Remove Automation Remove Data Discovery Remove Data Ingestion
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Re-evaluating data management in the generative AI age

IBM Journey to AI blog

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.

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What is Data Ingestion? Understanding the Basics

Pickl AI

Summary: Data ingestion 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 data ingestion comes in.

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Data architecture strategy for data quality

IBM Journey to AI blog

Next generation of big data platforms and long running batch jobs operated by a central team of data engineers have often led to data lake swamps. Both approaches were typically monolithic and centralized architectures organized around mechanical functions of data ingestion, processing, cleansing, aggregation, and serving.