BU Business Data Cleaning Process in Organizations Discussion – Description
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.
respond to two of your classmates:
1?Data cleaning is an essential step in the data analysis process, especially about big data. It refers to identifying and removing inaccurate or irrelevant portions of their dataset. The primary objective of this process is to improve the quality and reliability of their dataset, ensuring that it can provide accurate results when subjected to analysis using statistical models and methods (Kar & Dwivedi, 2020). An excellent example would be removing outliers from a housing price dataset scrapped online by scraping tools. Outliers such as extremely high-priced homes located in areas where similar properties are much more affordable could skew the mean housing prices for specific regions, resulting in meaningless conclusions derived later during analysis.
Another essential aspect involved includes refining computational models used based on pre-existing business practices reviewed over time through either trial-and-error approaches, including learned assumptions, etc., or very traditional empirical paperwork filing procedures practiced since newsprint was invented due diligence so all inconsistencies within input-independent variables potential future errors idiosyncrasies thoroughly smoothed-out before rendering plausibility’s confidence level biostable enough confer strong likelihood assessment premised primarily around unsupervised modelling applied through axiomatic mappings transformed probability distributions speaking holistic net effects observable local outcomes (Kar & Dwivedi, 2020). Proper Data Cleaning techniques require attention to both typos/ill-formed groups like NULLs, blank form fields, bad links, impossible ratios breaching some boundaries, anomalies introduced toward downstream analyses recommendations follow predefined higher-model accuracy objectives, corrected functional features/actions dependent upon effective training served sufficiently overall.
2?There are four main methods for data discovery and data cleansing: When data is visualized rather than just read aloud, it is simpler to spot trends and make inferences. While it may be difficult to find patterns and uncover hidden opportunities in plain text, anyone can quickly and easily make sense of the enormous amounts of data provided by companies like Google and Facebook. Your setup can be connected to produce a number of reports. A filter could be applied to the data to look for unnoticed patterns or promising trends. Users can use filters to analyze raw data and gain insights from it. The method separates out the most crucial aspects of raw data and discards the rest. Charts, graphs, and reports utilizing this data are possible (Xu et al., 2015).
The main hypothesis I want to investigate in this work is that a large portion of the data that make up big data is hard to interpret when presented in just text format. Nowadays, there is so much data that it is impossible for humans to sift through it all and look for patterns or trends. The explanations for this are given belowThe explanations for this are given below. People need a fresh perspective to view the data if you want them to understand its significance on a deeper level. By utilizing cutting-edge techniques or tools, like those used to produce reports or visualizations, this type of data exploration seeks to discover patterns that were previously unnoticed. In order to improve the overall quality of the data for analysis, errors and outliers are removed during the data cleaning process. This concept can be used in conjunction with any of the aforementioned techniques, including visualization. If the errors in the current data are not fixed, the potential advantages of Big Data might be reduced. This emphasizes the necessity of verifying your analytical data and ensuring that your reports accurately and fully reflect the information your audience requires. Big Data can be used to analyze people’s online behaviors and actions to find patterns and opportunities if it is protected properly.
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