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.
Cloud strategy is a roadmap where a business decides which cloud services and models to use in their digital transformation journey.
Data Modelling means data analysis in its simplest form. It is one of the best ways to understand, store and manage data. It is made by data scientists and modelers.
Data architecture is a set of rules, policies, standards, and models that govern and determine the type of data collected, and show how this data is used, stored, managed, and integrated within an enterprise and database systems.
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