adattisztítással

Adattisztítás, often translated as data cleansing or data scrubbing, is the process of detecting and correcting (or removing) corrupt or inaccurate records from a record set, database or database. This process is performed to identify incomplete, incorrect, inaccurate or irrelevant parts of the data and then replacing, modifying, or deleting the dirty or superfluous data.

The goal of data cleansing is to improve the quality of data for better decision-making, analysis, and operational efficiency. Inaccurate data can lead to flawed conclusions, inefficient processes, and wasted resources. Data cleansing can be applied to various types of data, including customer databases, financial records, scientific measurements, and more.

Common data quality issues that data cleansing addresses include missing values, duplicate records, inconsistent formats, invalid entries, and outdated information. Techniques used in data cleansing can range from simple manual editing to sophisticated automated tools employing statistical methods and machine learning algorithms.

The process typically involves several steps: profiling the data to understand its structure and identify potential errors, defining data quality rules, detecting and flagging errors, correcting or removing erroneous data, and validating the cleansed data to ensure it meets the defined quality standards. Regular data cleansing is an essential part of data management to maintain data integrity and reliability over time.