEE International Conference on Big Data and Smart Computing (BigComp), Jeju Island, Korea (South), 2021. DOI: 10.1109/BigComp51126.2021.00010.

Di TRIA, F.; LEFONS, E.; TANGORRA, F. A Proposal of Methodology for Designing Big Data Warehouses. Preprints, Basel, Switzerland, 2018. DOI: 10.20944/preprints201806.0219.v1.

FARIAS, F.; BARROS, R. LGPD – From Theory to Practice. 17th Iberian Conference on Information Systems and Technologies (CISTI). Madrid, Spain, 2022, DOI: 10.23919/CISTI54924.2022.9820267.

FERNANDO, P. R. Improving the quality of education system using Data Science Technologies: Survey. 5th International Conference on Innovative Technologies in Intelligent Systems and Industrial Applications (CITISIA), Sydney, Australia, 2020. DOI: 10.1109/CITISIA50690.2020.9371793.

FERREIRA, M.; OKANO, T.; AGUIAR, F.; SANTOS, H.; URSINI, E. A panorama of the implementation of the General Law for the Protection of Personal Data (LGPD) in Brazil: an exploratory survey. IEEE 12th Annual Computing and Communication Workshop and Conference (CCWC), Las Vegas, NV, USA, 2022. DOI: 10.1109/CCWC54503.2022.9720879.

LIU H. Application Research for real time computing. Global Conference on Robotics, Artificial Intelligence and Information Technology (GCRAIT), Chicago, IL, USA, 2022. DOI: 10.1109/GCRAIT55928.2022.00049.

MUNOZ, L., MAZON, J., TRUJILLO, J. ETL Process Modeling Conceptual for Data Warehouses: A Systematic Mapping Study. IEEE Latin America Transactions, vol. 9, no. 3, pp. 358-363, 2011. DOI: 10.1109/TLA.2011.5893784.

SHARMA, M. GUPTA, R. The Significance of using Data Extraction Methods for an Effective Big Data Mining Process. 2nd International Conference for Innovation in Technology (INOCON), Bangalore, India, 2023. DOI: 10.1109/INOCON57975.2023.10101236.

SUN, A. GAO, G. JI, T. TU, X. One Quantifiable Security Evaluation Model for Cloud Computing Platform. Sixth International Conference on Advanced Cloud and Big Data (CBD), Lanzhou, China, 2018. DOI: 10.1109/CBD.2018.00043.

PATEL, J. An Effective and Scalable Data Modeling for Enterprise Big Data Platform. IEEE International Conference on Big Data (Big Data), Los Angeles, CA, USA, 2019. DOI: 10.1109/BigData47090.2019.9005614.

SARAIVA, J.; SOARES, S. Privacy and Security Documents for Agile Software Engineering: An Experiment of LGPD Inventory Adoption. ACM/IEEE International Symposium on Empirical Software Engineering and Measurement (ESEM), New Orleans, LA, USA, 2023. DOI: 10.1109/ESEM56168.2023.10304806.

VILELA, F. A., CIFERRI, R. R. A novel solution to perform real-time ETL process based on non-intrusive and reactive concepts. International Conference on Computational Science and Computational Intelligence (CSCI), Las Vegas, NV, USA, 2021. DOI: 10.1109/CSCI54926.2021.00158.

WEST, N., GRIES, J., BROCKMEIER, C., GOBEL, J., DEUSE, J. Towards integrated Data Analysis Quality: Criteria for the application of Industrial Data Science. IEEE 22nd International Conference on Information Reuse and Integration for Data Science (IRI), Las Vegas, NV, USA, 2021. DOI: 10.1109/IRI51335.2021.00024.

XU, D., ZHANG Z., SHI, J. A Data Quality Assessment and Control Method in Multiple Products Manufacturing Process. 5th International Conference on Data Science and Information Technology (DSIT), Shanghai, China, 2022. DOI 10.1109/DSIT55514.2022.9943883.

Published

2024-02-07

How to Cite

Schemberger, E. E. (2024). Data management practices aligned with LGPD in the data science context. OBSERVATÓRIO DE LA ECONOMÍA LATINOAMERICANA, 22(2), e3108. https://doi.org/10.55905/oelv22n2-025

Issue

Section

Articles