adatinkonzisztenciát

Adatinkonzisztencia refers to a state where inconsistencies exist within a dataset. This can manifest in various ways, such as conflicting values for the same entity, missing data that should be present, or data that violates predefined rules or constraints. For instance, a customer record might list different birth dates in separate fields, or a product catalog might have a price listed as zero for an item that is clearly not free.

The causes of data inconsistency are diverse and can arise during data collection, entry, processing, or integration from multiple sources. Manual data entry is prone to human error, while automated processes might have bugs or incorrect logic. Merging datasets from different systems is a common scenario where inconsistencies emerge due to differing data formats, naming conventions, or validation rules.

The impact of data inconsistency can be significant. It can lead to inaccurate reporting, flawed decision-making, and decreased operational efficiency. For example, marketing campaigns targeting customers based on inconsistent demographic data might be ineffective. Financial reports based on erroneous sales figures could mislead investors. Furthermore, inconsistent data can erode trust in the information systems and the organization itself.

Addressing data inconsistency typically involves data cleaning and data validation processes. Data cleaning aims to identify and correct or remove errors, duplicates, and incomplete entries. Data validation ensures that data conforms to predefined standards and rules before or after it is entered into a system. Techniques such as data profiling, deduplication, and rule-based validation are employed to maintain data integrity and ensure that the information remains reliable and consistent over time.