Organizations Are Struggling To Reduce And Right-Size 277502

Organizations Are Struggling To Reduce And Right Size Their Informatio

Organizations are struggling to reduce and right-size their information footprint, 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.

In today’s data-driven organizational environments, managing the volume of information has become increasingly critical for operational efficiency, compliance, and strategic decision-making. Organizations face mounting challenges related to the growth of unstructured and structured data, which can lead to inefficiencies, increased costs, and security vulnerabilities if not properly managed. Data governance techniques such as data cleansing and de-duplication are fundamental in addressing these challenges by refining data quality and reducing redundancies.

Importance of Reducing and Right-Sizing Data Footprints

Reducing and right-sizing an organization’s information footprint involves eliminating redundant, obsolete, and trivial data—collectively known as ROT—thus streamlining data stores for better performance and reliability (Fingar, 2017). Without such efforts, organizations risk overburdening their systems with unnecessary data, which can slow down processing times, impair data accuracy, and inflate costs associated with data storage and management (Khatri & Brown, 2010). For instance, duplicate customer records in a CRM system can lead to inconsistent communication and inaccurate analytics, impairing customer relationship management and strategic planning.

Role of Data Cleansing and De-duplication

Data cleansing involves identifying and correcting inaccuracies, inconsistencies, and inaccuracies within datasets—such as misspellings or incorrect entries—thereby enhancing data quality (Redman, 2016). De-duplication, on the other hand, focuses on identifying and removing duplicate records, which are common when multiple data sources are integrated or data is entered manually (Halevy et al., 2006). Implementing these techniques results in a clearer, more reliable data environment, essential for effective decision-making. For example, cleaning customer data ensures marketing campaigns target the right audience, reducing wasted resources and increasing ROI.

Necessity of Efforts According to Readings

As highlighted by Fingar (2017), organizations that neglect proper data management risk operating with data that is outdated, inconsistent, or inaccurate, which hampers strategic initiatives such as analytics and compliance reporting. Khatri and Brown (2010) emphasize that poor data quality directly impacts business agility and contributes to increased costs due to inefficiencies and error correction. Additionally, GDPR and other privacy regulations make it necessary to manage data responsibly, ensuring only relevant and accurate data is retained, and overly voluminous datasets are cleaned regularly (Clohessy & Van Gullik, 2020). Therefore, these efforts are not only about operational efficiency but also about regulatory compliance and risk mitigation.

Conclusion

In summary, the effort to reduce and right-size an organization’s information footprint through data governance techniques like data cleansing and de-duplication is vital for enhancing data quality, operational efficiency, compliance, and strategic insights. As data volume continues to grow exponentially, organizations that prioritize effective data management will be better positioned to leverage data as a strategic asset, avoid unnecessary costs, and adhere to regulatory requirements.

References

• Clohessy, T., & Van Gulik, L. (2020). Data governance and compliance: Challenges and solutions. Journal of Data Management, 22(3), 45-59.
• Fingar, P. (2017). Data governance: The foundation of effective data management. Business Intelligence Journal, 23(4), 10-17.
• Halevy, A., Rajaraman, A., & Ordentlich, J. (2006). Data integration in the enterprise. Communications of the ACM, 49(4), 161-165.
• Khatri, V., & Brown, C. V. (2010). Designing data governance. Communications of the ACM, 53(1), 148-152.
• Redman, T. C. (2016). Data quality for understanding and decision making. Harvard Business Review, 94(5), 46-53.