Data Governance and Quality – Implications for EDM and Compliance
Comparative Analysis of Data Governance Frameworks
Young and McConkey (2012) identified the necessity of implementing effective data governance to maintain data quality and enhance compliance. They call for a structured framework to define the policies, roles, and responsibilities of data as an organizational asset. At the same time, Otto (2011) elaborates that more effective data quality management requires cleansing, validation, and enrichment of data.
On the other hand, Abraham et al. (2019) present a strategic, systemic approach to the governance of data quality that involves aligning the issue with organizational goals and objectives. Some of these are data acquisition and data ownership, also referred to as data stewardship, focusing on metadata and master data management as some of the primary components that are significant in data quality, coherence, and protection.
Impact on HealthcareX’s EDM Framework
The discrepancies in these governance frameworks are somewhat profound and directly affect the EDM of HealthcareX. Young and McConkey (2012) and Otto (2011) stress the importance of setting up basic governance practices to deal with data quality concerns, which are significant for HealthcareX due to the problems related to data inconsistency and inaccuracy it faced at the time. By adopting these measures, HealthcareX is poised to improve the reliability and accuracy of its EMR and PHI, thus providing quality care for patients and increasing operational efficiency.
Abraham et al. (2019) propose a more holistic approach that identifies the organization’s main goals and adapts data management to them. By utilizing such an approach, HealthcareX can guarantee that requirements for data governance are maintained to tackle quality problems and to further longer-term visions for better clinical decisions and compliance with the laws set by authorities.
Compliance Implications
From a compliance perspective, sound data management is a key condition that will enable HealthcareX to address laws such as HIPAA. Strong data governance structures minimize risks related to compromised data, unauthorized access, and non-compliance with the existing laws on data privacy. For instance, introducing effective data quality management solutions may prevent potential medical errors caused by inaccurate patient data, thus improving patients’ safety and decreasing legal exposures (Redman, 2016).
Based on the identified issues, HealthcareX needs to have a proper data governance structure to support data management effectively and support compliance with the healthcare rules and regulations. The frameworks by Young and McConkey (2012), Otto (2011), and Abraham et al. (2019) can shed light on how HealthcareX can improve the quality of collected and shared data, as well as its coherence and protection. HealthcareX can adopt these practices into its EDM plan to address these objectives in order to enhance patient care, organization performance, and corporate and legal requirements, thereby fulfilling its role as the premier healthcare institution.
References
Abraham, R., Schneider, J., & Vom Brocke, J. (2019). Data governance: A conceptual framework, structured review, and research agenda. International journal of information management, 49, 424-438.
Otto, B. (2011). Organizing data governance: Findings from the telecommunications industry and consequences for large service providers. Communications of the Association for Information Systems, 29(1), 3.
Redman, T. C. (2016). Getting in front of data: Who does what? Technics Publications.
Young, A., & McConkey, K. (2012). Data governance and data quality: Is it on your agenda?. Journal of Institutional Research, 17(1), 69-77.
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Question 
Primary Task Response: Within the Discussion Board area, write 1 page that responds to the following questions with your thoughts, ideas, and comments. This will be the foundation for future discussions with your classmates. Be substantive and clear, and use examples to reinforce your ideas.
Data Governance and Quality – Implications for EDM and Compliance
Read the Young and McConkey (2012) article and Otto (2011) articles about data governance and data quality. Read the Abraham et al (2019) articles to understand their governance framework (specifically the Figure 4 scope and elements). SEE ATTACHED
Discuss the differences and how they impact EDM, especially your EDM framework and company data. What does it mean to the company from a compliance perspective?