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Campbellsville University Data Cleaning Discussion and Responses

Campbellsville University Data Cleaning Discussion and Responses


When asked a question like, “What is Data cleaning?”, we as the subject matter expert (SME) are given the chance to impart specific knowledge to the person asking the question. For the discussion forum, identify a specific function that the item performs, and provide an explanation of how to utilize the item with Big Data.


Data cleaning is the process of eliminating redundant or superfluous observations from datasets, which mostly occurs during the data collecting phase. Duplicate data can be produced when merging datasets from many sources, scraping data, or getting data from numerous clients or departments. When cleaning data, de-duplication is a significant area that is taken into account, and when observations are noticed that don’t suit the problem that is being studied, they become irrelevant observations (Tableau, 2022). For instance, it is required to remove these useless observations when analyzing data on millennial customers yet the dataset also includes observations from older generations. By reducing distractions from the main goal, this will maximize the effectiveness of the analysis and result in more efficient and manageable datasets.Because big data holds an increasing amount of volume, velocity, and variety of data from multiple applications, data cleansing is thought to be difficult. Big data is complex and enormous by nature due to the continuous incremental accumulation of unstructured and semi-structured data from many sources, and data scientists are unable to utilise these data until they have been turned into an uniform form (Bhatt, 2021). By using data cleaning techniques such parsing, standardizing, clear formatting, fixing errors, language translation, handling missing data, and removing unnecessary outliers, large data can be improved.ReferencesTableau. (2022). Guide to Data Cleaning: Definition, Benefits, Components, And How To Clean Your Data., V. (2021, April 5). The Significance of Data Cleansing in Big Data. AiThority.


In the advent of the COVID-19 situation, the pandemic has meet people vulnerable and the use of the Internet and online mode of transaction became prevalent. Data and the information that is present in the computer sometimes needed to be checked. Data cleaning is a procedure which is very much vital so that it does not make things crowded and irrelevant. The process benefitted all and the establishment of the procedure make an organization capable of handling things (Ilyas and Chu, 2019). Data are needed to get considered and implemented so that it becomes much more convenient. Data needed to be accurate so that confusion does not come up. The conclusion is that individuals have come across the importance that lies in cleaning the data. The data are something where there are times corruption also takes place. Individuals think it is necessary to keep the data clean and keep away this information which does not have its power base. After the Covid-19 pandemic, people started to become much more aware of the fact. Data needed to be kept under safety and full security (Wang and Wang, 2019).  The process is so much innovative that it has become so integrated and benefitting the surroundings. The more the cleaning is done viruses and other interventions can be avoided.

References:Ilyas, I.F. and Chu, X., (2019). Data cleaning. Morgan & Claypool.Wang, X. and Wang, C., (2019). Time series data cleaning: A survey. Ieee Access, 8, pp.1866-1881.

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