Data quality in big data: A review
The Data Warehousing Institute (TDWI) estimates that data quality problems cost U.S. businesses more than $600 billion a year. The problem with data is that its quality quickly degenerates over time. Experts say 2 percent of records in a customer file become obsolete in one month because customers d...
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my.utm.582062021-08-17T02:08:13Z http://eprints.utm.my/id/eprint/58206/ Data quality in big data: A review Abdullah, Noraini Ismail, Saiful Azmi Yuhaniz, Siti Sophiayati Mohd. Sam, Suriani T Technology (General) The Data Warehousing Institute (TDWI) estimates that data quality problems cost U.S. businesses more than $600 billion a year. The problem with data is that its quality quickly degenerates over time. Experts say 2 percent of records in a customer file become obsolete in one month because customers die, divorce, marry, and move. In addition, data entry errors, system migrations, and changes in source systems, among other things, generate bucket loads of errors. More complex, as organizations fragment into different divisions and units, interpretations of data elements change to meet the local business needs. However, there are several ways that the Company should concern, such as to treat data as a strategic corporate resource; develop a program for managing data quality with a commitment from the top; and hire, train, or outsource experienced data quality professionals to oversee and carry out the program. The Organizations can sustain a commitment to managing data quality over time and adjust monitoring and cleansing processes to changes in the business and underlying systems by using the Commercial data quality tools. Data is a vital resource. Companies that invest proportionally to manage this resource will stand a stronger chance of succeeding in today's competitive global economy than those that squander this critical resource by neglecting to ensure adequate levels of quality. This paper reviews the characteristics of big data quality and the managing processes that are involved in it. International Center for Scientific Research and Studies 2015 Article PeerReviewed Abdullah, Noraini and Ismail, Saiful Azmi and Yuhaniz, Siti Sophiayati and Mohd. Sam, Suriani (2015) Data quality in big data: A review. Interntional Journal Of Advances In Soft Computing And Its Applications, 7 (Specia). pp. 16-27. ISSN 2074-8523 https://link.springer.com/chapter/10.1007/978-3-319-99007-1_11 |
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The Data Warehousing Institute (TDWI) estimates that data quality problems cost U.S. businesses more than $600 billion a year. The problem with data is that its quality quickly degenerates over time. Experts say 2 percent of records in a customer file become obsolete in one month because customers die, divorce, marry, and move. In addition, data entry errors, system migrations, and changes in source systems, among other things, generate bucket loads of errors. More complex, as organizations fragment into different divisions and units, interpretations of data elements change to meet the local business needs. However, there are several ways that the Company should concern, such as to treat data as a strategic corporate resource; develop a program for managing data quality with a commitment from the top; and hire, train, or outsource experienced data quality professionals to oversee and carry out the program. The Organizations can sustain a commitment to managing data quality over time and adjust monitoring and cleansing processes to changes in the business and underlying systems by using the Commercial data quality tools. Data is a vital resource. Companies that invest proportionally to manage this resource will stand a stronger chance of succeeding in today's competitive global economy than those that squander this critical resource by neglecting to ensure adequate levels of quality. This paper reviews the characteristics of big data quality and the managing processes that are involved in it. |
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Abdullah, Noraini Ismail, Saiful Azmi Yuhaniz, Siti Sophiayati Mohd. Sam, Suriani |
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Abdullah, Noraini Ismail, Saiful Azmi Yuhaniz, Siti Sophiayati Mohd. Sam, Suriani |
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Abdullah, Noraini |
title |
Data quality in big data: A review |
title_short |
Data quality in big data: A review |
title_full |
Data quality in big data: A review |
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Data quality in big data: A review |
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Data quality in big data: A review |
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data quality in big data: a review |
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International Center for Scientific Research and Studies |
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2015 |
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http://eprints.utm.my/id/eprint/58206/ https://link.springer.com/chapter/10.1007/978-3-319-99007-1_11 |
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