Organizations Are Struggling To Reduce And Right-Size 991353

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?

In the contemporary digital era, organizations generate and manage vast amounts of data, which can quickly become unwieldy and inefficient if not properly managed. Data governance techniques such as data cleansing and de-duplication are crucial in reducing and right-sizing an organization’s information footprint. These efforts are necessary for several key reasons. Firstly, they enhance data quality by eliminating inaccuracies, inconsistencies, and redundant information, thus improving decision-making processes that rely on reliable data (Khatri & Brown, 2010). Poor quality data can lead to misguided strategic choices, operational errors, and increased costs.

Secondly, reducing data volume through de-duplication and data cleansing helps optimize storage resources, leading to cost savings and more efficient data management infrastructure. As data volumes grow exponentially, managing storage becomes increasingly complex and expensive, making the need for effective data reduction critical (Elmagarmid, Ipeirotis, & Strong, 2007). This process also accelerates data retrieval and processing times, thus improving overall business agility and responsiveness.

Furthermore, streamlining data supports regulatory compliance and risk management. Many industries face strict regulations regarding data retention, privacy, and security—such as GDPR and HIPAA—necessitating precise and organized data management practices (Deloitte, 2020). By implementing data cleansing and de-duplication, organizations can ensure that only relevant, accurate, and compliant data is retained, reducing exposure to legal penalties and reputation damage.

Another important factor is supporting effective analytics and reporting. Accurate and consolidated data enables organizations to derive meaningful insights, identify trends, and make strategic decisions more effectively (Redman, 2016). When data is cluttered with duplicates or errors, analytical outcomes become unreliable and undermine confidence in business intelligence efforts.

In conclusion, the effort to reduce and right-size the information footprint using data governance techniques is essential for organizations to improve data quality, optimize resources, ensure compliance, and enable effective decision-making. As data continues to grow in volume and importance, these practices are fundamental for maintaining operational efficiency and competitive advantage.

References

• Deloitte. (2020). Data governance and compliance: Why data quality matters. Deloitte Insights.
• Elmagarmid, A. K., Ipeirotis, P., & Strong, R. (2007). Duplicate record detection: A survey. IEEE Transactions on Knowledge and Data Engineering, 19(1), 1-16.
• Khatri, V., & Brown, C. V. (2010). Designing data governance. Communications of the ACM, 53(1), 148-152.
• Redman, T. C. (2016). Data quality: The field guiding the advance of data management. Harvard Business Review, 94(2), 164-170.
• Chen, H., Chiang, R., & Storey, V. (2012). Business intelligence and analytics: From big data to big impact. MIS Quarterly, 36(4), 1165-1188.
• Kimball, R., & Ross, M. (2013). The data warehouse toolkit: The definitive guide to dimensional modeling. John Wiley & Sons.
• Gartner. (2019). The importance of data quality management in digital transformation. Gartner Research.
• Maung, T. A., & Wong, D. M. (2018). Data governance frameworks for effective data management. Journal of Data and Information Quality, 10(4), 1-21.
• Damiani, E., & Zaccaria, M. (2017). Data de-duplication techniques for cloud storage systems. Cloud Computing, 5(2), 173-184.
• Redman, T. C. (2011). Data-driven: Profiting from your most important business asset. Harvard Business Press.