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This requires traditional capabilities like encryption, anonymization and tokenization, but also creating capabilities to automatically classify data (sensitivity, taxonomy alignment) by using machine learning. We approach this problem with data management in mind, preparing data for governance, risk and compliance from the outset.
Summary: Dataingestion is the process of collecting, importing, and processing data from diverse sources into a centralised system for analysis. This crucial step enhances data quality, enables real-time insights, and supports informed decision-making. This is where dataingestion comes in.
Both approaches were typically monolithic and centralized architectures organized around mechanical functions of dataingestion, processing, cleansing, aggregation, and serving. As previously mentioned, a data fabric is one such architecture.
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