TY - GEN
T1 - Data quality applied to an academic business intelligence solution
T2 - 2017 IEEE Colombian Conference on Communications and Computing, COLCOM 2017
AU - Fernandez, Marshall
AU - Davila, Abraham
AU - Angeleri, Paula
N1 - Publisher Copyright:
© 2017 IEEE.
PY - 2017/10/26
Y1 - 2017/10/26
N2 - (BACKGROUND) Business Intelligence covers a set of technologies allowing the extraction, transformation and loading of data into a data warehouse, and presents the information in a way that allows managers of an organization to make decisions. However, stored data in a warehouse does not always have the expected quality required and this can lead managers to make wrong decisions. (OBJECTIVES) The objective of this work is the development of a quality model applicable to an academic business intelligence solution, based on the ISO/IEC 25000 series of standards. (METHOD) In this research, an analysis of the data quality requirements was done, as an input for the respective evaluation. (RESULTS) The results obtained were presented in a radar chart, along with a textual analysis. (CONCLUSIONS) It could be concluded that data quality models and standards could be used successfully for evaluating the quality of an academic BI system. And this information is important for identifying and mitigating risks inherent to data and risks that depend on an information system.
AB - (BACKGROUND) Business Intelligence covers a set of technologies allowing the extraction, transformation and loading of data into a data warehouse, and presents the information in a way that allows managers of an organization to make decisions. However, stored data in a warehouse does not always have the expected quality required and this can lead managers to make wrong decisions. (OBJECTIVES) The objective of this work is the development of a quality model applicable to an academic business intelligence solution, based on the ISO/IEC 25000 series of standards. (METHOD) In this research, an analysis of the data quality requirements was done, as an input for the respective evaluation. (RESULTS) The results obtained were presented in a radar chart, along with a textual analysis. (CONCLUSIONS) It could be concluded that data quality models and standards could be used successfully for evaluating the quality of an academic BI system. And this information is important for identifying and mitigating risks inherent to data and risks that depend on an information system.
KW - Business intelligence
KW - Data warehouse
KW - ISO/IEC 25012
KW - ISO/IEC 25024
KW - ISO/IES 25000
KW - SQuaRE
KW - data quality metrics
KW - data quality model
UR - http://www.scopus.com/inward/record.url?scp=85040233692&partnerID=8YFLogxK
U2 - 10.1109/ColComCon.2017.8088200
DO - 10.1109/ColComCon.2017.8088200
M3 - Conference contribution
AN - SCOPUS:85040233692
T3 - 2017 IEEE Colombian Conference on Communications and Computing, COLCOM 2017 - Proceedings
BT - 2017 IEEE Colombian Conference on Communications and Computing, COLCOM 2017 - Proceedings
A2 - Rodriguez, Yuli Andrea P
PB - Institute of Electrical and Electronics Engineers Inc.
Y2 - 16 August 2017 through 18 August 2017
ER -