Essays on Data Quality: Issues, Processes and Importance Literature review

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The paper "Data Quality: Issues, Processes and Importance " is a perfect example of a management literature review.   This essay is based on the concept of data quality and discusses several aspects which relate to this phenomenon. To start with, a number of issues and problems that affect the quality of data are discussed. Secondly, the different ways in which the issues related to data quality as identified in the discussion can be resolved are presented. Emphasis is on how solving the problems can help improve the overall quality of data. Thirdly, the relevant processes, tools and technologies that are used to improve the quality of data are presented.

Lastly, the various ways in which quality data is important to the public sector are discussed. Data Quality: Inherent Issues In general, the quality of data that is used by an organisation can be understood in terms of the extent to which the data is highly consistent, completely comprehensible and relevant to particular situations (Singh & Singh, 2010, p. 41). The extent to which data is of quality with regard to particular situations depends on the particular organisation that is using the data.

For example, educational institutions have different requirements for the data that is used as opposed to organisations operating in the other sectors. It is only when data adheres to these needs that it can be said to be of high quality. Further, an organisation that uses high-quality data stands to benefit a lot in terms of the quality of decisions made and their overall outcomes (Rhind, n.d. , p. 3). This implies that the issue of the quality of data is of great importance to organisations. There are several issues that are inherent in data quality.

The first one is the subject of accuracy. For data to be of high quality, it must represent the actual values that it stands for (Watson, Kraemer & Thorn, 2006, p. 3). This implies that since data is supposed to represent actual reality, it must not fail to do so. There are several ways in which data may fail to be accurate, thus losing its overall quality. For example, when data lack linkages that allow two or more separate systems to access and edit it, it usually fails to reflect the reality (Watson et al. , 2006, p.

4). Furthermore, issues of accuracy of data may arise as a result of large scale errors in the data itself. The result of this is that the overall quality of the data is compromised because of the lack of accuracy in individual values. The second issue that is related to the quality of data is its completeness. Generally, completeness of data can be understood in terms of whether or not the data represents all the information that is available and should be covered by it.

In order for data to be termed as complete, all the values must be available and should be in a state that is usable (Singh & Singh, 2010, p. 41). The importance of completeness in data cannot be overemphasised. According to Chapman (2005, p. 4) organisations can guarantee the quality of their data by prioritising the use of little but complete sets of data over large amounts of data that are not complete. Therefore, completeness is an important aspect of the quality of data.

References

Bujak, A., Carvalho, W., & Sriramulu, R. (2012). Lean management and operations in the global professional services industry. In U. Baumer, P. Kreutter & W. Messner (Eds.), Globalisation of professional services (pp. 95-104). Berlin: Springer-Verlag.

Chapman, A. D. (2005). Principles of data quality. Report for the Global Biodiversity Information Facility. Retrieved from http://www.niobioinformatics.in/books/Data%20Quality.pdf.

Cong, G., Fan, W., Geerts, F., Jia, X., & Ma, S. (2007). Improving data quality: Consistency and accuracy. Retrieved from http://win.ua.ac.be/~adrem/bibrem/pubs/CongFGJM07.pdf.

Health Information and Quality Authority. (2011, April). International review of data quality. Dublin: Health Information and Quality Authority,

Rahm, E., & Do, H. H. (2000). Data cleaning: Problems and current approaches. Retrieved from https://www.ki.informatik.hu-berlin.de/mac/lehre/lehrmaterial/Informationsintegration/Rahm00.pdf

Rhind, G. (n.d). Poor quality data: The pandemic problem that needs addressing. Independent White Paper Commissioned by Postcode Anywhere. Retrieved from http://www.grcdi.nl/PCAwhitepaper.pdf

Shakespeare, S. (2012). Shakespeare review: An independent review of public sector information. Retrieved from https://www.gov.uk/government/uploads/system/uploads/attachment_data/file/198752/13-744-shakespeare-review-of-public-sector-information.pdf

Singh, R., & Singh, K. (2010). A descriptive classification of causes of data quality problems in data warehousing. International Journal of Computer Science Issues, 7(3), 41-50. Retrieved from http://ijcsi.org/papers/7-3-2-41-50.pdf

SOA & LL Global. (2011). Experience data quality: How to clean and validate your data. Retrieved from https://www.soa.org/research/research-projects/life-insurance/research-2011-12-data-quality.aspx

Wang, R. Y., Ziad, M., & Lee, Y. W. (2001). Data quality. New York: Kluwer.

Watson, J. G., Kraemer, S. B., & Thorn, C. A. (2006). Data quality essentials: Guide to implementation – resources for applied practice. Centre for Educator Compensation Reform. Retrieved from http://cecr.ed.gov/pdfs/guide/dataQuality.pdf

Yiu, C. (2012). The big data opportunity: Making government faster, smarter and more personal. Retrieved from http://www.policyexchange.org.uk/images/publications/the%20big%20data%20opportunity.pdf

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