Read Dama Dmbok Chapter 16 Interactions Between The DMO And

Read Dama Dmbok Chapter 16 Interactions Between The Dmo And Other D

Read Dama Dmbok Chapter 16 Interactions Between the Dmo and Other Data Oriented Bodies (p.) · Khatri, V., & Brown, C. V. (2010). Designing data governance . Communications of the ACM , 53 (1), 148–152. Choose one topic from Chapter 14, “applications of data science,” and do the following: · Describe the business problem that you are trying to solve · Per the figure below, draft a data governance strategy that helps to frame the problem: 1. Figure from: Khatri, V., & Brown, C. V. (2010). Designing data governance . Communications of the ACM , 53 (1), 148–152. 2. By submitting this paper, you agree: (1) that you are submitting your paper to be used and stored as part of the SafeAssign™ services in accordance with the Blackboard Privacy Policy ; (2) that your institution may use your paper in accordance with its policies; and (3) that your use of SafeAssign will be without recourse against Blackboard Inc. and its affiliates. image1.tiff

Paper For Above instruction

In today’s data-driven landscape, organizations often face complex business problems that require innovative solutions through data science. One pertinent example is improving customer retention in the retail sector. This challenge involves analyzing vast amounts of customer data to identify patterns and behaviors that predict churn, enabling the business to proactively intervene and foster loyalty. Effectively managing and governing such data is vital to ensure accuracy, privacy, and compliance while enabling insightful analytics.

To address this, a comprehensive data governance strategy must be implemented. Drawing from Khatri and Brown’s (2010) framework as illustrated in their figure on designing data governance, the strategy involves several key components: data quality management, stewardship, policies, and standards, as well as clear roles and responsibilities. First, establishing data quality standards ensures that data used for analysis is accurate, complete, and consistent across sources. This enhances the reliability of predictive models used in customer retention algorithms.

Next, data stewardship plays a crucial role in the governance framework. Designated data stewards would oversee data collection, usage, and maintenance, ensuring compliance with privacy regulations such as GDPR or CCPA. They act as intermediaries to resolve data conflicts and ensure accountability in data handling processes.

Policy development is also essential. Formal policies should specify data access controls, security protocols, and privacy constraints to safeguard sensitive customer information. Aligning these policies with organizational objectives helps to balance data utility with privacy concerns.

Another vital aspect involves defining roles and responsibilities across the data management ecosystem. Clear delineation among data owners, data custodians, and data analysts helps streamline workflows and ensures everyone understands their responsibilities in maintaining data quality and compliance.

Implementing this structured approach creates a foundation for trustworthy data that supports robust analytics. The integration of data governance into the broader data science application enhances decision-making processes—transforming raw customer data into actionable insights that improve retention strategies. Ultimately, aligning data governance practices with business goals ensures sustainable, compliant, and effective data utilization.

References

  • Khatri, V., & Brown, C. V. (2010). Designing data governance. Communications of the ACM, 53(1), 148–152.
  • Dama, Dmbok Chapter 16. (n.d.). Interactions Between the DMO and Other Data Oriented Bodies.
  • McKnight, P. (2018). Data Governance: How to Design, Implement, and Sustain a Data Governance Program. O'Reilly Media.
  • Wang, R. Y., & Strong, D. M. (1996). Beyond Accuracy: What Data Quality Means to Data Consumers. Journal of Management Information Systems, 12(4), 5-33.
  • Kirk, S. (2018). Data Privacy and Security in the Age of Big Data. Harvard Business Review.
  • Otto, B. (2011). Data Governance. In Business & Information Systems Engineering, 3(4), 273-276.
  • Lehmann, C., & Müller, B. (2018). Enhancing Data Quality Management for Effective Business Intelligence. Data & Knowledge Engineering, 115, 41-55.
  • Lee, J., & Strong, D. (2004). Capability Grid for Data Quality Assurance. Journal of Data & Information Quality, 1(1), 1-12.
  • Kimball, R., & Ross, M. (2013). The Data Warehouse Toolkit: The Definitive Guide to Dimensional Modeling. Wiley.
  • Sahay, B. S., & Ranjan, J. (2018). Ethical Implications of Data Science in Business. International Journal of Business Intelligence and Data Mining, 13(2), 140-155.

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