Data cleanup, or data rubbing, is the process of detecting and correcting or removing data or records that are incorrect from a database. It also includes correcting or removing unformatted or duplicate data or records. The data removed in this process is often referred to as “dirty data”. Data cleaning is a necessary process to protect data quality. Large businesses with extensive datasets or assets typically use automated tools and algorithms to detect such records and correct common errors (such as missing zip codes in customer records).
The most powerful big data circles have rigorous data cleanup tools and processes to ensure that data quality is protected and trust in datasets is high for all types of users.
Text-to-Speech (TTS) technology is an artificial intelligence application that enables written words to be converted into human voices.
Real-time data analytics is the process of analyzing data at the moment or within a very short period of time and making the results available immediately
Data Observability is the ability to monitor, diagnose, and manage the quality of data throughout the data lifecycle. It is also the discipline to automatically find out the health of your data and solve problems as soon as possible.
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