Data quality is defined as “an inexact science in terms of assessments and benchmarks” [93]. Similarly high quality data can be described as “data that is fit for use by data consumers” [94].
11.2. Origin of Bad Data
There may be different sources from where erroneous data is originated. Data may become dirty if it is mistakenly entered, received from invalid external data source, or when good data is combined with outdated data and there is no way to distinguish between the two.
11.3. Categories and Dimensions of Data Quality
Since before data was the most valuable asset of an organization and data was rarely shared. Now businesses, governments, and research organizations rely on the exchange and sharing of various forms of data. As there is an increase in interconnectivity among data producers and data consumers; interest in data quality increases steadily. The management of data quality is typically a complex job. For the entire data management process all data quality aspects should be observed. Following table indicates the categories and dimensions of data quality [94]:
Table 11.1 Categories and Dimensions of Data Quality [94]
Categories Dimensions
Intrinsic Accuracy
Objectivity
Believability
Reputation
Contextual Completeness
Timeliness
Relevancy
Value Added
Among of data
Representational Interpretability
Ease of Understanding
Concise/Consistent representation
Accessibility Accessibility
Access security
11.4. Classification of data quality problems in data sources
Data quality problems are classified in two main categories: Single-Source problems and Multi-Source Problems [95]. A brief view of the classification and sub-classification is shown in the figure below that shows som...
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AHIMA's data quality management model depicts data collection as one of the four primary data functions. The others are application, warehousing, and analysis. All characteristics of data quality management should be applied to data collection ...
The four key processes in the data quality management model are analysis, warehousing, collection and application of data (AHIMA 2)
Laudon, K., & Laudon, J. (2007). Essentials of Business Information Systems (7th ed.) (Bob Horan, Ed.). Upper Saddle River, New Jersey: Pearson Prentice Hall.
As the Big Data era advances, the significance of data is changing. In addition to supporting business decisions and transactions, data is often now the good being traded, as companies begin to grasp the seemingly boundless potential value inherent in the data itself. Decreasing storage costs combined with the ability to collect data passively (through technological progress) mean that many companies are finding it easier to justify preserving the data rather than discarding it once its primary function h...
[7] Elmasri & Navathe. Fundamentals of database systems, 4th edition. Addison-Wesley, Redwood City, CA. 2004.
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HAND, D. J., MANNILA, H., & SMYTH, P. (2001).Principles of data mining. Cambridge, Mass, MIT Press.
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Wang , R. Y. (1998) A Product Perspective on Total Data Quality Management. Communications of the ACM, 41(2), 58-65.
Is there any technical guarantees of accuracy, reliability, ease of access, and the data security?
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