Data Anomalies
Data anomalies refer to any data inconsistencies, data errors, or data outliers that occur in a dataset. These anomalies can significantly impact the data accuracy and data reliability of data analysis, making it crucial to identify and address them promptly. There are various types of data anomalies, including but not limited to outliers, missing data, and duplicate records. These inconsistencies may arise due to errors in data entry, faulty sensors, or incorrect calculations. If not addressed, they can distort the analysis results, leading to misguided business decisions or faulty machine learning predictions.
https://en.wikipedia.org/wiki/Anomaly_detection
One of the most common types of data anomalies is the outlier, which occurs when a data point deviates significantly from the rest of the data. These outliers can be caused by errors in data collection, changes in system behavior, or rare events that are outside the scope of normal operations. For example, a sudden surge in sales due to a temporary promotion might be considered an outlier in a sales dataset. Identifying and handling these outliers is crucial, as they can skew statistical models or machine learning algorithms. In most cases, techniques like normalization, data transformations, or removal of outliers are used to address these issues.
https://www.kaggle.com/learn/feature-engineering
Another common type of data anomaly is missing data, which can occur due to incomplete data collection, network failures, or data corruption. Incomplete datasets can lead to incorrect analyses, biased results, or failure of machine learning algorithms. Methods to handle missing data include data imputation, where missing values are filled with predicted values or estimated values, or data deletion, where rows with missing data are removed. Understanding the source and impact of missing data is essential for maintaining the integrity of any dataset. Furthermore, missing data can sometimes indicate a deeper issue with data collection systems, making it important to resolve the root cause.