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.
Data Preparation is the process of cleaning, organizing, and making raw data suitable for analysis.
Comparative analysis means the comparison of two or more processes, document, dataset, or other objects. Pattern analysis, filtering, and decision tree analytics are types of comparative analysis.
Predictive maintenance is a method used to predict and prevent possible malfunctions in equipment. Using technologies such as sensors, artificial intelligence and machine learning, it constantly monitors the state of the machine and can pre-determine any problems.
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