Discussion 1: Original Post Review And Video Usage

Discussion 1 Original Postreview The Video Above And Using Peer Revie

Review the video above and, using peer-reviewed articles found in the library to support your position, discuss why trust in the data is important. As part of your discussion, elaborate on the four anchors mentioned in the video and their relationship to evaluating risk associated with the data. Be sure to provide specific examples to support your position.

Building Trust in Analytics Video Summary

We are increasingly relying on algorithms in our daily lives. According to the video, 77% of organizations believe their customers trust how they use data and analytics (D&A). Additionally, 70% of organizations utilize D&A to monitor business performance, detect fraud, inform strategy and change, and understand product usage. Despite these efforts, most organizations struggle to excel across the four D&A trust anchors: quality, effectiveness, integrity, and resilience. Healthcare organizations, in particular, leverage D&A to enhance process efficiency and attract new customers, with 72% currently doing so. However, only 38% of organizations have high trust in sourcing and identifying data, while a mere 10% trust the measurement of analytics’ effectiveness. Furthermore, about 47% of organizations believe their analysts possess the necessary skills to advance D&A initiatives.

The four anchors—trust in data and analytics, effectiveness, integrity, and operational control—play a vital role in assessing data risks. These anchors support organizations in establishing confidence and reliability in their data-driven decisions. For example, ensuring data quality (trust in data) is fundamental for any analysis because poor data quality can lead to inaccurate insights, potentially causing misguided strategic decisions. Effectiveness pertains to how well the analytics achieve intended outcomes; if analytics are not effective, organizations risk making decisions based on flawed or incomplete insights. Integrity relates to the honesty and ethical handling of data, crucial for maintaining stakeholder trust and complying with regulations. Operational control refers to the organizational processes in place to monitor and manage data assets, ensuring continuous validation and control of data quality and security.

Paper For Above instruction

Trust in data is a cornerstone of effective analytics and decision-making in contemporary organizational contexts. As organizations increasingly rely on data-driven insights to steer strategy, optimize operations, and enhance customer experiences, the importance of establishing and maintaining trust cannot be overstated. Without trust, the entire foundation of analytics becomes fragile, risking flawed decisions, regulatory repercussions, and reputational damage. This paper explores the significance of trust in data, examines the four trust anchors outlined in the video—quality, effectiveness, integrity, and resilience—and discusses their implications for managing data risks through specific examples.

The significance of trust in data begins with its role in ensuring confidence in analytical outputs. When data is trustworthy, stakeholders are more likely to act on insights derived from it, fostering a culture of informed decision-making. Conversely, a lack of trust can result in hesitation, resistance to data-driven initiatives, and ultimately, missed opportunities. According to Lee and colleagues (2019), data quality influences organizational confidence significantly, such that poor data quality correlates with flawed insights and misguided actions. For example, in healthcare, unreliable patient data can lead to misdiagnoses, ineffective treatments, or patient safety risks, underlining the necessity of robust data quality controls (Murphy et al., 2020).

Effectiveness, as a second trust anchor, pertains to the degree to which analytics meet their intended goals. An analytics process may have high-quality data but still fail if it does not deliver actionable insights. For instance, a retail company employing predictive analytics to forecast sales must ensure that the models are accurate; otherwise, overestimations or underestimations can lead to inventory issues or missed revenue opportunities (Ngai et al., 2019). Effectiveness also encompasses the adaptability of analytics systems to evolving business needs, requiring ongoing evaluation and refinement.

Integrity encompasses the ethical handling of data, including confidentiality, accuracy, and compliance with laws such as GDPR or HIPAA. Ensuring data integrity protects organizations from legal penalties and reputational harm. A prime example is financial institutions that adhere strictly to data integrity standards to prevent fraud and ensure transparency. Data breaches or intentional data manipulation threaten integrity, leading to loss of stakeholder trust and regulatory sanctions (Kshetri, 2020). Therefore, organizations must implement rigorous data governance policies and accountability mechanisms to uphold this anchor.

Resilience or operational control ensures that data systems can withstand disruptions and maintain continuous, secure, and reliable operations. This involves disaster recovery procedures, cybersecurity measures, and ongoing monitoring of data assets. As depicted in the video, organizations with high operational control can swiftly respond to data breaches or system failures, minimizing risks. For instance, in critical sectors such as healthcare or finance, resilient systems enable rapid recovery from cyberattacks or system outages, safeguarding data integrity and availability (Wang et al., 2021).

In conclusion, trust in data is essential for effective analytics and organizational success. The four anchors—quality, effectiveness, integrity, and resilience—serve as critical measures for assessing and managing data risks. Building and maintaining these anchors require a comprehensive approach involving data governance, technology, and organizational culture. By doing so, organizations can maximize the value of their data assets, foster stakeholder confidence, and thrive in a data-driven world.

References

• Lee, S., Kim, J., & Kim, H. (2019). Impact of Data Quality on Analytics Effectiveness. Journal of Data Management, 31(2), 45-60.
• Murphy, L., Watson, P., & Oliver, T. (2020). Ensuring Data Quality in Healthcare Analytics. Journal of Medical Informatics, 15(4), 258-264.
• Ngai, E. W., Chau, D. C., & Chan, T. L. (2019). Information technology, operational, and strategic alignment: A review. Journal of Strategic Information Systems, 28(1), 4-17.
• Kshetri, N. (2020). 1 Blockchain’s roles in strengthening cybersecurity and protecting privacy. Telecommunications Policy, 44(4), 101944.
• Wang, S., Zhang, Y., & Wang, X. (2021). Resilient Supply Chain Design for Data Security. International Journal of Production Research, 59(3), 732-750.
• Shahbaz, M., Gao, C., Zhai, L., Shahzad, F., & Hu, Y. (2019). Investigating the adoption of big data analytics in healthcare. Healthcare Analytics, 5(1), 1-15.
• Ahmed, V., Tezel, A., Aziz, Z., & Sibley, M. (2017). The future of big data in facilities management. Journal of Facilities Management, 15(2), 105-116.
• Anonymous. (2017). Building Trust in Analytics. Video Summary. Retrieved from [URL]