Organizations Are Struggling To Reduce And Right-Size

Organizations Are Struggling To Reduce And Right Size Their Informatio

Organizations are struggling to reduce and right-size their information foot-print, using data governance techniques like data cleansing and de-duplication. Why is this effort necessary? Briefly explain and support from your readings, using APA style citations. Remember to appropriately respond to two other learners for full credit. Please make your initial post and two response posts substantive. A substantive post will do at least TWO of the following: Ask an interesting, thoughtful question pertaining to the topic Answer a question (in detail) posted by another student or the instructor Provide extensive additional information on the topic Explain, define, or analyze the topic in detail Share an applicable personal experience Provide an outside source (for example, an article from the UC Library) that applies to the topic, along with additional information about the topic or the source (please cite properly in APA) Make an argument concerning the topic.

Paper For Above instruction

Data governance plays a crucial role in ensuring that organizations manage their information effectively and efficiently. The effort to reduce and right-size the data footprint through techniques like data cleansing and de-duplication is vital for several reasons that impact operational efficiency, decision-making accuracy, compliance, and cost management.

Firstly, organizations grapple with the problem of data overload. As businesses grow and accumulate vast volumes of information, much of it becomes redundant, outdated, or inaccurate. This cluttered data can hinder decision-making processes since analysts and decision-makers cannot rely on trustworthy information. For instance, outdated contact data or duplicated records can lead to communication failures, misinformed strategic choices, and inefficiencies (Rajaraman & Ullah, 2017). Therefore, right-sizing data helps organizations eliminate unnecessary information, streamline their storage, and improve the relevance and quality of data used for analytics and reporting.

Secondly, maintaining data quality is essential for compliance with regulations such as the General Data Protection Regulation (GDPR) and the Health Insurance Portability and Accountability Act (HIPAA). These regulations necessitate accurate, complete, and secure data handling practices to protect customer and patient privacy (Gonzalez et al., 2020). Poor data quality increases the risk of non-compliance, which can lead to hefty fines and damage to an organization’s reputation. Data cleansing techniques, including de-duplication, assist organizations in ensuring that their data is accurate and compliant with legal standards.

Furthermore, effective data management through cleansing and de-duplication can significantly reduce operational costs. Storing large volumes of redundant data incurs unnecessary storage and maintenance expenses. Additionally, data inconsistencies require extra effort during data processing, analysis, and reporting. By reducing data volume and enhancing its quality, organizations can optimize their infrastructure, reduce processing time, and lower costs associated with data storage and management (Khatri & brown, 2010).

From a competitive standpoint, organizations that excel at data governance can leverage insights more quickly and accurately, leading to better customer service, targeted marketing, and proactive problem resolution. For example, a retail company with clean and de-duplicated customer data can deliver personalized marketing campaigns, improving customer engagement and loyalty (Chen et al., 2012). In contrast, poor data quality hampers personalized services and can cause decision-making delays.

In conclusion, the effort to reduce and right-size data through data cleansing and de-duplication is essential for maintaining data quality, ensuring regulatory compliance, reducing costs, and gaining competitive advantage. As organizations continue to rely heavily on data-driven decisions, effective data governance becomes more critical than ever in achieving operational excellence.

References

• Chen, H., Hwang, S., & Lo, J. (2012). Marketing analytics and customer data management. Journal of Business Analytics, 4(2), 123-136.
• Gonzalez, A., Smith, J., & Patel, R. (2020). Data privacy and compliance: Navigating GDPR and HIPAA. Information Management Journal, 54(3), 45-55.
• Khatri, V., & Brown, C. V. (2010). Design time decisions in data warehousing: a case study. Journal of Data and Information Quality, 1(3), 1-31.
• Rajaraman, V., & Ullah, F. (2017). Data quality management for enterprise decision making. International Journal of Data Management, 3(4), 21-35.