Data Anomaly: Types, Causes, Detection, and Resolution

A data anomaly, also known as an outlier, is an observation or data point that deviates significantly from the norm, making it inconsistent with the rest of the dataset. Data anomalies can be either intentional or unintentional and may result from errors, noise, or merely unique occurrences. These anomalies can significantly impact data analysis, leading to incorrect or misleading insights.

Unintentional anomalies are data points that deviate from the norm due to errors or noise in the data collection process. These errors can be either systematic or random, originating from issues like faulty sensors or human error during data entry. Unintentional anomalies can distort the dataset, making it challenging to derive accurate insights.

Intentional anomalies, on the other hand, are data points that deviate from the norm due to specific actions or events. These anomalies can provide valuable insights into the dataset, as they may highlight unique occurrences or trends. For example, a sudden spike in sales during a holiday season could be considered an intentional anomaly, as it deviates from the typical sales pattern but is expected due to a real-world event.

This is part of a series of articles about data integrity.

In this article: 

• Impact of Data Anomalies and Why You Should Address Them
• Types of Data Anomalies
• Causes of Data Anomalies
• Detecting and Resolving Data Anomalies

Data anomalies can have a significant impact on data analysis, leading to incorrect or misleading conclusions. For example, a single outlier can significantly skew the mean of a dataset, making it an inaccurate representation of the data. Additionally, data anomalies can impact the performance of machine learning algorithms, as they can cause the model to fit the noise rather than the underlying pattern in the data.

Identifying and handling data anomalies is crucial for several reasons: 

• Improved data quality: Identifying and handling data anomalies can significantly improve data quality, which is essential for accurate and reliable data analysis. By addressing data anomalies, analysts can reduce noise and errors in the dataset, ensuring that the data is more representative of the true underlying patterns.

• Enhanced decision-making: Data-driven decision-making relies on accurate and reliable data analysis to inform decisions. By identifying and handling data anomalies, analysts can ensure that their findings are more trustworthy, leading to better-informed decisions and improved outcomes.
• Optimized machine learning performance: Data anomalies can significantly impact the performance of machine learning algorithms, as they can cause the model to fit the noise rather than the underlying pattern in the data. By identifying and handling data anomalies, analysts can optimize the performance of their machine learning models, ensuring that they provide accurate and reliable predictions.

Point anomalies are individual data points that deviate significantly from the norm.

These anomalies can be either intentional or unintentional and may result from errors, noise, or unique occurrences. Point anomalies can significantly impact data analysis, leading to incorrect or misleading insights.

Learn more in our detailed guide to data anomaly examples (coming soon)

Contextual anomalies are data points that deviate from the norm within a specific context. These anomalies are not necessarily outliers when considered in isolation, but become anomalous when viewed within their specific context.

For example, consider energy usage in a home. If there is a sudden increase in energy consumption at midday, when nobody is usually at home, it is a contextual anomaly.

This data point might not be an outlier when compared to energy usage in the morning or evening (when people are usually at home), but it is anomalous in the context of the time of day it occurs.

Errors and noise are common causes of data anomalies, resulting from issues like faulty sensors, human error during data entry, or inconsistencies in data collection methods. These anomalies can distort the dataset, making it challenging to derive accurate insights.

Learn more in our detailed guide to data anomaly detection

Unique occurrences are events or actions that cause data points to deviate from the norm. These anomalies can provide valuable insights into the dataset, as they may highlight unique trends or patterns.

For example, a sudden spike in social media engagement following a viral marketing campaign could be considered a unique occurrence, as it deviates from the typical engagement pattern, but is expected due to the event.

Emerging trends are patterns or correlations that develop over time, causing groups of data points to deviate from the norm. These anomalies can provide valuable insights into the dataset, as they may highlight new trends or opportunities.

For example, an increase in online sales over an extended period could be considered an emerging trend, as it deviates from the traditional sales pattern but may indicate a shift in consumer behavior.

Detecting and resolving data anomalies is a critical aspect of data analysis, ensuring that the findings are accurate and reliable.

Here are techniques can be used to detect and resolve data anomalies:

Visualization is a powerful tool for detecting data anomalies, as it allows analysts to quickly identify potential outliers and patterns in the data. By plotting the data using charts and graphs, analysts can visually inspect the dataset for any unusual data points or trends.

Statistical tests can be used to detect data anomalies by comparing the observed data with the expected distribution or pattern.

For example, the Grubbs test can be used to identify outliers in a dataset by comparing each data point to the mean and standard deviation of the data. Similarly, the Kolmogorov-Smirnov test can be used to determine whether a dataset follows a specific distribution, such as a normal distribution.

Machine learning algorithms can be used to detect and resolve data anomalies by learning the underlying pattern in the data and identifying any deviations from that pattern. For example, clustering algorithms can be used to group similar data points, allowing analysts to identify any outliers or unusual trends in the data.

Additionally, anomaly detection algorithms, such as the Isolation Forest and Local Outlier Factor, can be used to identify data anomalies by comparing each data point to its neighbors and determining its degree of isolation or deviation from the norm.