Data Inconsistencies
Data inconsistencies refer to discrepancies or variations in data that occur across different sources, records, or systems. These inconsistencies can manifest in many forms, including duplicate entries, conflicting information, and errors in formatting or data types. Such inconsistencies are problematic because they hinder the ability to perform accurate data analysis, leading to biased results, incorrect conclusions, or inaccurate decision-making. They may arise from human error, system malfunctions, or discrepancies between data sources. Identifying and resolving data inconsistencies is critical for ensuring the integrity and reliability of the dataset being analyzed.
https://en.wikipedia.org/wiki/Data_integrity
One of the most common types of data inconsistencies is duplicate records, where the same information is entered more than once into a dataset. Duplicate records can occur during data entry, during system integration, or when importing data from external sources. The presence of duplicates in a dataset can distort calculations, such as averages or totals, and impact the results of machine learning algorithms. Handling duplicate data involves identifying and removing or consolidating redundant entries, and sometimes determining the most accurate representation of the data.
https://www.dataversity.net/data-cleansing-how-to-remove-duplicate-entries/
Another form of data inconsistency is conflicting information, where data records contain contradictory or incompatible values. For instance, two records in a customer database might show different addresses or contact numbers for the same individual. Conflicting data can arise when different departments or systems update data independently or when there are errors in data synchronization. Resolving these conflicts requires data validation rules, cross-referencing, and data reconciliation processes. It's important to address conflicting data, as it can lead to misinterpretation of information and negatively affect analytics, reporting, or automated decision-making systems.