Data Quality Issues in Big Data: A Review

Salih, Fathi Ibrahim; Ismail, Saiful Adli; Hamed, Mosaab M.; Yusop, Othman Mohd; Azmi, Azri; Azmi, Nurulhuda Firdaus Mohd

doi:10.1007/978-3-319-99007-1_11

Cited by 8 publications

(12 citation statements)

References 10 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Haryadi et al [14] asserted that data quality is focused on data that have not been analysed, while information quality is focused on the analysis that has been done on the data. This study, however, opines that data quality should focus on the wellness and appropriateness of data, which encompasses either before or after it has been analysed, in which it should meet the requirements of organisations [12].…”

Section: Literature Reviewmentioning

confidence: 99%

“…Although it is vital to exploit structured and unstructured data for BDA, they are usually incomplete, inaccurate, inconsistent, and vague or ambiguous, which could lead to false decisions [8][9][10][11]. Salih et al [12] and Wamba et al [13] have highlighted the lack of data quality mechanisms being applied in BDA prior to data usage. Several studies have considered the potential of data quality for BDA application [14][15][16][17][18], yet, specific questions about what drives the dimensions of data quality remain unanswered.…”

mentioning

confidence: 99%

See 1 more Smart Citation

Exploring big data traits and data quality dimensions for big data analytics application using partial least squares structural equation modelling

et al. 2021

View full text Add to dashboard Cite

The popularity of big data analytics (BDA) has boosted the interest of organisations into exploiting their large scale data. This technology can become a strategic stimulation for organisations to achieve competitive advantage and sustainable growth. Previous BDA research, however, has focused more on introducing more traits, known as Vs for big data traits, while ignoring the quality of data when examining the application of BDA. Therefore, this study aims to explore the effect of big data traits and data quality dimensions on BDA application. This study has formulated 10 hypotheses that comprised of the relationships of big data traits, accuracy, believability, completeness, timeliness, ease of operation, and BDA application constructs. This study conducted a survey using a questionnaire as a data collection instrument. Then, the partial least squares structural equation modelling technique was used to analyse the hypothesised relationships between the constructs. The findings revealed that big data traits can significantly affect all constructs for data quality dimensions and that the ease of operation construct has a significant effect on BDA application. This study contributes to the literature by bringing new insights to the field of BDA and may serve as a guideline for future researchers and practitioners when studying BDA application.

show abstract

Section: Literature Reviewmentioning

confidence: 99%

mentioning

confidence: 99%

Exploring big data traits and data quality dimensions for big data analytics application using partial least squares structural equation modelling

et al. 2021

View full text Add to dashboard Cite

show abstract

“…Usability dimensions and indicators for structured data [10]- [16], [29]- [31] are not applicable to unstructured data due to the differences in their structure and schema [32]. The usability of unstructured text data has been addressed where consistency, accuracy, integrity, timeliness, and entity recognition have been identified as usability indicators [33].…”

Section: Significance Of Workmentioning

confidence: 99%

“…The existing literature on data quality provides an array of quality dimensions [29], [84]. Among these quality dimensions, data usability is a highly contextual data quality dimension that deals with the user requirements.…”

Section: Current State Of Research and Literaturementioning

confidence: 99%

“…Data availability is "the extent to which data are current and available for use as specified and in the time frame in which they are expected" [87] or "the extent to which information is physically accessible" [88]. Relevance is the "Extent to which information is applicable and helpful for the task at hand" [87] or "the degree to which data can be of benefit to solve the issue" [29]. Data fitness for the purpose at hand is an indicator of data relevance, which specifies the benefits of data and how it can solve the prevalent issue [34], [10].…”

Section: ) Usability Issuesmentioning

confidence: 99%

See 1 more Smart Citation

Development of Usability Enhancement Model for Unstructured Big Data Using SLR

2021

View full text Add to dashboard Cite

Unstructured text contains valuable information for a range of enterprise applications and informed decision making. Text analytics is used to extract valuable insights from unstructured big data. Among the most significant challenges of text analytics, quality and usability are critical affecting the outcome of the analytical process. The enhancement in usability is important for the exploitation of unstructured data. Most of the existing literature focuses on the usability of structured data as compared to unstructured data whereas big data usability has been discussed merely in context of its assessment. The existing approaches do not provide proper guidelines on usability enhancement of unstructured data. In this study, a rigorous systematic literature review, using PRISMA framework, has been conducted to develop a model enhancing the usability of unstructured data and bridging the research gap. The recent approaches and solutions for text analytics have been investigated thoroughly. Furthermore, it identifies the usability issues of unstructured text data and their consequences on data preparation for analytics. Defining the usability dimensions for unstructured big data, identification of the usability determinants, and developing a relationship between usability dimension and determinants to derive usability rules are the significant contribution of this research, and are integrated to formulate the model. The proposed usability enhancement model is the major outcome of the study. It would contribute to making unstructured data usable and facilitating the data preparation activities with more valuable data that eventually improves the analytical process.

show abstract

A Model of Big Data Analytics Applied to Teacher Evaluation Based on Enhanced Data Quality

Fan,

Cheng

2024

Lecture Notes in Computer Science

View full text Add to dashboard Cite

Data Quality Issues in Big Data: A Review

Cited by 8 publications

References 10 publications

Exploring big data traits and data quality dimensions for big data analytics application using partial least squares structural equation modelling

Exploring big data traits and data quality dimensions for big data analytics application using partial least squares structural equation modelling

Development of Usability Enhancement Model for Unstructured Big Data Using SLR

A Model of Big Data Analytics Applied to Teacher Evaluation Based on Enhanced Data Quality

Contact Info

Product

Resources

About