What is it about?

In simple terms, our paper is about evaluating the quality of large sets of data in different situations. Data quality is essential because if the data we use is not good, it can lead to wrong decisions, increased costs, and unhappy customers. The challenge with big data is that it is generated quickly and comes in many different forms, so traditional methods for assessing data quality don't work well. We did a thorough review of existing solutions and found that none of them were able to handle both the size and diversity of big data while considering the specific context in which it is used. We compared different approaches and developed recommendations for creating better methods to assess data quality in big data. We also highlighted the remaining challenges in this field. Overall, our paper helps researchers and industry professionals understand the importance of context and provides guidance for improving data quality assessment in the era of big data

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Why is it important?

Our work stands out for its focus on context-aware data quality assessment in the context of big data. While data quality has been a longstanding research issue, the advent of big data has posed new challenges that existing solutions have struggled to address. Our paper provides a comprehensive review of the existing approaches and highlights their limitations, particularly regarding context awareness and scalability. By identifying the gaps in current methods and proposing recommendations for context-aware data quality assessment, our work offers a fresh perspective and valuable insights to researchers and industry practitioners. Given the increasing prevalence of big data and its impact on decision-making and profitability across various sectors, our work is unique and timely. It addresses a pressing need for effective data quality assessment techniques that can handle big data's scale, heterogeneity, and velocity while considering its specific context of use. By clearly understanding the challenges and offering recommendations for improvement, our paper can potentially attract a wide readership interested in advancing the field of data quality assessment for big data. Researchers, data scientists, and industry professionals grappling with data quality issues in the era of big data will find our work relevant and beneficial, which can contribute to an increased readership.


As an author, I am truly excited about the findings and recommendations presented in this publication. It was a rewarding experience to delve into the complexities of data quality assessment in the context of big data and uncover the gaps in existing solutions. The research process allowed us to critically analyze various approaches and identify the need for context awareness in assessing data quality. The field of data quality assessment is rapidly evolving, especially with the proliferation of big data and its pervasive impact on decision-making processes. Our work fills a significant gap in the literature by highlighting the unique challenges posed by big data and emphasizing the importance of considering the specific context in which data is used. By providing a comprehensive review and a methodological framework for context-aware data quality assessment, we aim to guide future research and practical implementations in this domain. I believe that our publication has the potential to make a valuable contribution to the field and attract the attention of researchers, practitioners, and organizations grappling with data quality issues. By shedding light on the limitations of existing solutions and offering recommendations for improvement, we hope to inspire further advancements in assessing data quality in the era of big data. Ultimately, we aim to empower decision-makers with high-quality data, enabling them to make informed choices and drive positive outcomes in their respective domains.

Hadi Fadlullah
Saint Joseph University

Read the Original

This page is a summary of: Context-aware big data quality assessment: a scoping review, Journal of Data and Information Quality, June 2023, ACM (Association for Computing Machinery),
DOI: 10.1145/3603707.
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