期刊名称:Bulletin of the Technical Committee on Data Engineering
出版年度:2016
卷号:39
期号:2
页码:6
出版社:IEEE Computer Society
摘要:The prevalence of large volumes and varieties of accessible data is profoundly changing the way business, gov-ernment and individuals approach decision making. Organizational big data investment strategies regarding whatdata to collect, clean, integrate, and analyze are typically driven by some notion of perceived value. However,the value of the data is inescapably tied to the underlying quality of the data. Although for big data, value andquality may be correlated, they are conceptually different. For example, a complete and accurate list of the booksread on April 1, 2016 by the special editors of this issue may not have much value to anyone else. Whereas evenpartially complete and somewhat noisy GPS data from public transport vehicles may have a high perceived valuefor transport engineers and urban planners. In spite of significant advances in storage and compute capabilities,the time to value in big data projects often remains unacceptable due to the quality of the underlying data. Poordata quality is being termed as the dark side of big data, inhibiting the effective use of data to discover trustedinsights and foresights.