The Issue of Poor Data Quality

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Table of Contents

Introduction
Discussion
Conclusion
References

Introduction

Many organizations suffer from the problem of poor data quality and the result of this is huge overheads and expenses, which can lead to losses in an organization. This problem can result when poor quality data is used to make strategic decisions or present the financial reports to the management in organizations (Becker, 2001). If these occur, it can have a negative impact on an organization bearing in mind the financial impact it can have on the organization.

Discussion

The quality of data in any organization normally produces a good outcome in any organization. This may be in terms of profits, accurate data, and other positive issues that may portray the organization in a positive light. The following problems are a result of poor quality data in an organization:

Delay in deploying a new data system.
Loss of credibility in a system
The following standards accurately define what data quality is:
Completeness is where the person with the responsibility of inputting the data should comprehend the scope of the data he or she is dealing with and guarantee that all the data elements in the database make sense (Fuente & Domeneche, 2006).
Accuracy: This is where the values that are filled in the databases must be entirely correct. For instance, names should be correctly spelled and figures inputted correctly (Brackett, 2000).
Uniqueness: In this case, an entity should correctly respond with some information in the database (Yair & Wang, 2007).
Consistency: In this case, all the summarized information should be in agreement with any underlying atomic-level minute detail (Herrera & Kupar, 2007).
Timelessness: All data in the databases should be up-to-date in relation to the needs of an organization and the professionals tasked with data entry should be attentive to any kind of deviation used by a modernized schedule

The poor quality of data can affect an organization in some ways. First, poor data quality can cause some irregularities in some business processes such as in the financial records of an organization. Additionally, any decisions that can be made with poor quality data can have a detrimental effect on an organization. In addition to this, poor data quality can create a sense of mistrust within an organization such as it can lower the confidence of a client. Furthermore, the organization can ruin its reputation because of this as well as lose money.

In most cases, the professionals that are in charge of data entry are aware of the sensitivity of the data but in most cases, carelessness leads to poor quality of data. The cost, problems, and challenge of any incidences of poor data quality in an organization is by tradition not quite visible to the management of most organizations because it is often corrected before they can see it (Batini & Scannapieco, 2006).

Poor data quality can have considerable economic and social impacts. Though many organizations are improving data quality with practical tools and approaches, their continued enhancement efforts normally emphasize the accuracy of the data. It is a common belief that all data consumers have a broader conceptualization of data quality than the data professionals do such as the IS experts realize (Becker, 2001). In addition to this, to avoid poor data quality, IS professionals should adopt a hierarchical structure for categorizing dimensions in data quality that are quite vital to data consumers (Becker, 2001).

Conclusion

The issue of poor data quality is one that has been witnessed by many organizations and the result is normally quite detrimental. For instance, in a medical setting, poor data quality can even mess up patient records and the magnitude of this is quite hazardous. The best way that organizations should do to minimize instances of poor data quality is to input any data with the accuracy it deserves as failure to do this might lead to a negative impact.

References

Batini, C. & Scannapieco, M. (2006). Data quality: concepts, methodologies and techniques. New York, NY: Springer

Becker, S. (2001). Developing quality complex database systems: practices, techniques and technologies. Southfield, MI: Idea Group Inc (IGI)

Brackett, M. (2000). Data resource quality: turning bad habits into good practices. White Plains, NY: Addison-Wesley Publishing Company

Fuente, A. & Domenech, R. (2006). Human capital in growth regressions: how much difference does data quality make? Journal of the European Economic.

Herrera, Y. & Kapur, D. (2007). Improving data quality: actors, incentives and capabilities. Political analysis.

Yair, W. & Wang, R. (2007). Poor data quality can have a severe impact on the overall effectiveness.

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The Issue of Poor Data Quality

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