BIG DATA QUALITY DIMENSIONS: A SYSTEMATIC LITERATURE REVIEW

Ramasamy, Anandhi; Chowdhury, Soumitra

doi:10.4301/S1807-1775202017003

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Original Article • JISTEM J.Inf.Syst. Technol. Manag. 17 • 2020 • https://doi.org/10.4301/S1807-1775202017003 copy

BIG DATA QUALITY DIMENSIONS: A SYSTEMATIC LITERATURE REVIEW

Authorship SCIMAGO INSTITUTIONS RANKINGS

ABSTRACT

Although big data has become an integral part of businesses and society, there is still concern about the quality aspects of big data. Past research has focused on identifying various dimensions of big data. However, the research is scattered and there is a need to synthesize the ever involving phenomenon of big data. This research aims at providing a systematic literature review of the quality dimension of big data. Based on a review of 17 articles from academic research, we have presented a set of key quality dimensions of big data.

Keywords:
data; big data; quality; dimensions; assessment

Research Work	Structure	Terms used in relation to dimensions
An Hybrid Approach to Quality Evaluation Across Big Data Value Chain (Serhani et al., 2016Serhani, M. A., El Kassabi, H. T., Taleb, I., & Nujum, A. (2016). A hybrid approach to quality evaluation across big data value chain. In2016 IEEE International Congress on Big Data (BigData Congress)(pp. 418-425). IEEE.https://ieeexplore.ieee.org/abstract/document/7584971 https://ieeexplore.ieee.org/abstract/doc... )	2 Categories, 4 Dimensions	Contextual: Accuracy Intrinsic: Timeliness, Completeness, Consistency
An Investigation of How Data Quality is Affected by Dataset Size in the Context of Big Data Analytics (Woodall et al., 2014Woodall, P. et al., (2014). An Investigation of How Data Quality is Affected by Dataset Size in the Context of Big Data Analytics. In 19 th International Conference on Information Quality (ICIQ), Xi’an, China. https://is.gd/Woodall_et_al_big_data https://is.gd/Woodall_et_al_big_data... )	1 Dimension	Completeness
Big Data Pre-Processing: Closing the Data Quality Enforcement Loop (Taleb & Serhani, 2017Taleb, I., & Serhani, M. A. (2017). Big Data Pre-Processing: Closing the Data Quality Enforcement Loop. In 2017 IEEE International Congress on Big Data (BigData Congress) (pp. 498-501). https://ieeexplore.ieee.org/abstract/document/8029366 https://ieeexplore.ieee.org/abstract/doc... )	3 Dimensions	Accuracy, Completeness, Consistency
Big Data Quality: A Quality Dimensions Evaluation (Taleb et al., 2016Taleb, I., El Kassabi, H. T., Serhani, M. A., Dssouli, R., & Bouhaddioui, C. (2016). Big data quality: A quality dimensions evaluation. In2016 Intl IEEE Conferences on Ubiquitous Intelligence & Computing, (pp. 759-765).)	3 Dimensions	Accuracy, Completeness, Consistency
Big Data Quality: A Survey (Taleb et al., 2018Taleb, I., Serhani, M. A., & Dssouli, R. (2018). Big data quality: A survey. In2018 IEEE International Congress on Big Data (BigData Congress)(pp. 166-173). https://ieeexplore.ieee.org/abstract/document/8457745 https://ieeexplore.ieee.org/abstract/doc... )	4 Categories 18 Dimensions	Intrinsic: Accuracy, Timeliness, Consistency, Completeness Contextual: Reputation, Relevancy, Accessibility, Quantity, Value-added, Believability Representational: Interpretability, Representational, Conciseness of representation, Consistency, Manipulability, Ease of understanding Accessibility: Access, Security
Big Data Validation Case Study (Xie et al., 2017Xie, C., Gao, J., & Tao, C. (2017). Big data validation case study. In 2017 IEEE third international conference on big data computing service and applications (BigDataService)(pp. 281-286). https://ieeexplore.ieee.org/abstract/document/7944952 https://ieeexplore.ieee.org/abstract/doc... )	4 Dimensions	Validity, Completeness, Consistency, Accuracy
Big Data, Big Data Quality Problem (Becker et al., 2015Becker, D., King, T. D., & McMullen, B. (2015). Big data, big data quality problem. In2015 IEEE International Conference on Big Data (Big Data)(pp. 2644-2653). https://ieeexplore.ieee.org/abstract/document/7364064 https://ieeexplore.ieee.org/abstract/doc... )	7 Dimensions	Accuracy, Precision, Completeness, Consistency, Timeliness, Lineage/Pedigree and Relevance
Context-aware data quality assessment for big data (Ardagna et al., 2018Ardagna, D., Cappiello, C., Samá, W., & Vitali, M. (2018). Context-aware data quality assessment for big data.Future Generation Computer Systems , 89, 548-562. https://re.public.polimi.it/retrieve/handle/11311/1057520/295709/FutureGeneration.pdf https://re.public.polimi.it/retrieve/han... )	7 Dimensions	Accuracy, Completeness, Consistency, Distinctness, Precision, Timeliness, Volume
Data quality assessment: The Hybrid Approach (Woodall et al., 2013Woodall, P., Borek, A. & Kumar Parlikad, A., (2013). Data quality assessment: The Hybrid Approach. Information & Management, 50(7), 396-382. https://www.sciencedirect.com/science/article/abs/pii/S0378720613000517 https://www.sciencedirect.com/science/ar... )	2 Dimensions	Completeness, Accuracy
Data quality for data science, predictive analytics, and big data in supply chain management: An introduction to the problem and suggestions for research and applications (Hazen et al., 2014Hazen, B. T., Boone, C. A., Ezell, J. D. & Jones-Farmer, L. A., (2014). Data quality for data science, predictive analytics, and big data in supply chain management: An introduction to the problem and suggestions for research and applications. International Journal of Production Economics, 154, 72-80. https://www.sciencedirect.com/science/article/abs/pii/S0925527314001339 https://www.sciencedirect.com/science/ar... )	2 Categories, 4 Dimensions	Contextual: Accuracy Intrinsic: Timeliness, Completeness, Consistency
Data quality in big data processing: Issues, solutions and open problems (Zhang et al., 2017Zhang, P., Xiong, F., Gao, J., & Wang, J. (2017). Data quality in big data processing: Issues, solutions and open problems. In2017 IEEE Smart World, Ubiquitous Intelligence & Computing. (pp. 1-7). https://ieeexplore.ieee.org/abstract/document/8397554 https://ieeexplore.ieee.org/abstract/doc... )	4 Dimensions	Availability, Usability, Reliability, Relevance
Data Quality Issues in Big Data (Rao et al., 2015Rao, D., Gudivada, V. N., & Raghavan, V. V. (2015). Data quality issues in big data. In2015 IEEE International Conference on Big Data (Big Data)(pp. 2654-2660). https://ieeexplore.ieee.org/abstract/document/7364065 https://ieeexplore.ieee.org/abstract/doc... )	5 Dimensions	Accuracy, Confidentiality, Completeness, Volume, Timeliness
Data quality management, data usage experience and acquisition intention of big data analytics (Kwon et al., 2014Kwon, O., Lee, N. & Shin, B. (2014). Data quality management, data usage experience and acquisition intention of big data analytics. International Journal of Information Management, 34, 387-394. https://www.sciencedirect.com/science/article/pii/S0268401214000127 https://www.sciencedirect.com/science/ar... )	2 Dimensions	Consistency, Completeness
From Data Quality to Big Data Quality (Batini et al., 2015Batini, C., Rula, A., Scannapieco, M., & Viscusi, G. (2015). From Data Quality to Big Data Quality. Journal of Database Management, 26(1), 60-82. https://www.igi-global.com/article/from-data-quality-to-big-data-quality/140546 https://www.igi-global.com/article/from-... )	7 Clusters 17 Dimensions	Accuracy: Correctness, Validity and Precision Completeness: Pertinence, Relevance Redundancy: Minimality, Compactness and Conciseness Readability: Comprehensibility, Clarity and Simplicity Accessibility: Availability Consistency: Cohesion and Coherence Trust: Believability, Reliability and Reputation
My (Fair) Big Data (Catarci et al., 2017Catarci, T., Scannapieco, M., Console, M., & Demetrescu, C. (2017). My (fair) big data. In 2017 IEEE International Conference on Big Data (Big Data) (pp. 2974-2979). https://ieeexplore.ieee.org/abstract/document/8258267 https://ieeexplore.ieee.org/abstract/doc... )	3 Dimensions	Consistency, Accuracy, Confidentiality
The Challenges of Data Quality and Data Quality Assessment in the Big Data Era (Cai & Zhu, 2015Cai, L. & Zhu, Y.. (2015). The Challenges of Data Quality and Data Quality Assessment in the Big Data Era. Data Science Journal, 14(2), 1-10. https://datascience.codata.org/articles/10.5334/dsj-2015-002/ https://datascience.codata.org/articles/... )	5 Dimensions	Availability, Usability, Reliability, Relevance, and Presentation quality
Big Data Quality Metrics for Sentiment Analysis Approaches (El Alaoui, Gahi & Messoussi 2019El Alaoui, I., Gahi, Y., & Messoussi, R. (2019). Big Data Quality Metrics for Sentiment Analysis Approaches. InProceedings of the 2019 International Conference on Big Data Engineering(pp. 36-43). https://dl.acm.org/citation.cfm?id=3341629 https://dl.acm.org/citation.cfm?id=33416... )	11 dimensions	Real-time analyzability, accuracy, completeness, uniqueness, transformation, conformity, normalization, referential integrity, consistency, credibility, freshness

Data Quality Dimension	Definition
Accessibility	Accessibility and availability are related to the ability of the user to access data from his or her culture, physical status/functions, and technologies available (Batini et al., 2015Batini, C., Rula, A., Scannapieco, M., & Viscusi, G. (2015). From Data Quality to Big Data Quality. Journal of Database Management, 26(1), 60-82. https://www.igi-global.com/article/from-data-quality-to-big-data-quality/140546 https://www.igi-global.com/article/from-... )
Cohesion	Consistency, cohesion and coherence refer to the capability of data to comply without contradictions to all properties of the reality of interest, as specified in terms of integrity constraints, data edits, business rules and other formalisms (Batini et al., 2015Batini, C., Rula, A., Scannapieco, M., & Viscusi, G. (2015). From Data Quality to Big Data Quality. Journal of Database Management, 26(1), 60-82. https://www.igi-global.com/article/from-data-quality-to-big-data-quality/140546 https://www.igi-global.com/article/from-... ).
Confidentiality	This quality dimension determines whether right data is in the right hands. Is the data secure? (Catarci et al., 2017Catarci, T., Scannapieco, M., Console, M., & Demetrescu, C. (2017). My (fair) big data. In 2017 IEEE International Conference on Big Data (Big Data) (pp. 2974-2979). https://ieeexplore.ieee.org/abstract/document/8258267 https://ieeexplore.ieee.org/abstract/doc... )
Credibility	Data come from specialized organizations of a country, field, or industry. Experts or specialists regularly audit and check the correctness of the data content. Data exist in the range of known or acceptable values (Cai & Zhu, 2015Cai, L. & Zhu, Y.. (2015). The Challenges of Data Quality and Data Quality Assessment in the Big Data Era. Data Science Journal, 14(2), 1-10. https://datascience.codata.org/articles/10.5334/dsj-2015-002/ https://datascience.codata.org/articles/... ).
Pedigree/Lineage	This dimension helps in knowing the source of the data so that any inconsistency is corrected in the source and not in any other instances
Readability	Also represented as clarity, simplicity ease of understanding, interpretability, comprehensibility, this dimension refers to ease of understanding of data by users (Batini et al., 2015Batini, C., Rula, A., Scannapieco, M., & Viscusi, G. (2015). From Data Quality to Big Data Quality. Journal of Database Management, 26(1), 60-82. https://www.igi-global.com/article/from-data-quality-to-big-data-quality/140546 https://www.igi-global.com/article/from-... ).
Real-time analyzability	Sometimes data have to be analyzed in real-time. The time spent for storage could impact the results’ quality (El Alaoui, Gahi & Messoussi (2019El Alaoui, I., Gahi, Y., & Messoussi, R. (2019). Big Data Quality Metrics for Sentiment Analysis Approaches. InProceedings of the 2019 International Conference on Big Data Engineering(pp. 36-43). https://dl.acm.org/citation.cfm?id=3341629 https://dl.acm.org/citation.cfm?id=33416... )
Redundancy	Redundancy, minimality, compactness and conciseness refer to the capability of representing the reality of interest with the minimal use of informative resources. (Batini et al., 2015Batini, C., Rula, A., Scannapieco, M., & Viscusi, G. (2015). From Data Quality to Big Data Quality. Journal of Database Management, 26(1), 60-82. https://www.igi-global.com/article/from-data-quality-to-big-data-quality/140546 https://www.igi-global.com/article/from-... ).
Trust	Trust, including believability, reliability and reputation, catching how much data derive from an authoritative source (Batini et al., 2015Batini, C., Rula, A., Scannapieco, M., & Viscusi, G. (2015). From Data Quality to Big Data Quality. Journal of Database Management, 26(1), 60-82. https://www.igi-global.com/article/from-data-quality-to-big-data-quality/140546 https://www.igi-global.com/article/from-... )
Volume	This quality dimension provides the percentage of values contained in the analyzed Data Object with respect to the source from which it is extracted (Ardagna et al., 2018Ardagna, D., Cappiello, C., Samá, W., & Vitali, M. (2018). Context-aware data quality assessment for big data.Future Generation Computer Systems , 89, 548-562. https://re.public.polimi.it/retrieve/handle/11311/1057520/295709/FutureGeneration.pdf https://re.public.polimi.it/retrieve/han... ).

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[1] Address for correspondence: Anandhi Ramasamy, Linnaeus University, Växjö, Sweden. E-mail: ar223fx@student.lnu.seSoumitra Chowdhury, Linnaeus University, Växjö, Sweden. E-mail: sontuchowdhury@yahoo.co.uk