Limitations Of Spreadsheets For Data Management And The Advantages Of Databases

Data redundancy

The spreadsheet has been a solid backbone of accounting as well as financing for a decade, although technology has dramatically changed now. Outdated tools like spreadsheets need to evolve along with providing ways for better tools to keep that pace with all changing responsibilities. Spreadsheets aren’t collaborative and do not provide the ability in monitoring the overall process of transaction matching, along with these manages this within one place (Dou, et al., 2018). Spreadsheets also lack major functionality to manage compliance initiatives. The risks consist of increased fees of audit along with associated costs of compliance.

Data redundancy: Data redundancy takes place when the same data is kept within several locations, along with the common occurrence within several businesses. Data redundancy could result in a complicated process and inefficient coding. There is the use of spreadsheet applications for managing crucial data; however, they have some limitations when compared to the real database. The spreadsheet is inherently simpler and easier to understand, along with are much more familiar to numerous people when compared to the databases (Mack, et al., 2018). Due to data dependency within the spreadsheets, the same data could be stored in several locations of the same spreadsheet.

Data independence: Data independence is a crucial characteristic that allows changing the structure without making any changes within the programs. There is an existence of data independence when the characteristics of data storage are changed without impeding the ability of the program in accessing that data (Bøgholm, et al., 2019). The logical schema isn’t changed, although some storage space or data is altered due to optimization or reorganization. Logical data independence within the spreadsheets is much tougher to achieve when compared to physical data independence, as these are highly dependent upon the data’s logical structure used to access them.

Data consistency: Data inconsistency is the same data discrepancy within the spreadsheets. It means that a minimum of 2 data is entered within the spreadsheet in another format. Within the spreadsheets, such data inconsistency could take place between the cells. Fixing the data for making this consistent through the spreadsheets is also quite time-consuming. It takes time from all other crucial works for fixing the past mistakes (Azam, et al., 2019). Such a process forces in wasting time due to technical errors. Also, different programs, applications, and software have their systems for putting in along with processing data. Hence, this becomes quite tough for transferring inconsistent data within another system.

Data integrity: Spreadsheets have been a major area of issues of data integrity for several years. A huge challenge is how to manage, maintain, along with validating data integrity generated with the use of spreadsheets. Spreadsheets’ data integrity is quite crucial as they generate crucial data which have an impact upon the data. There could be negative impacts due to a lack of data integrity within the spreadsheets, as these are used for manipulating data generated (de Man & Strandhagen, 2018). Lack of inventory is the major indicator of lack of knowledge about overall spreadsheets needing validation along with should meet all requirements of data integrity.

Data independence

Data security: Spreadsheets give the ability in protecting all work if this is preventing anyone from opening the workbook without any password, providing read-only access to the workbook, and protecting the worksheet to ensure any unnecessary changes are not made. As opposed to the dedicated system needing access for logging in, the spreadsheets could be disseminated quite easily anywhere, to anyone with simple sending of the email. It makes this easy for the dishonest or disgruntled employee in sharing customer data along with leads with external contacts. With numerous people using one spreadsheet, as well as with several calculations and edits taking place at once, this is natural that the spreadsheets would include some kind of error (Koch, 2018).

The scale of data sharing: Sharing spreadsheets might sometimes result in document locking or unreadable content. Such errors could unshare that shared workbook with the users. The users wouldn’t be any longer able in saving the data to that shared spreadsheet file. Hence, all users could save their workbooks individually only to the local computers, which could result in possessing the same spreadsheet file’s different copies (Awad, et al., 2020). Such errors could be rising due to corruption within the spreadsheet file. The files with corruption still could be opening as well as functioning; however, at any point, that corruption could cause some issues.

Tables are used by the databases for storing along with retrieving information. The databases are relational, which means data between the tables could be cross-references and linked. In the relational database, all data within the table could be related as per common concepts and keys (Birch, et al., 2018). Databases could incorporate all other kinds of information easily. Databases could accommodate downloads of huge file sizes along with high volumes. As information is stored more efficiently by the databases, volumes of data could be handled by these databases that could be unmanageable within the spreadsheets (Goelman & Dietrich, 2018). Also, there is a record limitation in spreadsheets, whereas databases don’t have any such limitation. Updating databases is easier than updating the spreadsheets, particularly if the same data is maintained within multiple spreadsheets or records.

Within the databases, all regulatory standards are updated within one table, along with would be instantly available for every reporting query of such associated data. Additionally, databases could update all records in bulk. Though data within the spreadsheets could be filtered as well as sorted, the databases have wide querying functionality, which could retrieve cross-reference records within multiple tables, every record matching the criteria, along with performing complicated aggregate calculations over multiple tables. Hence, databases provide more flexibility for sorting along with presenting data in several ways that are almost impossible for these spreadsheets (Shaltry, 2020). Every database is designed in referring to the data without loading every data within the memory. Hence, databases operate much quicker than spreadsheets while handling huge datasets, whereas there are memory limitations for the spreadsheets.

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References

Awad, A., Elgohary, R., Moawad, I., & Roushdy, M. (2020, April). Metadata Extraction for Low-Quality Semi-structured Spreadsheets. In The International Conference on Artificial Intelligence and Computer Vision (pp. 448-457). Springer, Cham. Retrieved from https://doi.org/10.1007/978-3-030-44289-7_42 

Azam, A., Alam, K. A., & Umair, A. (2019, December). Spreadsheet smells: A systematic mapping study. In 2019 International Conference on Frontiers of Information Technology (FIT) (pp. 345-3455). IEEE. Retrieved from https://doi.org/10.1109/FIT47737.2019.00071 Bendre, M., Wattanawaroon, T., Rahman, S., Mack, K., Liu, Y., Zhu, S., … & Parameswaran, A. (2019, April). Faster, higher, stronger: Redesigning spreadsheets for scale. In 2019 IEEE 35th International Conference on Data Engineering (ICDE) (pp. 1972-1975). IEEE. Retrieved from https://doi.org/10.1109/ICDE.2019.00217 

Birch, D., Lyford-Smith, D., & Guo, Y. (2018). The future of spreadsheets in the big data era. arXiv preprint arXiv:1801.10231. Retrieved from https://arxiv.org/abs/1801.10231 

Bøgholm, T., Larsen, K. G., Muñiz, M., Thomsen, B., & Thomsen, L. L. (2019). Analyzing spreadsheets for parallel execution via model checking. In Models, Mindsets, Meta: The What, the How, and the Why Not? (pp. 27-35). Springer, Cham. Retrieved from https://doi.org/10.1007/978-3-030-22348-9_3 

de Man, J. C., & Strandhagen, J. O. (2018). Spreadsheet application still dominates enterprise resource planning and advanced planning systems. Ifac-Papersonline, 51(11), 1224-1229. Retrieved from https://doi.org/10.1016/j.ifacol.2018.08.423 

Dou, W., Han, S., Xu, L., Zhang, D., & Wei, J. (2018, September). Expandable group identification in spreadsheets. In Proceedings of the 33rd ACM/IEEE International Conference on Automated Software Engineering (pp. 498-508). Retrieved from https://doi.org/10.1145/3238147.3238222 

Goelman, D., & Dietrich, S. W. (2018, February). A Visual introduction to conceptual database design for all. In Proceedings of the 49th ACM Technical Symposium on Computer Science Education (pp. 320-325). Retrieved from https://doi.org/10.1145/3159450.3159555 

Koch, P. (2018). Now You’re Thinking With Structures: A Concept for Structure-based Interactions with Spreadsheets. arXiv preprint arXiv:1809.03435. Retrieved from https://arxiv.org/abs/1809.03435 

Mack, K., Lee, J., Chang, K., Karahalios, K., & Parameswaran, A. (2018, April). Characterizing scalability issues in spreadsheet software using online forums. In Extended Abstracts of the 2018 CHI Conference on Human Factors in Computing Systems (pp. 1-9). Retrieved from https://doi.org/10.1145/3170427.3174359 

Shaltry, C. (2020). A new model for organizing curriculum alignment initiatives. Advances in physiology education, 44(4), 658-663. Retrieved from https://doi.org/10.1152/advan.00174.2019 

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