Data Quality, Security, and Governance in Information Systems
This paper examines the role of data quality in organizational information systems, defining key standards—accuracy, completeness, consistency, uniqueness, and timeliness—and illustrating how failures in these areas create operational and security risks. Using a personal anecdote about a dental office's paper-based records and the high-profile Equifax data breach as case studies, the paper demonstrates how poor data management exposes individuals and organizations to identity theft, litigation, and reputational harm. It concludes by emphasizing data governance as an essential framework for maintaining data integrity, security, and usability across the full data lifecycle.
- Introduction: Data as a Core Business Asset: Data drives business operations; poor quality causes disruption
- Standards That Define Data Quality: Five standards: accuracy, completeness, consistency, uniqueness, timeliness
- Real-World Consequences of Poor Data Management: Dental office example illustrates everyday data security failures
- The Equifax Data Breach: A Case Study in Data Failure: Equifax breach exposed 145 million Americans to identity theft
- Improving Data Quality and Security Practices: CRM software, digitization, and compliance policies as remedies
- Data Governance as a Framework for Protection: Governance frameworks ensure usability, integrity, and security
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What makes this paper effective
- Grounds abstract data quality concepts in two concrete, relatable examples: a dental office with paper records and the large-scale Equifax breach.
- Moves logically from defining standards, to illustrating failures, to proposing remedies, giving the argument a clear cause-and-effect structure.
- Cites a specific legal outcome (the San Francisco Superior Court lawsuit) to show the real institutional consequences of poor data governance.
Key academic technique demonstrated
The paper uses a layered case-study approach: a small, personal anecdote (the dental office) establishes everyday stakes, while the Equifax breach escalates the argument to national scale. This progression from micro to macro helps readers understand that data quality failures are not isolated technical problems but systemic organizational and societal risks.
Structure breakdown
The paper opens by defining data quality standards drawn from Kimball et al. (2011), then illustrates failures through personal experience before escalating to the Equifax breach. It proposes practical remedies—digitization, CRM software, compliance policies—and closes by introducing data governance (Bhansali, 2014) as the overarching framework that ties all the remedies together. Six logical sections move from problem definition through evidence to solution.
Introduction: Data as a Core Business Asset
Data in information systems allows businesses to process various actions seamlessly. If one imagines a business as a factory, the main product of many businesses is data. From hospitals to banks, data is created, stored, transferred, and viewed every single workday. If this data is of poor quality, it may slow down a business's processes. For example, consider a hospital whose medical records remain on paper and have not been transferred to digital form. Such a situation can trigger easier data breaches and longer wait times for information to be retrieved, copied, and transferred.
Standards That Define Data Quality
What makes data quality poor? There are collectively defined standards that determine data quality. They are accuracy, completeness, consistency, uniqueness, and timeliness (Kimball, Ross, Thornthwaite, Mundy, & Becker, 2011). When poor-quality data is inaccurate—for example, the last name of a customer at a bank—this leads to service delays and mistakes. Regarding completeness and consistency, poor-quality data will not be clear or in agreement with underlying atomic-level detail (Kimball, Ross, Thornthwaite, Mundy, & Becker, 2011). If such data exists within a business, it can lead to further mistakes down the line, especially regarding assessments or information retrieval.
Effectiveness in information retrieval is important; that is why poor data quality often means little to no uniqueness. Lastly, if data is not current relative to a business's needs—that is, lacking timeliness—the business suffers as a result.
References
Bhansali, N. (2014). Data governance: Creating value from information assets. Boca Raton, FL: CRC Press.
Buhr, S. (2017, September 27). San Francisco sues Equifax on behalf of 15 million Californians affected by the breach. Retrieved from https://techcrunch.com/2017/09/27/san-francisco-sues-equifax-on-behalf-of-15-million-californians-affected-by-the-breach/
Johnson, C. (2018, February 14). Turns out, the Equifax data breach was even worse than we thought. Retrieved from http://www.daytondailynews.com/business/personal-finance/turns-out-the-equifax-data-breach-was-even-worse-than-thought/rT2KfaU5SLna2c2I3xprPP/
Kimball, R., Ross, M., Thornthwaite, W., Mundy, J., & Becker, B. (2011). The data warehouse lifecycle toolkit. Hoboken, NJ: John Wiley & Sons.
Lopez, D., & Durai, M. A. (2018). HCI challenges and privacy preservation in big data security. IGI Global.
Roebuck, K. (2011). Customer relationship management (CRM) software: High-impact strategies, what you need to know. Publisher not identified.
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