Data Integration and Data Redundancy: The pros and cons
Organizations can choose to use data redundancy or data integration as a way of managing their data. Data redundancy involves data being duplicated in several locations and then stored elsewhere. On the other hand, data integration is where multiple data sets are combined and stored in one place. The tradeoff between the two strategies depends on an organization’s data needs and their priorities. Due to multiple copies of data, data redundancy can improve data security and reduce costs associated with recovering data in the event of a system or network failure (Kiesel 2015). If data changes to the same data are made in different systems, it can lead to inconsistencies (Citron 2018,). Data integration, on the other hand can reduce redundancy and improve access to data and share data, as well as speed up data processing (Mulley 2019,). Combining multiple data sets can prove to be time-consuming and difficult. Duplication of data can lead to data loss, and data integration could make it more difficult for data security (Mckay, Chhikara 2021). Both data redundancy (and data integration) have advantages and drawbacks. This is why it’s important to carefully consider the pros and cons of each when planning a data management strategy. References Citron, D. (2018). Understanding data redundancy in database systems. Retrieved from https://securityintelligence.com/understanding-data-redundancy-in-database-systems/ Kiesel, F. (2015). Advantages & Disadvantages of Data Redundancy. Retrieved from https://smallbusiness.chron.com/advantages-disadvantages-data-redundancy-60306.html Mckay, D. & Chhikara, R. (2021). Integration vs. Redundancy: What’s the Big Data Trade-off? Retrieved from https://www.datafloq.com/read/integration-versus-redundancy-the-big-data-trade-off/12671 Mulley, C. (2019). Data Integration vs. Data Redundancy: What’s The Difference? Retrieved from https://www.iss.nus.edu.sg/data-integration-vs-data-redundancy-whats-the-difference/Cont…
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