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Journal articles on the topic 'Data and Data Quality'

Author: Grafiati

Published: 10 September 2021

Last updated: 29 July 2025

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1

Vaníček, J. "Software and data quality." Agricultural Economics (Zemědělská ekonomika) 52, No. 3 (2012): 138–46. http://dx.doi.org/10.17221/5007-agricecon.

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The paper presents new ideas in the International SQuaRE (Software Quality Requirements and Evaluation) standardisation research project, which concerns the development of a special branch of international standards for software quality. Data can be considered as an integral part of software. The current international standard and technical report of the ISO/IEC 9126, ISO/IEC 14598 series and ISO/IEC 12119 standard covert the whole software as an indivisible entity. However, such data sets as databases and data stores have a special character and need a different structure of quality character

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2

Reddy Desani, Nithin. "Enhancing Data Governance through AI - Driven Data Quality Management and Automated Data Contracts." International Journal of Science and Research (IJSR) 12, no. 8 (2023): 2519–25. http://dx.doi.org/10.21275/es23812104904.

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3

Stoykov, Evgeni. "DATA QUALITY FOR HYDROGRAPHIC MEASUREMENTS." Journal Scientific and Applied Research 21, no. 1 (2021): 26–30. http://dx.doi.org/10.46687/jsar.v21i1.314.

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The topic of preservation of cultural heritage is very important and is an integral part of National Security. It is up-to-date and timely. Its significance is determined by the scale and intensity of criminal attacks on cultural heritage, which have caused an increase in the need to update the system of measures to safeguard cultural values and overcome the underestimation of the protection of cultural heritage as a national security factor.

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4

D.B.Shanmugam, J.Dhilipan, A.Vignesh, and T.Prabhu. "Challenges in Data Quality and Complexity of Managing Data Quality Assessment in Big Data." International Journal of Recent Technology and Engineering (IJRTE) 9, no. 3 (2020): 589–93. https://doi.org/10.35940/ijrte.C5643.099320.

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High Quality Data are the precondition for examining and making use of enormous facts and for making sure the estimation of the facts. As of now, far reaching exam and research of price gauges and satisfactory appraisal strategies for massive records are inadequate. To begin with, this paper abridges audits of Data excellent studies. Second, this paper examines the records attributes of the enormous records condition, presents high-quality difficulties appeared by large data, and defines a progressive facts exceptional shape from the point of view of records clients. This system accommodates o

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5

Andrienko, Gennady, Natalia Andrienko, and Georg Fuchs. "Understanding Movement Data Quality." Journal of Location Based Services 10, no. 1 (2016): 31–46. https://doi.org/10.1080/17489725.2016.1169322.

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Understanding of data quality is essential for choosing suitable analysis methods and interpreting their results. Investigation of quality of movement data, due to their spatio-temporal nature, requires consideration from multiple perspectives at different scales. We review the key properties of movement data and, on their basis, create a typology of possible data quality problems and suggest approaches to identify these types of problems.

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Sandhya Kona, Sree. "Ensuring Data Integrity in Big Data Ingestion: Techniques and Best Practices for Data Quality Assurance." International Journal of Science and Research (IJSR) 9, no. 5 (2020): 1866–69. http://dx.doi.org/10.21275/sr24522140238.

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7

Arif, Bramantoro. "Data Cleaning Service for Data Warehouse: An Experimental Comparative Study on Local Data." TELKOMNIKA Telecommunication, Computing, Electronics and Control 16, no. 2 (2018): 834–42. https://doi.org/10.12928/telkomnika.v16.i2.7669.

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Data warehouse is a collective entity of data from various data sources. Data are prone to several complications and irregularities in data warehouse. Data cleaning service is non trivial activity to ensure data quality. Data cleaning service involves identification of errors, removing them and improve the quality of data. One of the common methods is duplicate elimination. This research focuses on the service of duplicate elimination on local data. It initially surveys data quality focusing on quality problems, cleaning methodology, involved stages and services within data warehouse environme

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8

Nazaire, Mare. "Integrating Data Quality Feedback: a Data Provider's Perspective." Biodiversity Information Science and Standards 2 (June 13, 2018): e26007. https://doi.org/10.3897/biss.2.26007.

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The Herbarium of Rancho Santa Ana Botanic Garden [RSA-POM] is the third largest herbarium in California and consists of >1.2 million specimens, of which ~50% are digitized. As a data provider, RSA-POM publishes its data with several aggregators, including the Consortium of California Herbaria, JSTOR, Symbiota (which is subsequently pulled into iDigBio and GBIF), as well as its own local webportal. Each submission of data needs to be prepared and formatted according to the aggregator's specifications for publication. Feedback on data quality (DQ) ranges from an individual user (often only a

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9

Rhazal, Oumaima El. "Improvement of Data Quality in Smart City: Toward Smart Data Aspect." Journal of Advanced Research in Dynamical and Control Systems 12, SP4 (2020): 1198–204. http://dx.doi.org/10.5373/jardcs/v12sp4/20201594.

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10

Ravi, Shankar Koppula. "Data Quality Assurance in Big Data and ETL Processes." European Journal of Advances in Engineering and Technology 7, no. 10 (2020): 101–5. https://doi.org/10.5281/zenodo.13789514.

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The rapid growth of data across various industries necessitates efficient and effective methods for Extract, Transform, and Load (ETL) processes. This paper delves into the critical aspect of data quality assurance within the context of big data and ETL workflows. By exploring the unique challenges posed by large-scale data, this study evaluates existing quality assurance techniques and their applicability to big data environments. It emphasizes the significance of maintaining high data quality to ensure reliable decision support and analytics. The paper also presents design patterns for incor

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11

Arjun, Mantri. "Enhancing Data Quality in Data Engineering using Data Testing Framework: Types and Tradeoffs." European Journal of Advances in Engineering and Technology 7, no. 10 (2020): 95–100. https://doi.org/10.5281/zenodo.13354036.

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Ensuring high data quality is critical in the era of big data, where reliable data is essential for accurate decision-making and business intelligence. This paper reviews various data testing frameworks designed to enhance data quality, including data validation, data cleansing, data profiling, data lineage, and automated testing frameworks. Each type of framework offers unique functionalities and presents distinct tradeoffs, such as customization versus complexity and real-time versus batch processing. By understanding these frameworks and their tradeoffs, data engineers can make informed dec

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12

Batini, Carlo, Anisa Rula, Monica Scannapieco, and Gianluigi Viscusi. "From Data Quality to Big Data Quality." Journal of Database Management 26, no. 1 (2015): 60–82. http://dx.doi.org/10.4018/jdm.2015010103.

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This article investigates the evolution of data quality issues from traditional structured data managed in relational databases to Big Data. In particular, the paper examines the nature of the relationship between Data Quality and several research coordinates that are relevant in Big Data, such as the variety of data types, data sources and application domains, focusing on maps, semi-structured texts, linked open data, sensor & sensor networks and official statistics. Consequently a set of structural characteristics is identified and a systematization of the a posteriori correlation betwee

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13

Gill, Rupali, and Jaiteg Singh. "A Review of Contemporary Data Quality Issues in Data Warehouse ETL Environment." Journal on Today's Ideas - Tomorrow's Technologies 2, no. 2 (2014): 153–60. http://dx.doi.org/10.15415/jotitt.2014.22012.

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14

Pranay, Mungara. "Ensuring Data Quality in Business Intelligence." European Journal of Advances in Engineering and Technology 8, no. 1 (2021): 89–96. https://doi.org/10.5281/zenodo.11908320.

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Many businesses have moved their operations to the cloud in an effort to fortify their data protection and enhance the standard of their business dealings. In modern operations, data quality is paramount. Data used to generate and gather information represents events and occurrences as they happen in real time. Customer happiness, the organization's decision-making strategy, and the organization's plan of execution are all negatively affected by low-quality data. The data quality has a major influence on how well machine learning and deep learning accomplish their duties in terms of accuracy,

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15

Chapman, Arthur. "Data Quality – Whose Responsibility is it?" Biodiversity Information Science and Standards 2 (June 13, 2018): e26084. https://doi.org/10.3897/biss.2.26084.

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The quality of biodiversity data is an on-going issue. Early efforts to improve quality go back at least 4 decades, but it has never risen to the level of importance that it should have. For far too long the push to database more and more data regardless of its quality has taken priority. So I pose the question - what is the use of having lots of data if 1) we don't know what its quality is, and 2) if much of it is not fit for use? When data-basing of herbarium and museum collections began in the 1970s many taxonomists saw the only use of the data as being for taxonomic purposes. But as more a

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16

De Smet, Bea, and Mark Stalmans. "LCI data and data quality." International Journal of Life Cycle Assessment 1, no. 2 (1996): 96–104. http://dx.doi.org/10.1007/bf02978653.

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17

K., Dharani* &. Dr. G. Abel Thangaraja**. "BIG DATA PREPROCESSING USING ENHANCED DATA QUALITY RULES DISCOVERY MODEL (EDQRM)." International Journal of Engineering Research and Modern Education (IJERME) 8, no. 2 (2023): 33–41. https://doi.org/10.5281/zenodo.8428545.

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In the Big Data Era, data is the center for any governmental, institutional, and private organization. Endeavors were equipped towards extricating profoundly important bits of knowledge that can't occur assuming data is of low quality. Hence, data quality (DQ) is considered as a vital component in big data processing. In this stage, bad quality data isn't entered to the Big Data value chain. This paper, proposed the Enhanced data quality Rules discovery model (EDQRM) for assessment of quality and Big Data pre-processing. EDQRM discovery model to improve and precisely focus on the pre-p

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18

Chicco, Joanne. "Data Quality." Health Information Management 27, no. 1 (1997): 12–21. http://dx.doi.org/10.1177/183335839702700106.

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19

Sadiq, Shazia, Tamraparni Dasu, Xin Luna Dong, et al. "Data Quality." ACM SIGMOD Record 46, no. 4 (2018): 35–43. http://dx.doi.org/10.1145/3186549.3186559.

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20

Glaze, William H. "Data quality." Environmental Science & Technology 36, no. 11 (2002): 225A. http://dx.doi.org/10.1021/es0223170.

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21

Fan, Wenfei. "Data Quality." ACM SIGMOD Record 44, no. 3 (2015): 7–18. http://dx.doi.org/10.1145/2854006.2854008.

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22

Huh, YU, FR Keller, TC Redman, and AR Watkins. "Data quality." Information and Software Technology 32, no. 8 (1990): 559–65. http://dx.doi.org/10.1016/0950-5849(90)90146-i.

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23

Kennedy, Dale J., Douglas C. Montgomery, Dwayne A. Rollier, and J. Bert Keats. "Data Quality." International Journal of Life Cycle Assessment 2, no. 4 (1997): 229–39. http://dx.doi.org/10.1007/bf02978420.

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24

Kennedy, Dale J., Douglas C. Montgomery, and Beth H. Quay. "Data quality." International Journal of Life Cycle Assessment 1, no. 4 (1996): 199–207. http://dx.doi.org/10.1007/bf02978693.

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25

Pantos, C., M, Schroeder, and P. Ivanova. "Open Access to Quality Biomedical Experimental and Clinical Data and Data-based Models." Biomedical Data Journal 01, no. 1 (2015): 1–2. http://dx.doi.org/10.11610/bmdj.01100.

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26

John Thayil, Jerry. "Enhancing Patient Care through Improved Provider Data Quality: An AI - Driven Data Solution." International Journal of Science and Research (IJSR) 13, no. 10 (2024): 1425–28. http://dx.doi.org/10.21275/sr241019045748.

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27

Ozmen-Ertekin, Dilruba, and Kaan Ozbay. "Dynamic data maintenance for quality data, quality research." International Journal of Information Management 32, no. 3 (2012): 282–93. http://dx.doi.org/10.1016/j.ijinfomgt.2011.11.003.

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28

Lee, Kyoung Ho, and Jang Ryong Lee. "Factors Affecting LOSA Data Quality." Journal of the Korean Society for Aviation and Aeronautics 31, no. 2 (2023): 72–80. http://dx.doi.org/10.12985/ksaa.2023.31.2.072.

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29

Researcher. "THE ROLE OF DATA QUALITY IN MODERN ANALYTICS." International Journal of Computer Engineering and Technology (IJCET) 15, no. 5 (2024): 174–86. https://doi.org/10.5281/zenodo.13744345.

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This comprehensive article explores the critical role of data quality in modern business analytics and decision-making processes. It examines the multifaceted nature of data quality, including its key dimensions of accuracy, completeness, consistency, timeliness, and reliability. The article delves into the significant impacts of both poor and high-quality data on business operations, customer satisfaction, and regulatory compliance. It also addresses the challenges organizations face in maintaining data quality in an increasingly complex digital landscape and proposes effective strategies for

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30

Golz, Claudia, Thomas Einfalt, and Gianmario Galli. "Radar data quality control methods in VOLTAIRE." Meteorologische Zeitschrift 15, no. 5 (2006): 497–504. http://dx.doi.org/10.1127/0941-2948/2006/0151.

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31

Mathes, Armin, Petra Friederichs, and Andreas Hense. "Towards a quality control of precipitation data." Meteorologische Zeitschrift 17, no. 6 (2008): 733–49. http://dx.doi.org/10.1127/0941-2948/2008/0347.

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32

Eken, Süleyman, Ahmet Sayar, and Kürşat Topçuoğlu. "AutoTest: Automation to Test Tabular Data Quality." International Journal of Computer and Electrical Engineering 6, no. 4 (2014): 365–68. http://dx.doi.org/10.7763/ijcee.2014.v6.854.

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33

C., S. Sindhu, and P. Hegde Nagaratna. "A Novel Integrated Framework to Ensure Better Data Quality in Big Data Analytics over Cloud Environment." International Journal of Electrical and Computer Engineering (IJECE) 7, no. 5 (2017): 2798–805. https://doi.org/10.11591/ijece.v7i5.pp2798-2805.

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With advent of Big Data Analytics, the healthcare system is increasingly adopting the analytical services that is ultimately found to generate massive load of highly unstructured data. We reviewed the existing system to find that there are lesser number of solutions towards addressing the problems of data variety, data uncertainty, and data speed. It is important that an errorfree data should arrive in analytics. Existing system offers single-hand solution towards single platform. Therefore, we introduced an integrated framework that has the capability to address all these three problems in on

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34

Mathes, Charles A. "Big Data Has Unique Needs for Information Governance and Data Quality." Journal of Management Science and Business Intelligence 1, no. 1 (2016): 12–20. https://doi.org/10.5281/zenodo.376753.

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Enterprises that are venturing into the technical environment of big data and are attempting to create a data lake environment need to take precautions. The principles of information governance and data quality need to be applied to the new world of big data to avoid the trap of the data lake turning into a data swamp. Applying the seven V’s of big data as foundational principles for information governance and data quality will help ensure the long-term success of the expertise big data environment.

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35

Srikanth I Chandan, O. "Advancing Clinical Data Capture: Embracing Electronic Data Capture (EDC) for Enhanced Efficiency and Quality." International Journal of Science and Research (IJSR) 12, no. 7 (2023): 1261–64. http://dx.doi.org/10.21275/sr23717190711.

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36

Yadavalli, Tulasiram. "Mastering Data Quality Management in Google Cloud: Strategies for Clean and Reliable Data Pipelines." International Journal of Science and Research (IJSR) 12, no. 9 (2023): 2232–36. https://doi.org/10.21275/sr230915093726.

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37

Gong, Xiaowen, and Ness B. Shroff. "Truthful Data Quality Elicitation for Quality-Aware Data Crowdsourcing." IEEE Transactions on Control of Network Systems 7, no. 1 (2020): 326–37. http://dx.doi.org/10.1109/tcns.2019.2905090.

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38

Taggart, Jane, Siaw-Teng Liaw, and Hairong Yu. "Structured data quality reports to improve EHR data quality." International Journal of Medical Informatics 84, no. 12 (2015): 1094–98. http://dx.doi.org/10.1016/j.ijmedinf.2015.09.008.

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39

Bernasconi, Anna. "Data quality-aware genomic data integration." Computer Methods and Programs in Biomedicine Update 1 (2021): 100009. http://dx.doi.org/10.1016/j.cmpbup.2021.100009.

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40

Wilantika, Nori, and Wahyu Catur Wibowo. "Data Quality Management in Educational Data." Jurnal Sistem Informasi 15, no. 2 (2019): 52–67. http://dx.doi.org/10.21609/jsi.v15i2.848.

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Every varsity in Indonesia is responsible for ensuring the completeness, the validity, the accuracy, and the currency of its educational data. The educational data is used for implementing higher-education quality assurance system and formulating policies related to universities and majors in Indonesia. Data quality assessment result indicates that educational data in Statistics Polytechnic did not meet completeness, validity, accuracy, and currency criteria. Data quality management maturity has been measured using Loshin’s Data Quality Maturity Model which result is in level 1 to level 2 of m

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41

M. Marvin Newhouse for the MPS Coor. "Data entry design and data quality." Controlled Clinical Trials 6, no. 3 (1985): 229. http://dx.doi.org/10.1016/0197-2456(85)90036-4.

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42

Li, Xingsen, Yong Shi, Jun Li, and Peng Zhang. "Data Mining Consulting Improve Data Quality." Data Science Journal 6 (2007): S658—S666. http://dx.doi.org/10.2481/dsj.6.s658.

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43

T., Aditya Sai Srinivas, Sravanthi Y., Vinod Kumar Y., and Dwaraka Srihith I.V. "Data Standardization: Key to Effective Data Integration." Advanced Innovations in Computer Programming Languages 6, no. 1 (2023): 1–4. https://doi.org/10.5281/zenodo.10060920.

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<i>Data standardization is a critical step in data preprocessing and analysis. This process involves transforming data to have a consistent scale, enabling meaningful comparisons and effective modeling. In this digital age, where data fuels decision-making across industries, understanding and implementing data standardization techniques is essential. This abstract introduces the concept of data standardization, emphasizing its importance in enhancing data quality, supporting data integration efforts, and facilitating data-driven decision-making. We explore various methods and tools for standar

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44

Ranwashe, Fhatani. "Georeferencing and data quality: SANBI's story." Biodiversity Information Science and Standards 2 (May 18, 2018): e25310. https://doi.org/10.3897/biss.2.25310.

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Georeferencing helps to fill in biodiversity information gaps, allowing biodiversity data to be represented spatially to allow for valuable assessments to be conducted. The South African National Biodiversity Institute has embarked on a number of projects that have required the georeferencing of biodiversity data to assist in assessments for redlisting of species and measuring the protection levels of species. Data quality in biodiversity information is an important aspect. Due to a lack of standardisation in collection and recording methods historical biodiversity data collections provide a c

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45

Researcher. "PROMOTING QUALITY DATA AND CLEANSING TECHNIQUES IN DATA ANALYTICS BASED ON SMART BUSINESS INTELLIGENCE TECHNOLOGY." International Journal of Engineering and Technology Research (IJETR) 9, no. 2 (2024): 519–31. https://doi.org/10.5281/zenodo.13970486.

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In advancement of recent era, business organization is developing drastically as there is an increase number of Information Technology (IT) as they gives wide impact on the national and international development as they all managing the vast and complex number of data. In order to analyses the performance of data, those vast data has to be processed and analysed. In managing the data, Extract, Transform and Load (DATA) process is applied and stored in the data warehouse as repository in order to take effective distributed based decision as it faces the problem of time consuming process. As the

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46

Shamihah, Muhammad Ghazali, Shaadan Norshahida, and Idrus Zainura. "Missing data exploration in air quality data set using R-package data visualisation tools." Bulletin of Electrical Engineering and Informatics 9, no. 2 (2020): 755–63. https://doi.org/10.11591/eei.v9i2.2088.

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Missing values often occur in many data sets of various research areas. This has been recognized as data quality problem because missing values could affect the performance of analysis results. To overcome the problem, the incomplete data set needs to be treated using imputation method. Thus, exploring missing values pattern must be conducted beforehand to determine a suitable method. This paper discusses on the application of data visualisation as a smart technique for missing data exploration aiming to increase understanding on missing data behaviour which include missing data mechanism (MCA

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47

Jesmeen, M. Z. H., Hossen J., Sayeed S., et al. "A Survey on Cleaning Dirty Data Using Machine Learning Paradigm for Big Data Analytics." Indonesian Journal of Electrical Engineering and Computer Science 10, no. 3 (2018): 1234–43. https://doi.org/10.11591/ijeecs.v10.i3.pp1234-1243.

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Recently Big Data has become one of the important new factors in the business field. This needs to have strategies to manage large volumes of structured, unstructured and semi-structured data. It’s challenging to analyze such large scale of data to extract data meaning and handling uncertain outcomes. Almost all big data sets are dirty, i.e. the set may contain inaccuracies, missing data, miscoding and other issues that influence the strength of big data analytics. One of the biggest challenges in big data analytics is to discover and repair dirty data; failure to do this can lead to ina

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48

Hejazi, Aylin, Neda Abdolvand, and Saeedeh Rajaee Harandi. "Assessing the Importance of Data Factors of Data Quality Model in the Business Intelligence Area." International Journal of Trade, Economics and Finance 8, no. 2 (2017): 102–8. http://dx.doi.org/10.18178/ijtef.2017.8.2.547.

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49

G., Ghadge Nagnath, Panchal Vishwanath D., and Shaikh Riyaj. "An Examination of Factors Influenced in the Quality Checking of Data in a Data Warehoused." International Journal of Trend in Scientific Research and Development Volume-2, Issue-1 (2017): 1509–17. http://dx.doi.org/10.31142/ijtsrd8213.

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50

Fletcher, Sam, and Md Zahidul Islam. "Measuring Information Quality for Privacy Preserving Data Mining." International Journal of Computer Theory and Engineering 7, no. 1 (2014): 21–28. http://dx.doi.org/10.7763/ijcte.2015.v7.924.

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