Adattisztító

Adattisztító is a Hungarian term that translates to "data cleansing" or "data cleaning" in English. It refers to the process of identifying and correcting or removing inaccurate, incomplete, incorrectly formatted, duplicate, or irrelevant records from a dataset. The goal of data cleansing is to improve the quality of data, ensuring its accuracy, consistency, and reliability for analysis, reporting, and decision-making.

The process typically involves several steps. First, data profiling is performed to understand the structure and content of the data, identifying potential issues. Next, data is cleaned by addressing various problems such as missing values (imputation or deletion), inconsistent formats (standardization), duplicate records (deduplication), and erroneous data (correction or removal). Data validation is then conducted to ensure that the cleaned data meets predefined quality standards.

Adattisztító is a crucial step in many data-driven processes, including data warehousing, data migration, and machine learning model development. Poor data quality can lead to flawed analysis, incorrect conclusions, and ineffective business strategies. Therefore, investing time and resources into thorough data cleansing is essential for organizations that rely on data for their operations. The specific techniques and tools used for adattisztító can vary depending on the type of data, the complexity of the dataset, and the available resources.