Abstract
Data quality serves as the cornerstone of effective Management Information Systems (MIS) and their role in enabling strategic decision-making. This academic study examines the importance of data quality through the lens of MIS, focusing on its technical, organizational, and strategic dimensions. By leveraging peer-reviewed references, standardized frameworks (e.g., DAMA-DMBOK), and real-world case studies, this paper equips MIS students and researchers with tools to understand how data quality impacts system design, analytics, and organizational value creation.
1. Introduction
In the digital age, data quality has evolved from a technical concern to a strategic imperative for MIS. According to Markus (2001), data quality determines the efficacy of information systems and their ability to support decision-making processes. MIS, positioned at the intersection of technology and business, provides an ideal framework for studying how data quality influences:
- Database design and decision support systems (Elmasri & Navathe, 2016).
- Data governance and regulatory compliance (Khatri & Brown, 2010).
- Big data analytics and artificial intelligence (Cai & Zhu, 2022).
2. Dimensions of Data Quality in MIS
Based on the DAMA-DMBOK framework, data quality is measured through multiple dimensions that interact with MIS components:
| Dimension | Impact on MIS | Academic Reference |
| Accuracy | Reduces input errors in ERP systems | Weber et al. (2009) |
| Completeness | Ensures coverage of all scenarios in simulations | Batini & Scannapieco (2016) |
| Timeliness | Enables real-time analytics | Power (2002) |
3. Data Quality and Information Systems Design
A. Decision Support Systems (DSS):
The effectiveness of DSS hinges on the quality of input data. In a study by Shim et al. (2002), 40% of analytical errors were attributed to inaccurate data, leading to flawed resource allocation decisions.
B. Database Systems:
Robust database design (e.g., normalization) minimizes redundancy and ensures consistency (Date, 2003). For example, poor data quality in Hershey’s ERP system in 1999 caused order fulfillment failures during peak seasons, resulting in $100 million in losses.
4. Technical and Organizational Challenges
A. Big Data:
As data volume and velocity increase, traditional MIS tools struggle to ensure quality. A 2022 MIS Quarterly study found that 60% of organizations face challenges applying quality controls to unstructured data (Cai & Zhu, 2022).
B. Data Governance:
Per Khatri & Brown (2010), effective data governance in MIS requires:
- Clear data management policies.
- Defined roles (e.g., data stewards).
- System integration to prevent data silos.
5. Case Studies: Successes and Failures
A. Success Case: Walmart
By leveraging accurate customer behavior data, Walmart developed a predictive analytics system that improved supply chain efficiency by 15% (Davenport, 2006).
B. Failure Case: Knight Capital Group
In 2012, inconsistent data in algorithmic trading systems caused a $440 million loss in 45 minutes, underscoring the importance of data validation in financial systems (JMIS, 2013).
6. Best Practices for MIS Students and Researchers
- Integrate Theory and Practice:
- Apply frameworks like DAMA-DMBOK for holistic system design.
- Analyze case studies from journals like JMIS to understand real-world challenges.
- Leverage Emerging Technologies:
- Use AI algorithms (e.g., machine learning) for anomaly detection (Cai & Zhu, 2022).
- Implement tools like Apache NiFi for real-time data quality enhancement.
- Strengthen Governance Skills:
- Study governance models in regulated industries (e.g., healthcare under HIPAA).
7. Conclusion
Data quality is not merely a technical requirement but a critical factor in designing MIS that drive strategic decisions. For MIS students and researchers, this field offers opportunities to explore the intersection of theory and practice—from optimizing databases to addressing AI challenges. By focusing on specialized academic references and case studies, MIS can deliver innovative solutions that transform data into tangible organizational value.