Why Are Raw Data Not Readily Usable By Analytics
Why Are The Originalraw Data Not Readily Usable By Analytics Tasks W
Why are the original/raw data not readily usable by analytics tasks? What are the main data preprocessing steps? List and explain their importance in analytics. Your response should be words. There must be at least one APA formatted reference (and APA in-text citation) to support the thoughts in the post. Do not use direct quotes, rather rephrase the author's words and continue to use in-text citations.
Paper For Above instruction
Raw data, as initially collected from various sources, are often incompatible with the requirements of analytical tasks because they are typically unstructured, inconsistent, and contain irrelevant or redundant information. These issues hinder the effective extraction of insights and can lead to inaccurate or misleading results if not addressed properly. Therefore, data preprocessing becomes a vital step in transforming raw data into a suitable form for analysis.
One fundamental preprocessing step involves data cleaning, which aims to remove inaccuracies, handle missing values, and correct errors within the dataset. Inaccurate or incomplete data can significantly distort analytical outcomes. For example, missing data can bias results, and erroneous entries can produce false correlations. Cleaning data ensures the quality and reliability of the analysis, making it an essential task.
Another key step is data transformation, which includes normalization, scaling, and encoding of data. Normalization ensures that features are on a comparable scale, preventing features with larger ranges from dominating the analysis. Encoding, such as converting categorical data into numerical form, allows algorithms to process non-numeric data effectively. These steps improve the efficiency and accuracy of machine learning models and statistical analyses.
Data integration is also critical, especially when data are collected from multiple sources. Combining datasets requires resolving conflicts and inconsistencies to generate a comprehensive view. Without proper integration, analysis could be fragmented, reducing the overall usefulness of the insights gained. Consistency and completeness are thus maintained through integration processes.
Feature engineering, which involves selecting, modifying, or creating new features from raw data, significantly enhances the model's ability to predict or classify. Relevant features can improve model performance, whereas irrelevant features can introduce noise and reduce accuracy. Proper feature engineering requires domain knowledge and analytical insight.
Sample processing steps like data reduction, which involves reducing the volume of data while preserving essential information, help manage computational resources and improve processing speed. Techniques such as dimensionality reduction (e.g., Principal Component Analysis) simplify data complexity without losing critical information.
Overall, data preprocessing is crucial in converting raw, often messy data into a structured, reliable format suitable for sophisticated analytical tasks. Proper preprocessing ensures that the results of data analysis are valid, consistent, and actionable, ultimately leading to better decision-making and insights.
According to Han et al. (2011), preprocessing enhances data quality, which directly impacts the effectiveness of analytics models. When data are properly prepared, the insights derived are more accurate and dependable, emphasizing the importance of these preparatory steps in data analytics workflows.
References
- Han, J., Kamber, M., & Pei, J. (2011). Data Mining: Concepts and Techniques (3rd ed.). Morgan Kaufmann.